The use of the total quality recovery model in determining optimal training loads and recovery periods

Abstract

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Conference Proceedings
Conference of Science, Medicine & Coaching in Cricket
Sheraton Mirage Gold Coast, Queensland, Australia
1-3 June 2010
Cricket Australia (Brisbane, Melbourne)
Edited by: Marc Portus, Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence
Peer reviewed conference proceedings.
Conference Organising Committee (Cricket Australia)
Marc Portus, Sonya Thompson, Sarah Sharpe, Dianne O’Neill, Matt Cenin
Scientific Review Sub-Committee
Marc Portus (Cricket Australia), Damian Farrow (Victoria University & Australian Institute of Sport), Bruce
Elliott (The University of Western Australia), David Pyne (Australian Institute of Sport), Kevin Sims (Cricket
Australia), John Orchard (The University of Sydney), Aaron Kellett (Cricket Australia), Michael Lloyd (Cricket
Australia), Michelle Cort (Cricket Australia)
Coaching Program Sub-Committee
Sonya Thompson, Damian Farrow, Greg Chappell, Matthew Betsey, Troy Cooley
Formatting & Typesetting
Carolyn Arthur (Cricket Australia)
Layout & Design
Matt Cenin (Cricket Australia) & MMR Studio
© Copyright Cricket Australia 2010, the authors and their institutions.
Title 1 of 1 - Conference of Science, Medicine & Coaching in Cricket 2010
Subtitle: Conference Proceedings
ISBN: [978-0-9751669-1-8]
Format: Paperback
Publication Date: 06/2010
Recommended Retail Price: $0.00
Number Of Pages: 199
Height By Width: 300 x 210
Illustrations Included: Black and White
Contributor: Marc Portus
Contributor Role: Editor
Subject: Sports and Games, Medicine, Science

i
Table of Contents
Conference Program ...................................................................................................................... 1
Conference Day 1 ............................................................................................................................ 5
An Overview of Sport Science Literature in Cricket: Where are we at? 
Bruce Elliott ................................................................................................................................... 7
Anti Doping and Other Medical Issues in Cricket 
Peter Harcourt ............................................................................................................................. 12
Holistic Skill Development: Balancing Technical and Tactical Needs 
Damian Farrow ............................................................................................................................ 14
Biomechanics of Overhand Throwing: Implications for Injury and Performance 
Glenn S. Fleisig ........................................................................................................................... 17
Throwing Mechanics, Load Monitoring and Injury: Perspectives from Physiotherapy and Baseball
as they Relate to Cricket 
Rod Whiteley ............................................................................................................................... 21
Individualisation of Cricket Players Hydration Strategies - A Necessity for High Performance 
Michelle Cort ............................................................................................................................... 25
Supplementation 2010 and Beyond Programs, Structures and Ways to Help Athletes Stay Safe 
Greg Shaw .................................................................................................................................. 29
A Novel Training Tool for Batters to ‘Watch the Ball’ 
David Mann, Bruce Abernethy, Damian Farrow .......................................................................... 32
A Constraint-Led Approach to Coaching 
Ian Renshaw and Darren Holder ................................................................................................. 35
Conference Day 2 .......................................................................................................................... 39
Monitoring and Managing Training Load and Fatigue in Elite Team Sport Athletes 
Stuart Cormack ........................................................................................................................... 41
Cricketers’ Hotspots & Coldspots: Talent Tracking the Key Development Geographies of
Australia’s Elite Cricketers 
Geoffrey Woolcock, Dwight Zakus, Murray Bird, Emily Hatfield ................................................. 44
Past, Present and Future of Injury Surveillance in Australian and World Cricket 
John Orchard, Trefor James, Alex Kountouris, Marc Portus ....................................................... 46
The Relationship between Quadratus Lumborum Asymmetry and Lumbar Spine Injury in
Junior Cricket Fast Bowlers 
Alex Kountouris, Jill Cook, Marc Portus, Howard Galloway, John Orchard ................................ 48
Psychological Aspects of Workload Management in Elite Sport 
Scott Cresswell ........................................................................................................................... 51
Planning & Monitoring Workloads: Identifying Performance Limiting Factors and Developing
Solutions 
Stuart Karppinen ......................................................................................................................... 56

ii
Conference Day 2 - Free Paper Abstracts .................................................................................. 58
The Effect of Footwear on the Lower Limb Biomechanics during the Fast Bowling Delivery Stride:
A Single Subject Case Study 
Chris Bishop, Dominic Thewlis, Wayne Spratford, Simon Bartold, Marc Portus, Nick Brown .... 59
The Biomechanics of the Initial Movement in Cricket Batting 
Gerard Randika Dias and Rene Ferdinands ............................................................................... 63
Kinematic Correlates of Lumbar Spine Loading in Fast Bowling 
René E.D. Ferdinands, Max Stuelcken, Andy Greene, Peter Sinclair, Richard Smith ................ 67
Proprioception and Throwing Accuracy after Exercise 
Jonathan Freeston, Roger Adams, Kieron Rooney .................................................................... 71
Validity of GPS for Measuring Distance Travelled in Cricket 
Adrian Gray, David Jenkins, Mark Andrews, Dennis Taaffe and Megan Glover ......................... 74
Multidisciplinary and Multivariate Approaches to Problem Solving in Exercise and Sport Science 
Ian Heazlewood .......................................................................................................................... 77
Development and Implementation of a Simulated Cricket Batting Innings for Testing and Training 
Laurence Houghton, Brian Dawson, Jonas Rubenson and Martin Tobin ................................... 82
Strength Training for Fast Bowlers: Resistance to Resistance Training 
Stuart Karppinen ......................................................................................................................... 86
Training Responses of AIS Cricket Scholars to an Elite Cricket off Season Program 
Aaron Kellett, Kevin Sims and Kieran Young............................................................................... 90
The Effect of a Formalised Goal Setting Program on Perceptions of Quality of Performance in
Training in an Elite Cricket Sample 
Michael Lloyd .............................................................................................................................. 94
Can Your Players See the Ball? What a Cricket Coach Needs to Know about the Eyes and Vision
of their Players 
David Mann ................................................................................................................................. 98
Back Injuries in Pace Bowlers – An Under-use Injury? 
Graeme Nuttridge and Peter Milburn ........................................................................................ 101
Practical, Field-Based Pre-Cooling for Medium-Fast Bowling in Hot Environmental Conditions 
Geoffrey Minett, Rob Duffield, Marc Portus and Aaron Kellett .................................................. 105
Transfer of Motor Skill Learning: Is it possible? 
Sean Müller and Simon Rosalie ................................................................................................ 109
CA/AIS/UWA GPS PhD Scholarship: Findings, Conclusions and Future Directions 
Carl Petersen, David Pyne, Marc Portus, Brian Dawson........................................................... 112
What the Experts Think: Fast Bowling Expertise Acquisition and Talent 
Elissa Phillips, Keith Davids, Ian Renshaw and Marc Portus .................................................... 114
How do our ‘Quicks’ Generate Pace? A Cross Sectional Analysis of the Cricket Australia Pace
Pathway 
Elissa Phillips, Marc Portus, Keith Davids, Nick Brown and Ian Renshaw................................ 117
Quantifying Variability within Technique and Performance in Elite Fast Bowlers: Is Technical
Variability Dysfunctional or Functional? 
Elissa Phillips, Marc Portus, Keith Davids, Nick Brown and Ian Renshaw ............................... 121
A Batting Skills Test to Assist the Development of Elite Cricketers 
Marc Portus, Stephen Timms, Wayne Spratford, Nadine Morrison, Rian Crowther ................. 125

iii
The Utility of a Bowling Skills Test to Assist Developing Fast Bowlers 
Marc Portus, Stephen Timms, Wayne Spratford, Nadine Morrison, Rian Crowther ................. 130
The Use of the Total Quality Recovery Model in Determining Optimal Training Loads and
Recovery Periods 
Gregory Reddan........................................................................................................................ 134
Links between Physiotherapy Measures and Physical Performance Measures in Australian First
Class Cricketers 
Kevin Sims and Aaron Kellett .................................................................................................... 137
Measurement of Ball Flight Characteristics in Finger-Spin Bowling 
Wayne Spratford and John Davison ......................................................................................... 140
The Influence of Batting Handedness on Rates of Shoulder Counter-Rotation in Cricket Fast
Bowlers 
Wayne Spratford, Chris McCosker, Nadine Morrison and Rian Crowther ................................ 144
Biomechanical Spin Bowling Research 
Wayne Spratford and Jacqueline Alderson ............................................................................... 147
Examining How Psychological Factors Contribute to Team Performance in an Australian National
Cricket Competition 
Rosanna Stanimirovic and Michael Lloyd ................................................................................. 150
The 3D Kinematics of the Single Leg Flat and Decline Squat 
Stephen Timms, Tony Shield, Marc Portus, Kevin Sims, Patrick Farhart ................................. 153
Conference Day 3 ........................................................................................................................ 157
The Spine in Cricket 
Peter O’Sullivan ........................................................................................................................ 159
Technology in Sports Assessment: Past, Present and Future 
Daniel James, Andrew Wixted .................................................................................................. 161
Wearable Sensors for on Field near Real Time Detection of Illegal Bowling Actions 
Andrew Wixted, Wayne Spratford, Mark Davis, Marc Portus, Daniel James ............................ 165
Smart Balls: Design and Application 
Franz Konstantin Fuss .............................................................................................................. 169
Examining How Psychological Factors Contribute to Team Performance in an Australian National
Cricket Competition 
Rosanna Stanimirovic and Michael Lloyd ................................................................................. 172
The Multi-Component Training Distress Scale for Monitoring Athlete’s Health and Wellbeing 
Luana Main................................................................................................................................ 175
Mental Toughness: Conceptualisation, Measurement, and Development 
Daniel Gucciardi ........................................................................................................................ 178
The Battle Zone: Constraint-Led Coaching 
Ian Renshaw, Greg Chappell, David Fitzgerald, John Davison and Brian McFadyen .............. 181
Subject Index ............................................................................................................................... 185

iv

1
Conference Program
TUESDAY 1 JUNE
7.00 am DELEGATE REGISTRATION OPEN Mirage Grand Terrace
8.50 am CONFERENCE OPENING Grand Ballroom 2
9.00 am
KEYNOTE ADDRESS 1: James Sutherland
The future of world cricket: implications for coaches, support staff and scientists Grand Ballroom 2
9.30 am
KEYNOTE ADDRESS 2: Prof. Bruce Elliott
An overview of the science & medicine literature in cricket: where are we at? Grand Ballroom 2
10.30 am Morning Tea
11.00 am
KEYNOTE ADDRESS 3 : Dr. Peter Harcourt
Anti-doping & other medical challenges in world
cricket Grand Ballroom 1
KEYNOTE ADDRESS 4: Prof. Damian Farrow
Holistic skill development: balancing technical and
tactical needs Grand Ballroom 2
12.00 pm Lunch
1.00 pm KEYNOTE ADDRESS 5: Dr. Glenn Fleisig
Overhand throwing biomechanics: implications for injury and performance Grand Ballroom 2
2.00 pm Seminar: The new powerhouse: do we need Twenty20™ specialists? Grand Ballroom 2
- Panel: Tim Nielsen, Darren Lehmann, Patrick Farhart, Ian Renshaw, Jamie Cox, Stuart Karppinen,
Greg Chappell, Troy Cooley
- Moderator: Ian Healy
3.00 pm Afternoon Tea
Parallel Seminars
3.30 pm Shoulder Seminar 1
Invited Speaker: Dr. Rod
Whiteley (45 mins)
Panel: Glenn Fleisig, Rod
Whiteley, Kevin Sims, Aaron
Kellett (45 mins)
Moderator: Kevin Sims
Grand Ballroom 2
Nutrition & Supplementation
Michelle Cort - Hydration
strategies for High Performance
(30 mins)
Invited Speaker: Dr. John Kellett
- Vitamin D & Muscle Function (30
mins)
Invited Speaker: Greg Shaw -
Supplements, Performance & Anti-
doping (30 mins)
Moderator: Michelle Cort
Grand Ballroom 1
Skill Acquisition in Practice
An introduction to constraints
based coaching – Dr. Ian
Renshaw & Darren Holder (15
mins) Grand Ballroom 4
Constraints based coaching –
examples from the AIS – Dr.
David Mann (15 mins) Grand
Ballroom 4
Constraints based coaching; (45
mins) Practical on tennis court
Moderators: Prof. Damian Farrow
& Matthew Betsey
5.00 pm –
6.30pm Shoulder Seminar 2
Assessment & management –
Dr. Rod Whiteley.
Grand Ballroom 4
7.00 pm Conference Dinner Sheraton Mirage Lagoon Lawn (by registration)

2
WEDNESDAY 2 JUNE
9.00 am KEYNOTE ADDRESS 6: Afterburner
Flawless execution Grand Ballroom 2
10.00 am KEYNOTE ADDRESS 7: Dr. Stuart Cormack
Managing workloads, fatigue & performance Grand Ballroom 2
11.00 am Morning Tea
11.30 am KEYNOTE ADDRESS 8: Jon Deeble
Talent identification: lessons from a life in the business Grand Ballroom 2
12.30 pm Lunch
Parallel Seminars
1.30 pm Identifying &
Developing Talent
Invited Speaker:
Scott Clayton (45
mins)
Geoff Woolcock –
Talent Hotspots (15
mins)
Panel: Brian
McFadyen, Geoff
Woolcock, Scott
Clayton, Jon Deeble
(30 mins)
Moderator: Sonya
Thompson
Grand Ballroom 2
Cricket Injuries &
Medicine
John Orchard – Injury
Surveillance (30 mins)
Alex Kountouris – QL
Research (15 mins)
Panel: Peter Harcourt,
John Orchard, Alex
Kountouris, Trefor
James, Kevin Sims (45
mins)
Moderator: Dr. Simon
Carter
Grand Ballroom 4
Workload & Wellbeing
Invited Speaker: Dr.
Scott Cresswell (45
mins)
Stuart Karppinen –
Planning & Monitoring
Workloads (15 mins)
Panel: Stuart Cormack,
Scott Cresswell, Stuart
Karppinen, Shona
Halson (30 mins)
Moderator: Aaron Kellett
Grand Ballroom 1
Illegal Bowling Actions
Panel: Troy Cooley,
Bruce Elliott, Damian
Farrow, Rene
Ferdinands, Tim
McCaskill, Wayne
Spratford, Andrew
Wixted
Moderator: Dr. Marc
Portus
Grand Ballroom 5
3.00 pm Afternoon Tea
Free Paper Parallel Sessions
Grand Ballroom 1 Grand Ballroom 2 Grand Ballroom 4 Grand Ballroom 5 Grand Ballroom 6
3.30 pm Marc Portus
Batting Skills Test
Wayne Spratford
Bowling Left & Right
Elissa Phillips
Expert Views Talent
Stephen Timms
Squat Kinematics
Adrian Gray
GPS & Cricket
3.45 pm Gerard Dias
Batting Biomechanics
Chris Bishop
Bowling Footwear
Michael Lloyd
Goal Setting
Ian Heazlewood
Multivariate Stats
Aaron Kellett
Training Responses
4.00 pm Laurence Houghton
Simulated Batting
Elissa Phillips
Generating Pace
Gregory Reddan
Recovery Model
Wayne Spratford
Ball Flight Measures
Geoff Minett
Precooling Effects
4.15 pm David Mann
Eyes & Vision
Rene Ferdinands
Bowling Spine Loads
Kevin Sims
Physio & S&C Links
Peter Milburn
Bowling Underuse
Stuart Karppinen
Strength Training
4.30 pm Sean Muller
Learning Transfer Stephen Timms
Bowling Skills Test Wayne Spratford
Spin Research Elissa Phillips
Technique Variability Jonathon Freeston
Exercise & Throwing
4.45 pm Day Concludes

3
THURSDAY 3 JUNE
Parallel Seminars
8.30 am Spine in Sport 1
Invited Speaker: Dr.
Matthew Scott-Young (45
mins)
Invited Speaker: Prof.
Peter O’Sullivan (45 mins)
Moderator: Alex Kountouris
Grand Ballroom 4
Technology in Sport
Invited Speaker: Dr. Daniel James:
Technology in Sport: Past, Present, Future
(25 mins)
Invited Speaker: Dr. Andrew Wixted:
Sensors to measure illegal bowling actions
– The ICC project (25 mins)
Invited Speaker: Prof. Franz Fuss: Smart
Balls in Sport (25 mins)
Richard McInnes: Using Technical
Information in a Practical Environment (15
mins)
Moderator: Richard McInnes
Grand Ballroom 1
Coach Longevity
Invited Speaker: Kevin
Sheedy (30 mins)
Invited Speaker: John
Wright (30 mins)
Q&A from the floor (30 mins)
Moderator: Greg Chappell
Grand Ballroom 2
10.00am Morning Tea
10.30 am KEYNOTE ADDRESS 9: Greg Chappell
Developing and managing talent in the changing cricket landscape Grand Ballroom 2
11.30 am Seminar: Bridging the gap between domestic and international cricket Grand Ballroom 2
- Invited Speaker: Ewen McKenzie: Bridging the gap: provincial & international rugby (25 mins)
- Panel: Ewen McKenzie, Tim Nielsen, Alex Kountouris, Ryan Harris, Ross Chapman, Stuart Karppinen
(35 mins)
- Moderator: Ian Healy
12.30 pm Lunch
Parallel Seminars
1.00 pm Spine in Sport 2:
Assessment &
management
Prof. Peter O’Sullivan
By registration only
Grand Ballroom 4
The Smart & Resilient Cricketer
Dr. Michael Lloyd – EQI Profiling in
Cricket: a three year project (20 mins)
Invited Speaker: Dr. Luana Main – A new
model to measure athlete stress (20 mins)
Invited Speaker: Dr. Daniel Gucciardi –
Mental Toughness in Cricket (20 mins)
Panel: Ross Chapman, Luana Main, Scott
Cresswell, Daniel Gucciardi (30 mins)
Moderator: Dr. Michael Lloyd
Grand Ballroom 2
The Battle Zone: Game
Based Training in Cricket
Greg Chappell
Ian Renshaw
Darren Holder
David Fitzgerald
John Davison
Starts in Grand Ballroom 1 for
intro presentation (15 mins)
and then moves to Tennis
Court (60 mins)
2.30 pm Seminar: Workload restrictions for young fast bowlers: are they doing more harm than good?
- Panel: John Orchard, Patrick Farhart, Aaron Kellett, Alex Kountouris, Bruce Elliott, Damian Farrow,
Peter O’Sullivan, Geoff Lawson, Craig McDermott, Troy Cooley
- Moderators: Marc Portus and Stuart Karppinen Grand Ballroom 2
3.15 pm - 3.30 pm CONFERENCE CLOSING Grand Ballroom 2

4

5
Day 1: Tuesday 1 June

6
TUESDAY 1 JUNE
7.00 am DELEGATE REGISTRATION OPEN Mirage Grand Terrace
8.50 am CONFERENCE OPENING Grand Ballroom 2
9.00 am
KEYNOTE ADDRESS 1: James Sutherland
The future of world cricket: implications for coaches, support staff and scientists Grand Ballroom 2
9.30 am
KEYNOTE ADDRESS 2: Prof. Bruce Elliott
An overview of the science & medicine literature in cricket: where are we at? Grand Ballroom 2
10.30 am Morning Tea
11.00 am
KEYNOTE ADDRESS 3 : Dr. Peter Harcourt
Anti-doping & other medical challenges in world
cricket Grand Ballroom 1
KEYNOTE ADDRESS 4: Prof. Damian Farrow
Holistic skill development: balancing technical and
tactical needs Grand Ballroom 2
12.00 pm Lunch
1.00 pm KEYNOTE ADDRESS 5: Dr. Glenn Fleisig
Overhand throwing biomechanics: implications for injury and performance Grand Ballroom 2
2.00 pm Seminar: The new powerhouse: do we need Twenty20™ specialists? Grand Ballroom 2
- Panel: Tim Nielsen, Darren Lehmann, Patrick Farhart, Ian Renshaw, Jamie Cox, Stuart Karppinen,
Greg Chappell, Troy Cooley
- Moderator: Ian Healy
3.00 pm Afternoon Tea
Parallel Seminars
3.30 pm Shoulder Seminar 1
Invited Speaker: Dr. Rod
Whiteley (45 mins)
Panel: Glenn Fleisig, Rod
Whiteley, Kevin Sims, Aaron
Kellett (45 mins)
Moderator: Dr. Kevin Sims
Grand Ballroom 2
Nutrition & Supplementation
Michelle Cort - Hydration
strategies for High Performance
(30 mins)
Invited Speaker: Dr. John Kellett
- Vitamin D & Muscle Function (30
mins)
Invited Speaker: Greg Shaw -
Supplements, Performance & Anti-
doping (30 mins)
Moderator: Michelle Cort
Grand Ballroom 1
Skill Acquisition in Practice
An introduction to constraints
based coaching – Dr. Ian
Renshaw & Darren Holder (15
mins) Grand Ballroom 4
Constraints based coaching –
examples from the AIS – Dr.
David Mann (15 mins) Grand
Ballroom 4
Constraints based coaching; (45
mins) Practical on tennis court
Moderators: Prof. Damian Farrow
& Matthew Betsey
5.00 pm –
6.30pm Shoulder Seminar 2
Assessment & management –
Dr. Rod Whiteley.
Grand Ballroom 4
7.00 pm Conference Dinner Sheraton Mirage Lagoon Lawn (by registration)

7
An Overview of Sport Science Literature in Cricket: Where are we at?
Bruce Elliott
School of Sport Science, Exercise and Health, The University of Western Australia, Perth
Correspondence: bruce.elliott@uwa.edu.au
Elite cricketers are unique in the world of sport, in that they are required to play so many matches
of varying formats over the entire year. Research that provides evidence-based direction for their
preparation is therefore imperative. This is particularly the case in Australia, where our relatively
small population requires us to develop our players to their optimal, while at the same time keeping
them injury free. To this end Cricket Australia (CA), through its Sport Science Sport Medicine Unit,
has been very forward thinking in the way that science has been used to enhance performance.
Typically the majority of CA supported research should be applied, that is it should answer
questions of interest and concern to coaches and players. However, some research may be more
theoretical, providing background to questions that may not as yet have been asked.
This presentation is not meant to summarise all aspects of science as it applies to cricket, that
would require a very extensive document such as Bartlett (2003), however it will attempt to ‘set the
scene’ for the presentation of science at this conference by briefly reviewing current literature in the
various discipline areas. Even in this endeavour it is not meant to be all inclusive and only
selected papers will be reviewed.
The presentation will be started with a discussion of how a combined van Mechelen (1992) and
Finch, (2006) research injury prevention model may be applied generally to cricket research (see 5
steps below) before discussing current research directions for each of the various science
disciplines applied to cricket.
1: Establish need for research. For injury-based research you need valid epidemiological data
(extent, nature and severity).
2: Identify aetiology of the problem - bigger than just technique.
3: Develop technique enhancement or preventive measures.
4: Educate the relevant population (player, coach, parents, media) - other models expand on
this step to include such factors as efficacy of approach in the field setting.
5: Evaluate the effectiveness of the coaching or preventative measures.
Each of the sport sciences has played a significant role in developing cricket in this country. Brief
summaries of recent research are included.
Sport and Health Psychology
(a) Psychological skills training for junior cricketers.
Results from unpublished research by Tobin and Gordon at UWA showed significant gains
in both ‘knowledge of’ and ‘use of’ mental skills that did not diminish with time. Social
validation procedures indicated that psychological training at a young age was regarded as
both acceptable and appropriate by players, coaches and parents.
(b) The development of a ‘mental toughness’ inventory for high performance players.
Mental toughness is a collection of experientially developed and inherent sport-general and
sport-specific values, attitudes and emotions that influence the way in which we approach,

8
respond to and appraise construed pressures, challenges and adversities to consistently
achieve goals. Research by Gucciardi and Gordon (2009) identified a 5 factor 15 item
model, the factors being: affective intelligence, attentional control, resilience, self-belief and
desire to achieve
Exercise Physiology
(a) Heat acclimatisation/acclimation in preparing high performance players.
Four sessions of high-intensity cycling (30 - 45 mins) on consecutive days in a
environmental chamber (30°C & 60%RH) were only moderately successful in eliciting heat
acclimation, as were 4 days of acclimatisation for a group, who spent the same period in
Chennai. Longer and more intensive protocols were recommended (Petersen 2010).
(b) Fast bowler movement characteristics in an ODI (1 bowler over 12 matches).
• Covered 16 km per game, with 12% of the time striding or sprinting.
• Performed 66 sprints / game over 18 m and 1 high intensity run, of ~3 s every 68 s.
• Recorded a maximum sprinting speed of 8.3 m/s (Petersen et al., 2009a).
(c) Ground movements and the Twenty20 cricketer (18 players over 30 innings).
Data from Petersen et al. (2009b) showed players:
• Covered 6.4 - 8.5 km per game, while sprinting 0.1 - 0.7 km during 80 mins of fielding.
• Fast bowlers covered 8.5 km and sprinted 42 times typically over 17 m.
• While batting (30 mins) players covered 2.5 km and sprinted 12 times over 14 m.
(d) Preparing cricketers for various formats (Pertersen et al., 2010).
• Overall, ODI & Twenty20 required 50 -100% more sprinting/hour than multi-day
matches.
• However, the longer duration of multi-day matches resulted in 16 - 130% more
sprinting per day.
• Shorter formats were more intensive per unit time but multi-day cricket has a greater
overall physical load.
Motor Learning
(a) Skill decomposition (Renshaw et al., 2007 – batting against bowler or ball machine).
• Significant adaptation of coordination and timing was observed under different
practice task constraints.
• Different ratio of backswing-downswing, when batting against a bowling machine
(47% - 53%) compared with a bowler (54% - 46%).
• Mean length of front foot stride was shorter against the bowling machine (0.55 m),
compared with (0.59 m) a bowler.
• Correlation between initiation of backswing and front foot movement was higher
against a bowler (r = 0.88), compared with a ball machine (r = 0.65).
(b) Anticipation and batting performance (Weissensteiner et al., 2008 - skilled and lesser
skilled U15, U20 and adult batsmen completed a temporal occlusion task).
• Skilled adult and U20 players showed an ability to use pre-release kinematic
information to anticipate ball type that was not evident among other groups.
• Accumulated hours of experience explained only a modest percentage of the
variance in anticipatory skill.
(c) Learning new skills: Talent development programs should avoid the notion of common
optimal performance models (Phillips et al., 2010).
• Emphasise the individual nature of pathways to expertise by identifying a range of
interacting constraints that impinge on performance potential of the individual.

9
(d) Modifying techniques (Elliott & Khangure 2002; Ranson et al., 2009)
• Early work showed that providing data and a seminar to players, coaches and parents
was NOT successful in changing key bowling characteristics over ~2.5 yrs.
• An individual approach, with small group coaching was successful in modifying
bowling techniques, shown to be related to back injury.
• 2-years coaching intervention with elite 18 year old bowlers, showed specific
changes, such as shoulder counter rotation were possible, even with high-
performance players.
Sports Medicine
(a) Workload and injury (Orchard et al., 2009; Saw et al., 2009).
• >50 over’s/ match had an injury incidence in the next 21 days of 3.4/1000 over’s
bowled.
• >30 overs in the 2nd innings increased the risk of injury.
• Injured players threw ~40 more times/week (12.5 throws/day) than uninjured players.
(b) The role of muscle morphology and loading on the bowlers’ back (Hides et al., 2007; Visser
et al., 2007). Asymmetry of quadratus lumborum (QL) muscle, reported by Craig Engstrom
in the 1990’s was initially thought to be related to lumbar loading. Recent research shows:
• QL and erector spinae muscles were larger on the ipsilateral bowling arm side with
this asymmetry linked to impaired motor control, for players with lower back pain.
• Mathematical modelling has cast some doubts on this assumption, with some
suggestion that this asymmetry may reduce the stresses in the pars.
Biomechanics
(a) Performance optimisation; spin bowling (Chin et al., 2009).
While the velocity (≈ 22 m/s) and spin rate (≈ 25 rev/s) are similar for off-break and ‘doosra’
deliveries, the angle of rotation is very different. This is caused by a different kinematic
profile used to create these performance variables.
(b) Performance optimisation; fast bowling (Middleton et al., in progress). A forward kinematics
approach will permit key questions, such as ‘does elbow extension enhance delivery speed’
to be addressed through manipulation of segment kinematics. This research supported by
Cricket Australia is a collaborative effort between The University of Western Australia and
Griffith University.
(c) Lower back injury reduction in male and female fast bowlers (Stuelcken et al., 2010 – data
collected on 26 high-performance female fast bowlers; Portus et al., 2007).
• 14 females had a history of lower back pain (LBP), and bowlers with more counter-
rotation of the shoulder alignment were no more likely to have a history of pain.
• Female bowlers with a history of LBP positioned the thorax in more lateral flexion
relative to the pelvis.
• Female bowlers with LBP moved the thorax through a significant greater range of
lateral flexion relative to the pelvis.
• Age, growth and physical maturation are important factors when assessing back
pathomechanics in fast bowlers.
• Adolescent fast bowlers more susceptible to injury from poor technique (ie shoulder
counter rotation) than their senior counterparts.
(d) Legality and bowling: The current ICC approach using the UWA method will be presented,
along with testing of a proposed Loughborough model.

10
References
Bartlett, R. The science and medicine of cricket: an overview and update, Journal of Sports
Sciences, 21: 733-752, 2003.
Chin, A., Elliott, B., Alderson, J. & Foster, D. The off-break and ‘doosra’: Kinematic variations of
elite and sub-elite bowlers in creating spin in cricket bowling, Sports Biomechanics, 8: 187-198,
2009.
Elliott, B. & Khangure, M. Disc degeneration and the cricket fast bowler: an intervention study,
Medicine and Science in Sport & Exercise, 34: 1714-1718, 2002.
Finch, C. A new framework for research leading to sports injury prevention, Journal of Science and
Medicine in Sport, 9: 3-9, 2006.
Gucciardi, D & Gordon, S. Development and preliminary validation of the cricket mental toughness
inventory, Journal of Sports Science, 27: 1293-1310, 2009.
Hides, J., Freke, M., Wilson, S., McMahon, S. & Richardson, C. MRI study of the size, symmetry
and function of the trunk muscles among elite cricketers with and without back pain, British Journal
of Sports Medicine, Dec: 509-513, 2007.
Orchard, J., James, T., Portus, M., Kountouris, A. & Dennis, R. Fast bowlers in cricket demonstrate
up to 3- to 4-week delay between high workloads and increased risk of injury, The American
Journal of Sports Medicine, 37: 1186-1192, 2009.
Petersen, C., Pyne, D., Portus, M., Karppinen, S. & Dawson, B. Variability in movement patterns
during one day internationals by cricket fast bowlers, Int. Journal of Sports Psychology and
Performance, 4: 278-281, 2009a.
Petersen, C., Pyne, D., Portus, M & Dawson, B. Quantifying positional movement patterns in
Twenty20 cricket, Int. Journal of Performance Analysis of Sport, 9: 165-170, 2009b.
Petersen, C., Pyne, D., Dawson, B., Portus, M. & Kellett, A. Movement patterns in cricket vary by
both position and game format, Journal of Sports Sciences, 28: 45-52, 2010.
Petersen, C. Unpublished PhD Thesis, The University of Western Australia, 2010.
Phillips, E., Davids, K., Renshaw, I. & Portus, M. Expert performance in sport and the dynamics of
talent development, Sports Medicine, 40: 1-13, 2010.
Portus, M., Galloway, H., Elliott, B. & Lloyd, D. Pathomechanics of lower back injuries in junior and
senior fast bowlers: a prospective study, Sport Health, 25: 8, 2007.
Ranson, C. King, M., Burnett, A., Worthington, P. & Shine, K. The effect of coaching intervention
on elite fast bowling technique over a two year period, Sports Biomechanics, 8: 261-274, 2009.
Renshaw, I., Oldham, A., Davids, K. & Golds, T. Changing ecological constraints of practice alters
coordination of dynamic interceptive actions, European Journal of Sports Sciences, 7: 157-167,
2007.
Saw, R., Dennis, R., Bentley, D. & farhart, P. Throwing workload and injury risk in elite cricketers,
British Journal of Sports Medicine, Aug, 2009.

11
Stuelcken, M., Ferdinands, R. & Sinclair, P. Three-dimensional trunk kinematics and lower back
pain in elite female fast bowlers, Journal of Applied Biomechanics, 26: 52-61, 2010.
Van Mechelen, W., Hlobil, H. & Kemper, H. Incidence, severity and prevention of sports injuries. A
review of concepts, Sports Medicine, 14: 82-99, 1992.
Weissensteiner, J., Abernethy, B., Farrow, D. & Muller, S. The development of anticipation: a
cross-sectional examination of the practice experiences contributing to skill in cricket batting,
Journal of Sport and Exercise Psychology, 30: 1-23, 2008.
Visser, H., Adam, C., Crozier, S. & Pearcy, M. The role of quadratus lumborum asymmetry in the
occurrence of lesions in the lumbar vertebrae of cricket fast bowlers, Medical Engineering &
Physics, 29: 877-885, 2007.

12
Anti Doping and Other Medical Issues in Cricket
Peter Harcourt
Chair, International Cricket Council Medical Committee
Correspondence: peter_harcourt@tac.vic.gov.au
The International Cricket Council Medical Committee was formed in 2008 with a portfolio of
interests that include: anti-doping, injury surveillance, medical facilities of cricket venues, heat risk
to players and officials, protocols for the assessment of the biomechanics of bowling, age
determination in underage competition and other emerging medical issues. This presentation will
discuss these areas of interest with a focus on the anti-doping issues confronting the sport.
The ICC is quickly moving towards WADA compliance, however, there are specific problems within
the sport regarding this outcome. In particular some ICC members lack the capability to embrace
managing contemporary international anti-doping strategies. There are challenges with team
practitioner knowledge of drugs issues, therapeutic use exemptions, whereabouts compliance,
player support and education – significant problems in an environment where there is rigorous drug
testing and a high risk of inadvertent doping.
The challenge is highlighted by the significant developments in the international anti-doping
strategies over the last 30 years. Cricket needs to ‘fast track’ its ability to manage current WADA
compliant anti-doping strategies.
The sport has had a number of doping cases over recent years. These cases appear to have
involved inadvertent doping as well as deliberate cheating. The complexity of these cases has
demonstrated the difficulty detecting true cheating and the ease of making unfortunate mistakes.
The inadvertent doping risk includes the use of prescription drugs when a treating doctor is
unaware of the WADA prohibited list. It also includes ‘social’ illicit substance use and nutritional
supplements which either deliberately or inadvertently contain prohibited substances. Cricket
Australia (CA) and other elite sporting organisations address these inadvertent risks with annual
education programs.
In the case of CA it also has an Illicit Substances Rule (pertaining to ‘social’ drugs which are illegal
in most countries but not necessarily on the WADA banned list) and specific strategy to address
illicit substance abuse. There have been no positives with CA’s testing program but a number of
players have come forward voluntarily so that their circumstances could be addressed medically.
The Australian Football League (AFL) has a similar program with quite different results. This
demonstrates the cultural differences of the two sports but also provides insight as to how these
programs might evolve. In the case of the AFL there has been a significant drop in the illicit
substance detection incidence from 4% to less than 1% over 4 years, compared with illicit
substance use survey rates of 30% by a compatible cohort. Both these programs use primarily a
medical model to respond to illicit substance use.
The presence of WADA prohibited substances in nutritional supplements pose a significant risk
due to the high incidence of stimulant or steroid contamination in the manufacturing process. In
some countries such as the Netherlands, China, New Zealand and the USA, the presence of
prohibited substances in supplements can be as high as 20 to 25%. These prohibited substances
include DHEA, stanozolol, boldenone, designer steroids and a range of stimulants. Frequently
these ‘additions’ are not listed in a product’s contents labelling. Sports scientists, dietitians and
sports doctors need to understand these risks for athletes. Generally speaking due to the national
regulatory environment Australian manufactured supplements are safe with high reliability in the

13
labelling. Risks still occur with ‘Australian’ supplements which have sourced bulk product from
overseas suppliers.
Science is slowly addressing the problem of objective assessment of illegal bowling actions. The
ICC Medical Committee is overseeing the ICC’s work in standardising the protocols for assessing
the legality of a bowler’s action.
An emerging medical issue for international cricket is age cheating in underage competition. The
sport is looking for ‘medical’ solutions to this problem with age determination via imaging similar to
FIFA’s initiative in this area. The evidence supporting this approach is thin and there have been
incidents where players have been excluded from competition where clearly they were not
‘overage’.
Other active issues are heat risk, gender verification and injury surveillance.

14
Holistic Skill Development: Balancing Technical and Tactical Needs
Damian Farrow 1,2
1 School of Sport and Exercise Science, Institute of Sport, Exercise and Active Living,
Victoria University, Melbourne
2 Skill Acquisition Department, Australian Institute of Sport, Canberra
Correspondence: damian.farrow@vu.edu.au
The most appropriate method of instruction and practice to facilitate the learning of game skills has
long been a question of interest to both coaches and scientists alike. A longstanding debate has
centred around whether skills need to be first practised in contexts isolated from game conditions
(referred to as the technical skill drill approach) or whether suitably designed games can be used
to develop both technical and tactical skill prowess (games-based approach).
Advocates for a technical approach typically cite a range of advantages over a game-based
methodology. These include: greater quality control in terms of practice form and effort, ability to
maximise practice repetition, greater direct instruction and feedback opportunities; and more
chance of maximising player confidence (if required). These advantages are cited as being
particularly pertinent when developing new skills or when a player is attempting to break an
ingrained behaviour (habit).
An alternative to this approach has centred on adaptations to a physical education curriculum
referred to as “teaching games for understanding” (Bunker & Thorpe, 1982) or ‘Game Sense’
(Australian description). ‘Game Sense’ proponents argue that providing players with an
understanding of why a particular skill should be applied in a game context is critical and should
precede technical skill instruction. Furthermore, games contain the most pertinent information
sources that players need to become educated to if transfer to the competitive setting is to occur.
Hence, technical skills are learnt via a series of games that challenge the players to solve tactical
and decision-making problems through predominantly a facilitated or discovery-based learning
approach. The skills are applied in the context of the game, rather than in practice drills that may
not necessarily reproduce the dynamics of the game.
It is the aim of this presentation to place this debate in context. As highlighted previously (Chow et
al., 2007; Hopper, 2002) many of the coach arguments and research findings presented, have
simplified the issue to a matter of sequencing – do you coach the isolated skill before tactics or
tactics-before the skill? When viewed from this perspective, anecdotes and empirical evidence can
be found that support either approach. But such a position avoids the key issue, what are the
learning processes or skill acquisition concepts that support effective skill development (Rink,
2001). A focus on this question allows both coaches and scientists to then make some educated
decisions and predictions about how a player’s skill practice should be structured and how it is
likely to develop. I will highlight that these approaches must not be examined empirically, nor
applied in practice, as dichotomous methods. Rather that the skill learning process must be viewed
holistically and nurtured through a program that extends an individual player’s skills in a context
that is both appropriately challenging and transferable to the game setting.
The Foundations of a Holistic Perspective
Understanding learning and performance: These two concepts establish a framework for which all
skill development initiatives can be applied and evaluated. Unfortunately, despite being a topic
covered in beginning coaching courses it is apparent that, if understood, they are rarely considered
when planning and implementing a skill practice intervention. Learning is an observable change in
the capability of a player to perform a skill as a result of practice or experience that remains stable
irrespective of conditions. Performance is a skill execution at a particular moment in time (not

15
permanent) and can be affected by a range of factors including fatigue, instructions or a lack of
sleep (Magill, 2007). Whether a coach is interested in facilitating learning or performance has
significant implications for the design of a particular session or phase.
Players must be challenged: Players need to be challenged at a level appropriate to their current
skill level with a coach mindful of the difficulty of the task being practised. Guadagnoli and Lee
(2004) (or Guadagnoli, 2007 for a coach friendly discussion) refer to this issue as the Challenge
Point Framework and argue that for learning to occur, there is an optimal amount of information,
which differs as a function of the skill level of the individual and the difficulty of the to-be-learned
task. In order to set an appropriate skill practice session, one needs to account for the amount of
challenge (information) presented by the organisation of practice trials within a session and the
feedback content and frequency.
Practice specificity: “Transfer of practice to game conditions depends on the extent to which
practice resembles the game” (Magill, 1993). The relative simplicity of this quote belies the
challenge this presents coaches when attempting to structure an effective practice setting.
Renshaw and colleagues (Pinder et al., 2009; Renshaw et al., 2007) have addressed this issue in
relation to the practice value of batting against a ball machine as compared with a bowler and refer
to this challenge as one of task representativeness. Coaches are challenged to design practice
activities that enable players to make decisions based on attunement to information sources
reflective of the competition environment.
Active learning: A wide variety of research findings emanating from a variety of theoretical
perspectives are all pointing to the importance of empowering the player in the learning process.
There are a number of methods that can be used to achieve this aim and include: practice settings
that encourage discovery learning (Davids et al., 2008); more implicit instructional approaches
(Farrow, 2006; Masters, 2008); and instructions / feedback that focus a player’s attention externally
on the image of achievement rather than internally on the image of the act (Davids, et al. 2008).
Conclusion: With a clear understanding concerning how the above principles interact within a
practice setting, coaches are armed to design appropriate skill practice sessions to facilitate
learning that transfers to the competition setting. The issue of whether this was achieved by
technical skill drill or game becomes irrelevant.
References
Bunker, D. & Thorpe, R. (1982). A model for teaching games in secondary schools. Bulletin of
Physical Education, 18, 7-10.
Chow, J., Davids, K., Button, C., Shuttleworth, R., Renshaw, I., & Araujo, D. (2007). The role of
nonlinear pedagogy in physical education. Review of Educational Research, 77, 251-278.
Davids, K., Button, C., & Bennett, S. J. (2008). Dynamics of skill acquisition: A constraints-led
approach. Champaign: Human Kinetics.
Farrow, D. (2006). Implicit learning: A challenge to traditional coaching approaches. Overview:
Cricket Coaches Australia, 2-3.
Guadagnoli, M.A. (2007). Practice to learn. Play to win. Ecademy Press.
Guadagnoli, M.A., & Lee, T.D. (2004). Challenge point: A framework for conceptualizing the effects
of various practice conditions in motor learning. Journal of Motor Behaviour, 36, 2, 212–224.
Hopper, T. (2002). Teaching games for understanding: The importance of student emphasis over
content emphasis. Journal of Physical Education Recreation and Dance, 73, 44-48.

16
Magill, R. (2007). Motor learning and control: Concepts and applications. 8th Ed, New York:
McGraw Hill.
Masters, R. (2008). Skill learning the implicit way – Say no more! In Farrow, D., Baker, J., &
MacMahon, C. (Eds.) Developing Sport Expertise: Researchers and coaches put theory into
practice. Routledge.
Pinder, R.A., Renshaw, I., & Davids, K. (2009). Information-movement coupling in developing
cricketers under changing ecological practice constraints. Human Movement Science, 28, 468-479.
Renshaw, I., Oldham, A. R. H., Davids, K., & Golds, T. (2007). Changing ecological constraints of
practice alters coordination of dynamic interceptive actions. European Journal of Sport Science, 7,
157–167.
Rink, J. (2001). Investigating the assumptions of pedagogy. Journal of Teaching in Physical
Education, 20, 112-128.

17
Biomechanics of Overhand Throwing:
Implications for Injury and Performance
Glenn S. Fleisig
American Sports Medicine Institute, Birmingham, Alabama, USA
Correspondence: glennf@asmi.org
While the cricket bowler is required to deliver the ball with unique mechanics (that is, neither
bending nor straightening the elbow), the other players on a cricket team share mechanics similar
to throws in other sports. Good throwing mechanics can help a cricket player minimize risk of injury
and also maximize performance.
Only one study has been published on cricket throwing mechanics (other than bowling mechanics)
in the scientific literature (Cook & Strike, 2000). But something happened in the 18th century that
may help us today understand the science of throwing a cricket ball. In the mid 1700’s, English
immigrants who settled in New York and Boston played cricket and “rounders” in their leisure.
These American pastimes merged and made several transformations over the next hundred years
- including adding a third and fourth base, removing the wickets, adding a pitching mound, and
changing the name of the game to “baseball.” One thing that did not change much was the ball. A
modern baseball has a mass of 145 grams and a circumference of 230 mm, which are very similar
to the mass (156 grams) and circumference (225 mm) of today’s cricket ball. While there is only 1
published study on cricket throwing mechanics, numerous biomechanical studies have been
published in recent years describing baseball throwing. The purpose of this paper is to present
baseball throwing biomechanics, and provide implications about injury and performance for cricket
players.
Most studies of baseball throwing biomechanics have focused on the pitcher. Earlier studies
captured pitching biomechanics with high-speed video and manual digitization (Atwater, 1979).
More recent studies have used reflective markers in indoor lighting with automatic data capture
and 3D computation (Chu et al, 2009; Fleisig et al., 1995; 1996; Fortenbaugh et al. 2009; Miyashita
et al., 2010). In 2009, a study of flat-ground baseball throws was conducted by ASMI
(unpublished). In this study, the automated captured system was brought outdoors. Data were
collected at night under artificial stadium lighting since the automated system cannot track the
reflective markers in sunlight. Eighteen healthy college baseball players participated in the study.
Each player started with the baseball in his hands and was then studied throwing the ball 37 m, 55
m, and maximum distance. For the 37 m and 55 m distances, the athlete was instructed to throw
the ball “hard and on a horizontal line.” For the maximum distance, the athlete was asked to throw
the ball as far as possible without any restrictions on trajectory angle. Results from this study
showed the same general throwing pattern previously seen for baseball pitchers, American football
quarterbacks, and other overhand throws (Chu et al, 2009; Fleisig et al., 1996). It is believed that
cricket throws also follow the same general pattern, which is described below. The values
presented in the description are the averages measured for the college baseball players tested in
the 2009 study. The pictures are from an elite British cricket player recorded in ASMI’s
biomechanics lab. The player is described as male in order to reduce the wordiness of the
description; previous research has shown that male and female throwing patterns are similar (Chu
et al, 2009).

18
The fielding phase of the throw varies greatly. The athlete must position himself
to catch the ball (off the ground or in the air), generate some momentum of his
body towards the target, and position his feet for the subsequent dynamic
phases of the throw. The athlete typically fields the ball with both hands, and
with his legs spread apart and bent at the knees and hips.
In the step-phase, an athlete aligns his legs so that his trunk
is approximately perpendicular to the target. He then steps
or skips so that his back foot (right foot for a right-handed
thrower) is closer to the target. This step can be either
behind or in front of the lead leg. A picture of each
technique is shown here.
In the stride phase, the athlete strides his front leg (left leg for a right-handed
thrower) towards the target. At the same time, the athlete separates his hands
and swings them down, apart, and up. The coordination of the leg and arm
motions is critical to enable optimal timing in the later throwing phases. At the
time of front foot contact, the stride length should be approximately 80% of body
height and the lead knee should be flexed 45°. Also at this time, the pelvis
should be slightly open to the target, but the two shoulders should be in line with
the direction of the throw. Abduction (that is, the “armpit” angle) of the throwing
shoulder should be 100°. The elbow is flexed 80°, and the shoulder has about 55° of external
rotation. (External rotation is defined as 0 when the forearm is horizontal and 90° when the
forearm is vertical.)
The arm cocking phase begins at the time of front foot contact. During this phase the pelvis and
then upper trunk rotate to face the target while the throwing arm cocks back. The non-throwing
arm is tucked in near the trunk in order to increase velocity of the upper trunk rotation. The lag
between pelvis rotation and upper trunk rotation is critical for generating energy from the trunk in
the kinetic chain. Without proper timing of pelvis and upper trunk rotation, the athlete may have
low ball speed and/or excessive loads in the shoulder and elbow (Fortenbaugh et al. 2009;
Whiteley, 2007).
The arm cocking phase ends with the throwing shoulder in maximum external
rotation (MER). MER is 175°; in other words, the forearm is almost
perpendicular to the trunk and the palm of the hand is facing up. Achieving
such external rotation is strongly related to ball velocity (Fortenbaugh et al.
2009; Whiteley, 2007). An athlete must cock his arm back far in order to
accelerate his hand forward. Measured MER is not just rotation within the
shoulder joint, but actually a combination of shoulder (glenohumeral) rotation,
scapula motion, and arching of the back (Miyashita et al., 2010).
While MER is vital for ball speed, it is also a position of potential injury. In this position the rotator
cuff muscles on the back of the shoulder (especially the infraspinatus muscle) may become
pinched in the shoulder joint. When this muscle is impinged, it may tear during the forceful
shoulder rotation (Fortenbaugh et al. 2009; Whiteley, 2007). The front of the shoulder capsule is
under tension and may tear as well (Fleisig et al., 1995). The torque (“rotational force”) at the
shoulder and elbow both peak near the time of MER, as the joints must stop the arm cocking and
initiate the forward rotation of the arm. Peak elbow varus torque is 95 Newton-meters (which is
equivalent to holding a 25 kg mass in the thrower’s hand at this instant). Repetition of this varus

19
torque can lead to tension and tearing in the elbow’s ulnar collateral ligament and bone spurs in
the back of the elbow (Fleisig et al., 1995).
From this cocked position, the athlete initiates arm acceleration. Elbow extension velocity reaches
2500 °/s and shoulder internal rotation velocity reaches an incredible 7500 °/s. This is the fastest
joint rotation documented in any sport (Fleisig et al., 1996). The biceps muscle of the upper arm
contracts to decelerate the elbow extension. This contraction in the arm cocking and arm
acceleration phases may lead to a tear of the cartilage (the labrum) at the shoulder joint (Fleisig et
al., 1995).
The arm acceleration phase ends with ball release. At the time of
ball release, the front knee is flexed 35°. The front knee is
extending (straightening) through ball release, which allows the
athlete to stop the forward motion of his pelvis and transfer
energy up his body to the ball. The trunk is tilted 25° forward and
25° to the side. The throwing shoulder is abducted 90° (that is,
the throwing elbow is on the imaginary line passing through both
shoulders). If the shoulder is abducted significantly more or less
than 90° near the instant of ball release, there can be misalignment in the shoulder leading to
damage to the shoulder capsule and surrounding tissue. Different athletes in various throwing
situations may alter the sideways tilt of their trunk; however, the shoulder abduction at ball release
should always be approximately 90° (Atwater, 1979).
The rapid rotations of the upper trunk and throwing arm create a large force at both the shoulder
and elbow. At the time of ball release more than 1000 Newtons are produced at both the shoulder
and elbow to resist distraction. In other words, the body rotation creates forces greater than body
weight that are trying to pull the arm out at the shoulder and elbow joint. Tension on the ligaments
and muscles – especially the rotator cuff – may lead to tensile tears from repetitive throwing
(Fleisig et al., 1995).
After ball release the throwing arm continues to internally rotate, leaving the
forearm in a pronated position. Pronation happens in all overhand throws –
straight throws, curveballs, etc.
The arm horizontally adducts in front of the chest. The trunk continues to tilt
forward and the back leg steps forward. An athlete with an abreviated
deceleration and follow-through may not be using his body to dissipate the
energy produced in throwing which may lead to excessive force in the shoulder
and elbow (Fortenbaugh et al. 2009; Whiteley, 2007).
In conclusion, proper throwing mechanics can help a cricket player minimize the risk of arm injury
and also maximize his throwing velocity and accuracyError! Bookmark not defined.. While
coaches work on technique with bowlers, throwing technique with cricketers should also be
emphasised. Biomechanical research shows a kinetic chain of events in proper throwing. This
chain includes fielding the ball, step, stride, arm cocking, arm acceleration, arm deceleration, and
follow-through. While it might seem that a cricket player sustains an arm injury from a specific hard
throw or from one match, a throwing injury is really accumulated micro-trauma from repetitive
throwing (that is, “overuse”). Improved throwing mechanics as well as avoiding throwing when

20
fatigued can reduce the chance of injury. An understanding of throwing mechanics can also help
technique coaches design training drills, and strength coaches design functional exercises.
Acknowledgement: Dave Fortenbaugh, Becky Bolt, Ryosuke Ito, and James Andrews for their
contribution to the biomechanical research used for this paper.
References
Atwater, AE. Biomechanics of overarm throwing movements and of throwing injuries. Ex Sport Sci
Rev 7: 43-85, 1979.
Chu Y, Fleisig GS, Simpson KJ, Andrews JR. Biomechanical Comparison between Elite Female
and Male Baseball Pitchers. J Appl Biomech 25:22-31, 2009.
Cook, D. & Strike, S. Throwing in cricket. Journal of Sports Sciences 18(12): 965-973, 2000.
Fleisig GS, Escamilla RF, Andrews JR, Matsuo T, Satterwhite Y, Barrentine SW. Kinematic and
kinetic comparison between baseball pitching and football passing. J Appl Biomech 12(2):207-
224, 1996.
Fleisig GS, Andrews JR, Dillman CJ, Escamilla RF. Kinetics of baseball pitching with implications
about injury mechanisms. Am J Sports Med 23(2):233-239, 1995.
Fortenbaugh D, Fleisig GS, Andrews JR. Baseball pitching biomechanics in relation to injury risk
and performance. Sports Health 1:314-320, 2009.
Miyashita K, Kobayashi H, Koshida S, Urabe Y. Glenohumeral, scapular, and thoracic angles at
maximum shoulder external rotation in throwing. Am J Sports Med 38(2):362-368, 2010.
Whiteley R. Baseball throwing mechanics as they relate to pathology and performance – a review.
J Sports Sci Med 6:1-20, 2007

21
Throwing Mechanics, Load Monitoring and Injury: Perspectives from Physiotherapy
and Baseball as they Relate to Cricket
Rod Whiteley
School of Physiotherapy, University of Sydney, Sydney
Experienced coaches and players have often empirically observed what “looks” like good throwing
technique, or mechanics, and this knowledge has grown in part from instruction, but is often
learned too late as they observe athletes incur injury related to throwing in a manner that “looks
bad”. Much work has been done in accurately describing and modelling the kinematics and
kinetics, particularly as it relates to the baseball pitch (Fleisig, 1994; Fleisig et al., 1996, 1999),
while some work has been conducted comparing other throwing techniques – from the field and
throwing American footballs. The lessons learned here suggest that there are similar features of
each of these throws that are common and, more importantly, parameters are described which will
minimise the stresses to the throwing shoulder and elbow. In a practical sense, monitoring body
position at the instant of stride foot contact is useful. Particular parameters of interest are: the
position of the stride foot in relation to the stance foot and the target, the angle of the throwing
shoulder abduction, shoulder external rotation, and horizontal abduction, trunk and pelvic
orientation, and elbow flexion. A simple method is presented for teaching and monitoring these
mechanical traits suitable for all ages of athletes which includes these parameters and
incorporates utilisation of a stretch-shortening cycle initiated at the pelvis.
Figure 1: Key kinematic variables in the throwing motion. Top left image depicts stride length and
direction. In practice, measurement of stride length from the beginning position proves more
practical, and will be up to 90% of the athlete’s height. The stride foot should land on the line drawn
from the trailing ankle to the target, and not to the right side (from this view), and the foot should
stay pointed toward the target or slightly to the left. Top right image depicts shoulder external
rotation which should aim to be approximately 53°. The bottom two images depict target elbow
flexion (90°, left image) and horizontal abduction (23°).

22
Figure 2: Depiction of shoulder abduction angle from the moment of stride foot contact until
release.
With this method understood, contrast can then be made between different throwing strategies
where varying temporal and spatial demands are placed: throwing from the infield in comparison to
throwing from the outfield are contrasted where the trade-off for fielding time (the time from picking
the ball up until it is released) and flight time (the time from release until the ball reaches the target)
results in different strategies optimising performance. A further complication is made when the
athlete is forced to make an off-balance throw and it can be seen that the mechanics remain the
same for the trunk and upper body.
Figure 3: Analysis of three different outfield returning strategies performed by one skilled thrower
(throwing distance approximately 70m). It is shown that the maximum effort throw (Outfield 1 step
hard) resulted not in higher throwing velocity (as would be evidenced by a shorter flight time) rather
a quicker time to release and an improvement in total fielding time of 0.3seconds which translates
to runner’s distance of 2.3m for an average runner.
It is suggested that these strategies could readily be incorporated into the coaching of junior
players thereby allowing enough time for expert skill acquisition required for high level performance
as well as protective structural adaptations such as throwing-related increase in humeral retro-
torsion.
Injury surveillance and prevention: A load monitoring strategy has been in place in baseball for
many years in the form of individual pitch limits per outing, and a pitching rotation enforcing days of
rest between pitching outings. While these practices have substantially diminished the injury rates
for these athletes, injury has not been entirely abolished, in fact far from it. In an effort to reduce
injury rates, a process of prospective and retrospective examination was conducted which initially
involved establishing demographics and injury incidence, and then instigating attempts at reducing
injury incidence. After encountering many dead ends, some success has been achieved in more

23
frequent monitoring of a few key variables, in particular range of motion of the posterior shoulder
structures and monitoring of the strength of the shoulder’s internal and external rotators. Much
more work still needs to be done. A clinical series of over 2,500 days’ observations in over 600
patients established that strength testing employing a hand held dynamometer was conducted.
This series suggested that the ratio of shoulder internal rotation strength to external rotation
strength was significantly associated with the presence of shoulder injury. In particular, values in
excess of the ratio of 1.5:1 (Internal Rotation: External Rotation) were associated with shoulder
injury. A subsequent pilot prospective analysis of Institute of Sport adolescent baseball athletes
monitoring these strengths suggested that monitoring these strengths and altering throwing load
and strengthening regimens when these values exceed target criteria were associated with a
reduction in injury incidence.
Burkhart et al., (2003) have suggested that aberrant thickening of the posterior capsule of the
glenohumeral joint is key in the creation of a commonly seen throwing-related shoulder injury –
superior labral injury associated with an undersurface fraying of the postero-superior rotator cuff
tendons. These workers suggest that during the maximum external rotation incurred during the
cocking phase of throwing, this thickened posterior capsule occasions an obligate postero-superior
humeral head translation which both accentuates traction on the biceps anchor at the superior
labrum and mechanically irritates the undersurface of the posterior superior rotator cuff tendons.
Accordingly, clinical interest in the passive range of motion of the posterior capsule is heightened,
and measurement of this range of motion can, theoretically, identify athletes at risk of succumbing
to this pathological cascade. Two measurement strategies are suggested: passive internal rotation
range of motion in 90° of forward flexion; and passive shoulder horizontal adduction range of
motion in 90° of forward flexion. It is suggested that regular monitoring of this range of motion can
identify athletes in whom either a reduction in throwing load or an increase in posterior capsular
range of motion is warranted.
A load monitoring strategy has been suggested in many athletic areas where excessive or
increased loads are associated with injury. The logic behind this strategy is that by prescribing
appropriate loads an athlete is able to maximise their activity while not exceeding presumed
loading limits. In practice, coaches and athletes have identified that some athletes will be unable to
cope with “normal” loads, while others seemingly escape unscathed from abnormally high loads. It
is suggested that some degree of biologic variability is at play in these situations, and rigidly
adhering to these prescribed limits will unnecessarily disadvantage athletes in either of these
categories.
An alternate strategy is suggested where measurement of key performance markers (shoulder
rotation strengths, and shoulder flexibilities) might prove a more accurate post hoc measure of an
individual’s reaction to loading and thereby allow for more individualised prescription of loading. An
algorithm can then be derived for each athlete in terms of number of throws made over a time
period and more accurately predict maximum values for activity without exceeding safe ranges.
These values will vary with time as the athlete matures, strengthens, or ages, and ongoing
monitoring will allow for more accurate estimation of appropriate loading for individual athletes.
References
Fleisig, G.S. (1994). The biomechanics of baseball pitching. Unpublished doctoral thesis. The
University of Alabama.
Fleisig, G.S., Barrentine, S.W., Escamilla, R.F. and Andrews, J.R. (1996) Biomechanics of
overhand throwing with implicationsfor injuries.Sports Medicine 21, 421-437.
Fleisig, G.S., Barrentine, S.W., Zheng, N., Escamilla, R.F. and Andrews, J.R. (1999) Kinematic and
kinetic comparison of baseball pitching among various levels of development. Journal of
Biomechanics, 32, 1371-1375.

24
Burkhart, S.S., Morgan, C.D. and Kibler, W.B. (2003a) The disabled throwing shoulder: spectrum
of pathology. Part I: Pathoanatomy and biomechanics. Arthroscopy 19, 404-42

25
Individualisation of Cricket Players Hydration Strategies - A Necessity for High
Performance
Michelle Cort
Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: mcort@coe.cricket.com.au
Long before there is a risk to a cricket player’s health, dehydration causes a reduction in exercise
performance. A fluid deficit of as little as 1.5%) of body mass can impact an athlete’s performance
with the decrement being proportional to the fluid loss (Maughan, 2004). This performance
decrease is further exacerbated when exercising in the heat. Loss of body water and associated
electrolytes can impair cardiovascular and thermoregulatory function. The need for a cricket player
to maintain hydration throughout a match is therefore vital to ensuring performance is maintained.
The majority of published studies investigating hydration in sport have focussed on demonstrating
the reduction in endurance exercise performance that occurs when an athlete is dehydrated in both
temperate and hot environments. However, of perhaps more relevance to cricket, several studies
have shown that the performance of complex tasks is also impaired at relatively low levels of fluid
deficit. A study investigating the motor skill performance of cricket bowling (Devlin, Fraser. Barras,
& Hawley, 2001) revealed no influence of dehydration on bowling speed, but bowling accuracy, (as
determined by line and distance), was significantly worse when undertaken in a dehydrated state.
Similarly, studies investigating other sports skill tests show significant deterioration in skill
performance at around 2% dehydration (Maughan & Shirreffs, 2008). There are also negative
effects of mild dehydration on cognitive function and mood (Grandjean & Grandjean, 2007).
Interestingly, an observational study conducted by the Football Association in the UK showed that
the risk of injury increases in later stages of game when players are suffering from fatigue and
dehydration. This poses the question as to whether a cricket player is also more likely to become
injured when dehydrated and fatigued?
Although a variety of published “fluid replacement guidelines” are available to sports people they
do not take into account the individual variation that occurs between individuals with regard to their
hydration requirements. The generalised guidelines are of limited use to practitioners who face the
task of providing advice to cricket players to aid in achieving optimal performance (Maughan 2007).
No single recommendation is best for all cricket players in every situation, and development of
individualised hydration strategies are essential to preserve performance.
Optimum fluid replacement strategies during exercise are dependent on the exercise task (length
and intensity), the environmental conditions (ambient temperature and humidity) and the individual
physiological characteristics (sweat rate and sweat composition) of the athlete. Table 1 illustrates
the range of sweat rates and sweat sodium concentrations for the current Australian Men and
Women’s cricket team players (measured during summer training sessions). A ‘one size fits all’
approach to hydration is clearly not possible when such variation exists between athletes.

26
Table 1: Australian cricket players sweat rate, and sweat sodium concentration ranges measured
in training.
Athlete Group Environmental
Conditions
Sweat Rate Range
(ml/hr)
Sweat Sodium
Concentration Range
(g/hr)
Australian Men’s
Cricket Team
29°C; 67% RH 850-2050 0.5-1.8
Australian Women’s
Cricket Team
22°C; 74% RH 220-909 0.2-0.9
Collection of the relevant data required to develop an individual player’s hydration strategy
protocols should be undertaken over a number of training sessions or matches. After this data is
collected, it needs to be interpreted by an experienced practitioner and the applicable
individualised hydration protocols developed.
The calculation of an athlete’s sweat rate during a training session or a match involves more than
just measuring ‘pre and post’ body weight. Without an associated assessment of the athlete’s fluid
intake, an underestimate of the sweat losses will usually occur. A fluid balance assessment,
((change in body weight + fluid intake) – (urine losses + weight of food consumed)), is more
comprehensive. The data collected in this method allows calculation of the absolute and hourly
rate of each athlete’s fluid intake, sweat loss, percentage fluid replaced and percentage body
weight change. There is extensive published data on sweat rates in athletes from a range of
sports and environmental conditions. The average sweat rate across sports is reported as 900-
1500ml/hr (Shirreffs 2006).
Only a small number of studies have investigated sweat electrolyte losses, most of these have
been conducted in soccer and American football. Sodium is the main electrolyte lost in sweat and
chloride is present in slightly smaller amounts. Potassium, calcium and magnesium are present at
vastly lower concentrations. Of these sodium is quantitatively the most important.
Average reported sodium loss from sweat in athletes is 0.4 -1.5g/hr (Shirreffs 2006). Some athletes
lose as much as 5 times the sodium as others during the same exercise session.
The extent of sodium loss in some players warrants replacement of this electrolyte during training
and competition. Accounting for differences in sweat rate, training status, degree of acclimation
and dietary intake there is still a considerable inter-individual variability in sweat electrolyte
concentration, indicating genetic variability. There is no correlation between sweat rate and sweat
electrolyte concentration. There are 2 methods of sweat sodium measurement. These include, i)
collection of sweat from a specific body region using a bag, capsule or patch, or ii), the more
difficult, ‘whole body wash down’ technique. The whole body wash down method is the most
accurate, however is not practical outside of a laboratory setting. During exercise sessions with
cricketers (and other athletes) the regional (localised) sweat patch method is most commonly used.
The sweat patch method provides higher electrolyte concentrations than the wash down method,
overestimating whole body sweat sodium losses by approx 30-40%. This must be taken into
account when interpreting results. Sweat patches therefore provide an approximate value of sweat
electrolyte losses and identify athletes who have electrolyte rich sweat. Interpretation of sweat
patch results needs to be evaluated together with fluid balance data (collected during the same
session), exercise intensity, exercise duration, environmental temperature and humidity. If it is
interpreted that a player is likely to lose significant sodium in a training session or match, additional
sodium intake (during the session) is warranted.
Recent data from tennis and American football (Maughan & Shirreffs, 2008), has suggested that
muscle cramps occurring during exercise in hot environments are more likely to occur in players
who sweat profusely, especially those with a high sweat sodium concentration. It has long been
known that ingestion of salt (NaCl) and water, but not water alone, in susceptible individuals in the
work force (industrial workers, soldiers) can reduce the frequency and intensity of muscle cramps.

27
Anecdotally this has also been observed in athletes including cricketers. For cricketers to meet
their individual hydration requirements in training and matches a strategic and systematic approach
is required.
Before exercise: There is no universal agreement on the definition of the optimal pre exercise
hydration status. However it is well established that if an athlete begins exercise dehydrated
physiological and thermal strain will ensue. Urinary specific gravity (USG) measured by
refractometry is commonly employed by cricketers prior to starting training and matches as an
indicator of hydration status. Results however must be interpreted with caution, as recent food or
fluid intake and the time of sample collection will all impact on the USG. The first sample passed in
the morning (on rising) is the most accurate USG testing sample. If an athlete is found to be
dehydrated at this point a strategic rehydration plan can be employed to ensure hydration is
achieved prior to starting exercise. If testing samples are collected at other times during the day,
more careful interpretation of results is required. ‘Falsely hydrated’ results can occur if large
volumes of fluid are consumed prior to collection of the sample (which is a common occurrence
leading into to the start of training and matches). It should be noted that very low USG values
(~1.004 or less) are likely to indicate the recent ingestion of a large volume of fluid, or that the
sample has been tampered with.
During Exercise: Cricketers should be encouraged to weigh themselves before and after sessions,
the difference indicating the mismatch between their fluid intake and fluid loss. Ideally weight loss
should generally not exceed 1-2% of body mass, however the athletes pre exercise hydration
status will need to be taken into account when interpreting this value. Consumption of cold fluid
reduces the physiological strain induced by the playing in the heat. Heart rate and sweat rate is
reduced when drinking cold fluid as compared with drinking a warmer fluid. Fluids at 4 degrees
Celsius (the temperature of fluids when stored in a refrigerator) reduce thermal sensation and
perceived exertion.
After Exercise: The need for cricketers to restore water and electrolyte balance after exercise is
critical in ensuring subsequent exercise performance is maintained. Aggressive rehydration
strategies will be required by players who have lost significant fluid in a morning session and
continue to play in the afternoon session, or if playing over multiple days, or if undertaking multiple
training sessions in the same day. Well established rehydration guidelines suggest that the athlete
needs to consume 1½ times the fluid lost in the session in order to re-hydrate successfully. If
significant fluid loss has occurred, or if the time for rehydration is limited, the rehydration fluid must
contain water plus moderately to high levels of sodium (at least 50mmol/L) (Maughan & Shirreffs,
1997). Alternatively low sodium fluids (in the required rehydration volume) can be consumed with
high sodium containing foods to successfully rehydrate. Urinary markers including colour and USG
don’t correlate well with hydration status after exercise and such measurements have no value.
In summary, the solution to the problem of fluid losses in cricket players is not simply suggesting
an increase in fluid intake. Mean data has little relevance in assessing the requirements of
individual players and detracts from the considerable variation in sweating response and drinking
behaviour. Individual monitoring and assessment of players is required to determine individual fluid
and electrolyte requirements. In order for physical, skill and cognitive performance to be optimised
this should be an essential part of a player’s performance nutrition strategy.

28
References
Maughan, R.J., Merson, S.J., Broad, N.P & Shirreffs, S.M. (2004). Fluid and electrolyte intake and
loss in elite soccer players during training. International Journal of Sport Nutrition and Exercise
Metabolism, Jun 14(3):333-46.
Devlin, L.H., Fraser, S.F., Barras, N.S. & Hawley, J.A. (2001). Moderate levels of hypohydration
impairs bowling accuracy but not bowling velocity in skilled cricket players. Journal of Science and
Medicine in Sport, Jun 4(2), 179-87.
Maughan, R.J., & Shirreffs, S.M. (2008). Development of Individual Hydration Strategies for
Athletes. International Journal of Sport Nutrition and Exercise Metabolism, 18, 457-472.
Grandjean, A.C., & Grandjean, N.R. (2007). Dehydration and Cognitive Performance. Journal of
the American College of Nutrition, Vol. 26, No. 5, 549S–554S.
Shirreffs, S.M., Sawka, M.N., & Stone, M. (2006). Water and electrolyte needs for football training
and match play. Journal of Sports Sciences, Jul; 24(7):699-707.
Maughan, R.J., & Shirreffs, S.M. (1997). Recovery from prolonged exercise: Restoration of water
and electrolyte balance. Journal of Sports Sciences, 15, 297-303.

29
Supplementation 2010 and Beyond
Programs, Structures and Ways to Help Athletes Stay Safe
Greg Shaw
Sports Nutrition Department, Australian Institute of Sport, Canberra
Correspondence: greg.shaw@ausport.gov.au
Supplement usage in sport has become part and parcel of being an elite athlete over the past 10
years. Athletes believe in the benefits, sports foods and supplements can provide to enhance
performance and adaptation to training stimuli. Administrators focus on the issue of doping risk
supplementation presents. This divergence of how supplementation is perceived by the different
parties can lead to a conflict between athletes and administrators. Athletes are often taking
exceptional risk with supplement safety due to insufficient guidance, in an attempt to find the
elusive “one percent” that they believe will be the difference between being successful and being
confined to the also rans. Sports science/sports medicine practioner’s and administrators need to
develop a safe and workable framework within which athlete’s can feel confident and safe with
their supplement usage. The Australian Institute of Sport (AIS) in 2000 developed a sports
supplement program built on scientific evidence for ergogenic supplements and sports foods as
well as the potential risk of doping (Burke and Deakin, 2010). This presentation will discuss the AIS
supplement program and review the success of the program and how sports foods and ergogenics
can be used successfully while minimising the risk of doping.
Supplementation in sport has become a major area of concern for many sporting organisations
around the world. Australian athletes regularly report using sports foods and ergogenics to help
drive training adaptations. A recent study of Australian athletes indicated athletes reported
supplementation use of >85% (Dascombe et al., 2010). This seems to have decreased slightly
since the inception and initial concept of the AIS supplement program in 1999 which was
discussed by Baylis and colleagues who reported 99% of Australian swimmers reported using
supplementation (Baylis et al., 2001). This led to the development of the AIS sport supplement
program in 2000 (Burke and Deakin, 2010) (www.ausport.gov.au/ais/nutrition/supplementation).
This program provides athletes with a way of making informed decisions, not only on what
supplements are considered beneficial but also which sports foods and supplements may present
a risk of doping.
The AIS sports supplement program is divided into 4 main classifications (Group A, B, C, D).
Supplements range from Group A supplements, which are supplements that have large bodies of
scientific evidence to support their use to aid performance, to Class C supplements which are
considered to have no scientific evidence to support their use as a performance ergogenic. Class
D supplements are supplements that are either illegal under the WADA doping code or have a high
possibility of returning a positive doping result if consumed.
There have now been a number of cases of inadvertent doping through the alleged consumption of
tainted products. These range from products that have been taken independently by athletes or
allegedly provided by sports governing bodies. It is understandable that sporing organisations are
apprehensive to provide advice, condone or even provide athletes with supplementation due to
legal ramifications. It is however irresponsible to allow athletes to use supplementation without
providing at a minimum safe advice on how to reduce risk of inadvertent doping. This is one of the
main aims of the AIS sports supplement program.
The use of nutrients like protein, carbohydrate, electrolytes and ergogenics like creatine and
caffeine all have large bodies of evidence supporting their use to enhance performance in sports.
These nutrients are available in real foods but often the composition and volumes required are not

30
practical in a competition or training setting. This is where the use of sports foods (foods with
specially formulated nutrient compositions to be used before, during or after sport) become highly
practical and useful. An example of this is the use of sports drinks like Gatorade as a means of
replacing fluid, electrolytes and carbohydrates in a convenient formulated fluid source. Sports
foods have a unique position in the athletes training diet to provide convenient, compact and
specific nutrient sources. This is why the AIS sports supplement program has a large number of
sports foods in their group A classification. Through the program athletes can be educated on
when specific products should be used and when their use is inappropriate. By providing structure,
practices can be changed to bring them in line with more appropriate use of this subgroup of sports
supplements.
The use of other ergogenics like creatine, buffering agents, and caffeine all have a place in a well
constructed training and competition nutrition plan. The problem with current usage seems to be
the mis-supplementation of these ergogenics through the use of poly-supplements (concoctions of
ergogenics in one all encompassing source). Athletes often consume these poly-supplements as a
way of consolidating a number of key ergogenics in one convenient source. The problem with this
is the often unknown composition of key ingredients in the supplement. Key ergogenic ingredients
are often clumped on the nutrition panel as a “proprietary blend” rather than as exact amounts of
active ingredients. This presents obstacles for athletes who are trying to consume specific doses
of nutrients through supplementation. Without knowing exact amounts of an active ingredient
athletes can inevitably take larger doses than required of some ergogenics or insufficient doses of
others. Providing athletes with a structured program that enables them to make suitable choices of
supplements with known ergogenic ingredient amounts, reduces the need for poly-
supplementation that appears to be coming more widely practiced. The AIS supplement program
endeavours to supply athletes with known ergogenic supplements in pure form to reduce this issue
of mis-supplementation and potentially reduce risk.
Athletes need to be educated on what is the main reason for taking ergogenic supplements and
sports foods. The AIS supplement program and its tiering system attempt’s to provide athletes
with guidance on what can enhance performance or what may be a waste of their time. There are
large numbers of nutrition supplements on the market. Without guidance on what ergogenics have
good solid evidence for performance enhancement athletes are relying on the marketing hype and
anecdotal reports. The AIS supplement program only looks at performance studies when building
evidence for an ergogenic benefit. Each ergogenic and sports food along with scientific evidence
for its use is presented to a panel of experts before it is accepted into the program and the panel of
experts decide at which level of the program a supplement is added. Educational material like fact
sheets are developed and provided to athletes at the time of supplement provision to help educate
them on correct dosage, product name, and side effects.
The AIS supplement program provides an excellent frame work that has been successful in
educating athletes on correct use of ergogenic supplementation since its inception over 10 years
ago. Some sporting organisations like Rowing Australia have taken the program onboard and
ratified the AIS supplement program and promote its message to their athletes. Rowing Australia
have taken this one step further and provided greater structure to the program and developed
guidelines for levels of athletes that should be exposed to certain ergogenics based on their
development pathway. Other sports are in the process of developing similar programs. This
framework provides administrators, coaches and parents with guidelines for which stage of athletic
development the addition of ergogenics should be undertaken. It is recommended that Cricket
Australia consider looking at what other NSO’s are undertaking to ensure athlete supplement
usage is safe. Cricket Australia could begin by adopting the AIS supplement program and develop
suitable usage structures similar to other NSO’s.

31
References
Baylis A, Cameron-Smith D, Burke L., Inadvertent Doping through supplement use by athletes:
assessment and management of the risk in Australia. Int J Sport Nutr Exerc Metab (2001) 11;365-
383
Burke L and Deakin V. Ch16: Supplements and sports foods, Clinical sports nutrition 4th ed.
(2010) McGraw-Hill, Sydney, Aus.
Dascombe BJ, Karunaratna M, Cartoon J, Fergie B, Goodman C. Nutritional supplementation
habits and perceptions J Sci Med Sport. 2010 Mar;13(2):274-80.

32
A Novel Training Tool for Batters to ‘Watch the Ball’
David Mann1,2, Bruce Abernethy3, Damian Farrow1,4
1 Skill Acquisition, Australian Institute of Sport
2 School of Optometry and Vision Science, University of New South Wales
3 Institute of Human Performance, University of Hong Kong
4 School of Sport and Exercise Science, Victoria University
Correspondence: david.mann@ausport.gov.au
The mantra to ‘watch the ball’ is one of the most fundamental and often-heard instructions in the
game, but are coaches actually able to coach it? This presentation will address a series of studies
which have examined the role of vision in cricket batting, and in particular how good vision must be
for successful batting, and the role of implicit visual skills in the development of expertise in batting.
As a result of these studies, a novel training tool will be proposed to implicitly enhance the
concentration of skilled cricket batters.
Vision is clearly important for the performance of hitting skills like those demonstrated by elite
cricket batters. This has lead to the natural assumption that excellent cricket batters may have
some form of ‘above-normal’ vision, and that any action taken to improve their visual skills would
result in direct improvements in batting performance. This assumption has been challenged by
studies of sporting expertise which typically advocate vision to be a poor predictor of sporting
success. Rather than relying on superior visual skills, these studies suggest that excellent cricket
batting is more likely to be underpinned by highly developed perceptual-cognitive, psychological,
and motor skills. This viewpoint is supported by the relatively common occurrence of anecdotal
stories of sportspeople, including cricketers, who have been highly successful performers despite
displaying patently poor levels of vision. To examine this discordance in the role of vision in cricket
batting, a series of studies were performed to examine how good vision must be for optimal batting
performance.
The vision of grade level cricket batters was blurred using contact lenses (four increasing levels:
plano, +1.00, +2.00, +3.00; see Figure 1) in each of two experimental phases. In the first phase
batters faced a bowling-machine and live bowlers to examine the effect of blur on batting
performance. It was revealed that the highest level of blur (+3.00) was required to produce a
significant decrease in batting performance when facing the bowling-machine at medium-paced
ball-velocities (105-115 kph; Mann, Ho, De Souza, Watson, & Taylor, 2007). This finding indicated
that somewhat surprisingly, vision was required to be blurred to the level of legal blindness before
there was a significant decrease in batting performance.
Figure 1. Four increasing levels of blur experienced by batters

33
A viable interpretation of the inability of blur to decrease batting performance may have, in part,
been that facing a bowling-machine was a highly predictable task; however a similar effect of blur
was found when facing live bowlers of comparable ball-velocity. Once again, the highest level of
blur (+3.00) was required before there was any measurable decrease in performance when facing
medium-paced bowlers. Only when batters faced faster-paced ball-velocities (120-130 kph) did a
lower level of blur (+2.00) affect performance (Mann, Abernethy, & Farrow, in press-a). Even when
batters were tested in a situation simulating the batting conditions experienced at the higher levels
of competition, the +1.00 level of blur was concluded to have no measurable effect on batting
performance.
The second phase of testing sought to investigate anticipation: a perceptual skill established to be
an important component of expertise in many interceptive sports such as cricket batting. Skilled
batters are able to predict ball-flight characteristics such as the type of swing or spin of the delivery
prior to ball-release by the bowler (Müller, Abernethy, & Farrow, 2006; Renshaw & Fairweather,
2000). We sought to examine whether skilled grade-level batters could predict the line of the
delivery prior to ball-release, and in particular whether this was an explicit skill which could be
verbalised, or that is was a more implicit one that was embedded in movement. Skilled batters
observed balls being bowled towards them and attempted to predict the line of those deliveries
when decisions were made based only on the visual information available prior to ball-release.
With vision occluded at the moment of ball-release, batters predicted the direction of the ball either
(i) verbally, (ii) by moving their foot towards the ball, (iii) by playing a ‘shadowed’ shot, or (iv) by
attempting to hit the ball. It was shown that batters were unable to verbally predict the anticipated
direction of the ball; performance in this task was no better than levels achievable by chance
guessing. When producing a predictive movement, skilled batters were, in some cases, able to
predict the line of the delivery prior to ball-release. Only when attempting to hit the ball did the
batters reach their maximal performance in predicting the line of the delivery prior to ball-release
(Mann, Abernethy, & Farrow, 2010). These findings demonstrate that, although when trying to hit
the ball, skilled batters are able to predict ball-direction based on the movements of the bowler,
they do not appear to have the explicit knowledge of how they do so. This provides some
evidence to suggest that the ability to predict line is an implicit skill which has developed over many
years of batting practice. It is clear that not all elements of batting expertise can be verbalised; in
this case skilled batters were able to perform a skill, but they did not appear to have the declarative
knowledge to replicate this when explaining the outcome. The implication for coaching is that first,
skilled batters cannot explicitly verbalise or explain some of the skills that they demonstrate on a
daily basis. Second, these findings highlight that practice design must simulate real-life conditions
as closely as possible, otherwise these more implicit skills may not develop; in particular, these
findings highlight that a bowling machine will be detrimental in seeking to develop these implicit
anticipatory skills.
The vision of skilled batters was manipulated by the same four levels of blur used in the first phase
of testing (plano, +1.00, +2.00, +3.00) to examine the level of vision required for the successful
anticipation of ball-flight characteristics based on pre ball-flight information. Skilled batters
predicted the line of deliveries based on the vision of live bowlers occluded at the moment of ball-
release in each of two different response conditions: (i) a coupled condition where batters
attempted to hit the ball, and (ii) an uncoupled condition where batters verbally predicted the
direction of the ball. Coupled anticipation demonstrated velocity-dependent resilience to blur;
+3.00 and +2.00 levels of blur were required for respective decreases in the anticipation of
medium- and fast-paced ball-velocities (Mann, Abernethy, & Farrow, in press-b), replicating the
resilience to blur found in the first phase of testing. Remarkably, the results for the uncoupled
anticipation suggest that blur may actually enhance anticipation according to the movement
velocity of the bowler. It has been proposed that visual blur may ‘filter out’ information of high-
detail which has the potential to distract the observer from the visual information (of lower detail)
that is most useful for the detection of movement. Further work is required to test this rather
speculative suggestion.

34
Collectively, these results lead to the conclusion that clear vision is not necessarily required for the
performance of a task like cricket batting, even when the demanding spatio-temporal task
simulates the conditions experienced at the higher levels of competition. Although clear vision may
be an advantage for related tasks such as detecting grip of the bowler’s hand prior to ball-release,
or in identifying the position of the seam in ball-flight, on this basis of these findings it should not be
so surprising that players have reached elite levels of performance despite possessing below-
normal levels of vision. The findings also suggest that the training of ‘supra-normal’ levels of vision
is unlikely to result in improvements in performance.
Rather than blur acting as an impediment for cricket batting, it has been proposed that visual blur
in some cases may provide a relative advantage as a potential tool to be used in the training
environment. A number of batters who took part in this series of testing expressed an anecdotal
preference for batting with a low level of visual blur, particularly when using the +1.00 lenses. On
further investigation it was found that some batters felt that they - with the introduction of blur -
were more active in visually searching for the ball out of the bowler’s hand. It has been proposed
that visual blur may prove to be a useful tool to be used in training to modify visual attention. Many
batters when out of form tend to focus internally (particularly on their kinematic body movements)
rather than focussing externally on the ball and bowler; this internal focus of attention is thought to
decrease performance in skilled athletes (Beilock, Carr, MacMahon, & Starkes, 2002). Visual blur
in the form of spectacles or contact lenses may prove to be a useful tool as an intervention, in
particular for those batters experiencing a ‘form slump’, by implicitly forcing batters to focus
externally on the ball, and allowing what are well-learned batting movements to ‘flow’ in a more
natural manner. This presentation will address in which conditions this training tool is most likely to
be useful and how coaches can go about applying it in the daily training environment.
Acknowledgments: the work performed in this series of studies was funded by a Cricket Australia
Sport Science Sport Medicine Research Grant and by AIS Discretionary Research Funding.
Contact lenses used in these studies were kindly supplied by Johnson & Johnson Vision Care.
References
Beilock, S. L., Carr, T. H., MacMahon, C., & Starkes, J. L. (2002). When paying attention becomes
counterproductive: impact of divided versus skill-focused attention on novice and experienced
performance of sensorimotor skills. Journal of Experimental Psychology: Applied, 8(1), 6-16.
Mann, D. L., Abernethy, B., & Farrow, D. (2010). Action specificity increases anticipatory
performance and the expert advantage in natural interceptive tasks. Manuscript submitted for
publication.
Mann, D. L., Abernethy, B., & Farrow, D. (in press-a). The resilience of natural interceptive actions
to refractive blur. Human Movement Science.
Mann, D. L., Abernethy, B., & Farrow, D. (in press-b). Visual information underpinning skilled
anticipation: the effect of blur on a coupled and uncoupled in-situ anticipatory response. Attention,
Perception, & Psychophysics.
Mann, D. L., Ho, N., De Souza, N., Watson, D., & Taylor, S. (2007). Is optimal vision required for
the successful execution of an interceptive task? Human Movement Science, 26, 343-356.
Müller, S., Abernethy, B., & Farrow, D. T. (2006). How do world-class cricket batsmen anticipate a
bowler's intention? Quarterly Journal of Experimental Psychology: Section A, 59(12), 2162-2186.
Renshaw, I., & Fairweather, M. M. (2000). Cricket bowling deliveries and the discrimination ability
of professional and amateur batters. Journal of Sports Sciences, 18, 951-957.

35
A Constraint-Led Approach to Coaching Cricket
Ian Renshaw1 and Darren Holder2
1 School of Human Movement Studies, Queensland University of Technology, Brisbane
2 Brisbane Grammar School, Brisbane
Correspondence: i.renshaw@qut.edu.au
Traditional approaches to skill development in cricket have been based around the ubiquitous net
session and in particular an emphasis on the acquisition of the perfect technique. More recently,
some coaches have taken ideas from sports science and attempted to use them to guide their
practice. Some of these mono-disciplinary strategies have resulted in a reduction of ‘performance’
into separate building blocks (e.g. technical, tactical, physical and mental skills), which can be
worked on in isolation before the whole (performance) is stitched back together again. Each of
these units is then broken down again. For example, batting is broken down into sub-phases to
develop ‘hitting mechanics’ via use of drop feeds, throw-downs and by use of bowling machines,
while, for bowlers the run-up and bowling action are practiced separately. A major problem of this
approach is the strong focus on technique development at the expense and in isolation from
perception and decision-making skills. Although, for many coaches this intuitively makes sense
because it simplifies learning into manageable bites, some experienced high level coaches have
been highly critical of this specific contribution of sport scientists and suggested that cricket needed
a serious debate to determine whether these new methods are in fact more efficient and better
than the methods of the past (e.g., Chappell, 2004). In 2004 we responded to these comments
and were in general agreement with the sentiments of Chappell. We pointed out that perhaps the
main problem with the approach of the scientists was the relative usefulness of the theoretical
model they were basing their work upon and that recent research was highlighting the importance
of a holistic, multi-disciplinary approach to skill development (Renshaw et al., 2004). Since then,
our research using cricket bowling and batting has shown us that the development of appropriate
technique requires learners to practice tasks where perception and action are maintained via
environments representative of the competitive performance (Renshaw & Davids, 2006; Renshaw
et al., 2007). This view suggests that performance is a function of the interaction of unique
individuals with specific task and environmental constraints. In the rest of this article we describe
the constraint-led approach and suggest that it is a suitable theoretical model that coaches and
scientists can utilise to underpin learning design.
Constraints are boundaries that shape a learner’s self-organising movement patterns, cognitions
and decision-making processes (Renshaw et al., 2010). Three categories of constraints have been
proposed. 1. Performer constraints include physical and mental factors such as height, limb length,
fitness levels, technical skills, attentional control and intrinsic motivation. All of these factors can
influence decision-making behaviours. 2. Environmental constraints include: physical
environmental constraints such as weather conditions, pitch conditions, quality practice facilities
and perhaps the structure of the backyard or locality in which a player was raised; and cultural
constraints such as family, team mates, the culture of a sport club and access to high-quality
coaching. 3. Task constraints include the goal of the task, rules of the game, equipment available
and the relative state of the game.
The ideas underpinning the constraint-led perspective have important implications for the coach.
Adopting a constraint-led approach requires coaches to understand that performers have the
potential to solve performance problems in a number of ways and therefore there is a rejection of
the concept of one optimal movement solution. This change of thinking is perhaps one of the
greatest challenges for coaches as the traditional approach to developing skill is the concept of
demonstrations and feedback. In effect we ‘instruct’ players how to bat, bowl or field. However,
evidence from motor learning is showing that the natural way to learn most movement skills is at a

36
sub-conscious level and forcing players to ‘think’ via explicit instructions leads to performance
decrements (Beek, 2000). Indeed, Glenn McGrath and Craig McDermott both report singing as
they ran into bowl in order to “stop the voices” from interfering with performance.
If instruction is not necessarily helping players to improve, what strategies can be adopted by the
coaches? As behaviours emerge as a result of self-organisation under constraints, coaches can
deliberately manipulate the surroundings of players to create the conditions that lead to changes in
organisation states. For example, the coach could create rule changes in small-sided games that
reward taking of singles or encourages bowlers to bowl in specific areas (Renshaw & Holder,
2010). However, manipulating constraints should not just be limited to changing rules. Coaches
could change the environment by ‘doctoring’ specific areas of the pitch by roughing it up or leaving
on more grass. These types of manipulations force players to adapt their strategies and can lead to
changes in perceptual, decision-making and action skills. Similarly, the development of strength
and conditioning and mental skills should not be seen as something that sits separately to skill
acquisition as an apparent technical fault may in fact be due to poor strength, concentration or
decision making. For example, a common problem for many young batters is that the top-hand is
‘weaker’ than the bottom hand which leads to difficulties in ‘playing the ball straight’. Some
coaches have recognised this problem as the key factor limiting performance and have developed
strategies to help. For example, Indian batsman Virender Sehwag’s first coach made him use just
his top hand to swing a bat in a case filled with sand repeatedly in order to strengthen the arm.
Secondly, because a consequence of a weak top hand in batting is often the inability to swing the
bat in a straight line, in order to make him pick his bat up straight, Sehwag’s coach stuck a piece of
bamboo in the ground just outside off stump. If the bat was not picked up straight he would hit the
bamboo (Renshaw et al., 2010). This practical example neatly demonstrates how a cricket coach
can use an understanding of the interaction of individual, environmental and task constraints in
order to shape behaviour.
One final point that needs to be made is that the unique interactions between the individual, task
and environment constraints means that variability is a key feature in enhancing performance. This
is related to both movement variability and variability of practice. Contrary to popular belief expert
performers are not able to ‘repeat’ their movements invariantly, but use functional adaptability in
their movement patterns to achieve high levels of accuracy and adaptability to solve problems in
constantly changing performance landscapes. Consequently, practice tasks must of course provide
high levels of variability.
In summary, adopting a constraints-based perspective to cricket provides coaching with a
framework for understanding how performer, task and environmental constraints shape each
individual’s performance. By adopting an athlete–centred approach which is harmonious with
constraint-led coaching, coaches can base learning design on the needs of individuals. Crucially,
there is no one ideal movement model that each player needs to be able to achieve and the unique
interactions between individual, environmental and task constraints means that each player will
solve distinctive performance problems in ways best suited to their own strengths and
weaknesses.

37
References
Beek, P. J. (2000). Toward a theory of implicit learning in the perceptual motor domain.
International Journal of Sport Psychology, 31, 547-554.
Chappell, G. (2004). Some thoughts to end a tremendous year of learning. Thanks to all who took
part. In N. <newsletter@chappellway.com.au> (Ed.).
Renshaw, I., Davids, K., Oldham, A. R., & Glazier, P. (2004). Why sport scientists need a
theoretical model of the performer for applied work. Sport & Exercise Scientist, 1(1), 24.
Renshaw, I., Davids, K., & Savelsbergh, G. (Eds.). (2010). Motor Learning in Practice: A
constraints-led approach. London: Routledge.
Renshaw, I., & Davids, K. (2006). A comparison of locomotor pointing strategies in cricket bowling
and long jumping. International Journal of Sports Psychology, 37(1), 1-20.
Renshaw, I., Oldham, A. R., Golds, T., & Davids, K. (2007). Changing ecological constraints of
practice alters coordination of dynamic interceptive actions. European Journal of Sport Sciences,
7(3), 157-167.
Renshaw, I., & Chappell, G. S. (2010). A Constraints-led Approach to Talent Development in
Cricket. In L. Kidman & B. Lombardo (Eds.), Athlete-Centred Coaching: Developing Decision
Makers (2nd ed., pp. 151-173). Worcester: IPC Print Resources.
Renshaw, I., & Holder, D. (2010). The Nurdle to leg and other ways of winning cricket matches. In
I. Renshaw, K. Davids & G. Savelsbergh (Eds.), Motor Learning in Practice: A constraints- led
approach. London: Routledge.

38

39
Day 2: Wednesday 2 June

40
WEDNESDAY 2 JUNE
9.00 am KEYNOTE ADDRESS 6: Afterburner
Flawless execution Grand Ballroom 2
10.00 am KEYNOTE ADDRESS 7: Dr. Stuart Cormack
Managing workloads, fatigue & performance Grand Ballroom 2
11.00 am Morning Tea
11.30 am KEYNOTE ADDRESS 8: Jon Deeble
Talent identification: lessons from a life in the business Grand Ballroom 2
12.30 pm Lunch
Parallel Seminars
1.30 pm Identifying &
Developing Talent
Invited Speaker:
Scott Clayton (45
mins)
Geoff Woolcock –
Talent Hotspots (15
mins)
Panel: Brian
McFadyen, Geoff
Woolcock, Scott
Clayton, Jon Deeble
(30 mins)
Moderator: Sonya
Thompson
Grand Ballroom 2
Cricket Injuries &
Medicine
John Orchard – Injury
Surveillance (30 mins)
Alex Kountouris – QL
Research (15 mins)
Panel: Peter Harcourt,
John Orchard, Alex
Kountouris, Trefor
James, Kevin Sims (45
mins)
Moderator: Dr. Simon
Carter
Grand Ballroom 4
Workload & Wellbeing
Invited Speaker: Dr.
Scott Cresswell (45
mins)
Stuart Karppinen –
Planning & Monitoring
Workloads (15 mins)
Panel: Stuart Cormack,
Scott Cresswell, Stuart
Karppinen, Shona
Halson (30 mins)
Moderator: Aaron Kellett
Grand Ballroom 1
Illegal Bowling Actions
Panel: Troy Cooley,
Bruce Elliott, Damian
Farrow, Rene
Ferdinands, Tim
McCaskill, Wayne
Spratford, Andrew
Wixted
Moderator: Dr. Marc
Portus
Grand Ballroom 5
3.00 pm Afternoon Tea
Free Paper Parallel Sessions
Grand Ballroom 1 Grand Ballroom 2 Grand Ballroom 4 Grand Ballroom 5 Grand Ballroom 6
3.30 pm Marc Portus
Batting Skills Test
Wayne Spratford
Bowling Left & Right
Elissa Phillips
Expert Views Talent
Stephen Timms
Squat Kinematics
Adrian Gray
GPS & Cricket
3.45 pm Gerard Dias
Batting Biomechanics
Chris Bishop
Bowling Footwear
Michael Lloyd
Goal Setting
Ian Heazlewood
Multivariate Stats
Aaron Kellett
Training Responses
4.00 pm Laurence Houghton
Simulated Batting
Elissa Phillips
Generating Pace
Gregory Reddan
Recovery Model
Wayne Spratford
Ball Flight Measures
Geoff Minett
Precooling Effects
4.15 pm David Mann
Eyes & Vision
Rene Ferdinands
Bowling Spine Loads
Kevin Sims
Physio & S&C Links
Peter Milburn
Bowling Underuse
Stuart Karppinen
Strength Training
4.30 pm Sean Muller
Learning Transfer Stephen Timms
Bowling Skills Test Wayne Spratford
Spin Research Elissa Phillips
Technique Variability Jonathon Freeston
Exercise & Throwing
4.45 pm Day Concludes

41
Monitoring and Managing Training Load and Fatigue in
Elite Team Sport Athletes
Stuart Cormack
Essendon Football Club
Correspondence: scormack@essendonfc.com.au
A commonly accepted principle of training is that a period of loading followed by adequate rest
results in improved performance (Lambert and Borresen 2006). Due to this, monitoring the fatigue
response to a training stimulus has become an important area of focus for many practitioners and
researchers (Coutts, Slattery et al. 2007; Cormack, Newton et al. 2008; Borresen and Lambert
2009). The general aim of this work has been to develop tools to assist in optimising the training
program and minimising negative outcomes, both in the form of injury and undue fatigue.
Numerous methods have been proposed to assist in this process. These include a variety of self
reporting questionnaires (Foster, Florhaug et al. 2001; Main and Robert 2009) and potentially
useful objective markers such as hormonal and neuromuscular status (Cormack, Newton et al.
2008; Cormack, Newton et al. 2008). The challenge for the practitioner is the implementation and
interpretation of data from valid and reliable measurement devices. It is arguable that the greatest
benefits from monitoring and manipulation of training programs are achieved when all stakeholders
(players, coaches, sport scientists and administrators) understand and commit to a particular
approach.
Fatigue has been variously defined as a reduction in the ability to produce force or the inability to
continue performance at a given workload (Lopes-Martins, Marcos et al. 2006). Multiple models
have been proposed to describe fatigue, including the Cardiovasuclar, Energy Supply/Depletion,
Biomechanical, Thermoregulatory and Neuromuscular models (Noakes 2000). Each model
suggests a slightly different origin of fatigue and it is likely that each one has a varying level of
contribution to the fatigue state depending on the activity being undertaken. Fatigue has also been
classified along a severity and time continuum. Acute or transient fatigue results in homeostasis
being returned within minutes or hours whilst functional overreaching describes fatigue that occurs
as part of planned training overload. Non-functional overreaching occurs outside the planned
response of the training program and can persist for weeks (Meeusen, Duclos et al. 2006; Coutts,
Reaburn et al. 2007). The most severe stage is overtraining. This occurs where, despite reductions
in training load, physical and psychological fatigue symptoms continue, sometimes for several
months (Grant 2004).
Arguably the greatest challenge for the practitioner is to implement a system of monitoring load
and fatigue that allows early detection of unplanned responses to the training stimulus and allows
initiation of interventions aimed at minimising negative outcomes. The most suitable monitoring
tools will vary depending on individual program requirements, however a common starting point
should be an agreement by all stakeholders that a particular system or philosophy will be adopted
and adhered to.
The following example (Figure 1) currently in use in an elite program, provides a guide to the type
of system that can be implemented. A critical component of this model is agreement by all involved
regarding the approach to fatigue monitoring and load modification. The annual plan is varied for
individual athletes (regardless of acute fatigue status) in accordance with previously agreed
criteria. Acute training load variations are made on the basis of valid and reliable assessment
items. Importantly, scores on each measurement tool are analysed using appropriate statistical
techniques (Hopkins 2000; Pettit 2010) including the establishment of appropriate baseline values
from which comparisons can be made. In general, the current model uses magnitude based
inferences and standard difference scores to determine the practical importance of any change on

42
an individual basis (Hopkins 2000; Pettit 2010). This occurs on both a weekly and rolling average
basis. Although gaining popularity, values from individual items are not currently combined to
produce a single number representing overall fatigue status or risk of injury. Whilst this is
theoretically possible (and under investigation in the current model) and potentially attractive via
multiple regression equations or mixed modelling, such an approach may carry the risk of
oversimplifying complex biological processes and result in a diluted understanding of an athlete’s
response.
Table 1: Load and Fatigue Monitoring Program currently in place in an elite football setting.
Frequency Monitoring/Measurement Tool
Annually
•
Agreed Training Philosophy→ load selection
→ interventions
• Periodisation
Monthly
•
Multi-component Training Distress Questionnaire
Weekly
•
Neuromuscular fatigue
(1 x wk)
• Hormonal status (1 x wk)
• Load, Monotony & Strain (calculated from RPE)
Sessional
•
Pre training Wellness Questionnaire (3 x wk)
with follow up → Physio
→ Dietitian
→Sleep assessment
• RPE
Whilst the system outlined above should by no means be considered perfect, it combines both
subjective and objective measures in an attempt to optimise the training stress. A key feature is the
“buy in” from all involved in the training process (coaches, players, sports science/medicine staff)
combined with a substantial allocation of resources. It is important to note that data is analysed
with appropriate statistical techniques in order to identify potential problems and a pro-active
approach is taken to training load modifications. Although it may be argued that the measurement
tools utilised in the current model could be substituted for other markers (eg. Heart rate variability,
muscle damage, inflammatory response etc), the concepts employed may have merit in numerous
elite environments.
References
Borresen, J. and M. I. Lambert (2009). "The Quantification of Training Load, the Training
Response and the Effect on Performance." Sports Med 39(9): 779-795.
Cormack, S. J., R. U. Newton, et al. (2008). "Neuromuscular and Endocrine Responses of Elite
Players to an Australian Rules Football Match." Int J Sports Physiol and Perf 3: 359-374.
Cormack, S. J., R. U. Newton, et al. (2008). "Reliability of Measures Obtained During Single and
Repeated Countermovement Jumps." Int J Sports Physiol and Perf 3(2): 131-144.
Coutts, A. J., P. Reaburn, et al. (2007). "Monitoring for overreaching in rugby league players." Eur
J Appl Physiol 99(3): 313-324.
Coutts, A. J., K. M. Slattery, et al. (2007). "Practical tests for monitoring performance, fatigue and
recovery in triathletes." J Sci Med Sport In press(doi:10.1016/j.jsams.2007.02.007).

43
Foster, C., J. A. Florhaug, et al. (2001). "A New Approach to Monitoring Exercise Training." J
Strength Cond Res 15(1): 109-115.
Grant, A. (2004). "Overtraining syndrome - Part 1." Sports Coach 27(1): 36-37.
Hopkins, W. G. (2000). "A New View of Statistics." Retrieved Feb 2007, from
http://www.sportsci.org/resource/stats/procmixed.html/#indif.
Lambert, M. and J. Borresen (2006). "A Theoretical Basis of Monitoring Fatigue: A Practical
Approach for Coaches." Int J Sports Sci and Coaching 1(4): 371-388.
Lopes-Martins, R. A. B., R. L. Marcos, et al. (2006). "Effect of low-level laser (Ga-A1-As 655nm) on
skeletal muscle fatigue induced by electrical stimulation in rats." J Appl Physiol 101: 283-288.
Main, L. and G. J. Robert (2009). "A multi-component assessment model for monitoring training
distress among athletes." Eur J Sp Sci 9(4): 195-202.
Meeusen, R., M. Duclos, et al. (2006). "Prevention, diagnosis and treatment of Overtraining
Syndrome." Eur J Sport Sc 6(1): 1-14.
Noakes, T. D. (2000). "Physiological models to understand exercise fatigue and the adaptations
that predict or enhance athletic performance." Scan J Med Sci Sports 10: 123-145.
Pettit, R. W. (2010). "The Standard Difference Score: A New Statistic for Evaluating Strength and
Conditioning Programs." J Strength Cond Res 24(1): 287-291.

44
Cricketers’ Hotspots & Coldspots: Talent Tracking the Key Development
Geographies of Australia’s Elite Cricketers
Geoffrey Woolcock, Dwight Zakus, Murray Bird, Emily Hatfield
Nathan Campus, Griffith University, Brisbane
Correspondence: g.woolcock@griffith.edu.au
There is a dearth of spatial and social data that addresses causal factors in the identification and
development of sporting talent (Baker & Horton 2004; Abernethy & Farrow 2005). It is generally
acknowledged that critical social factors such as family upbringing and the socio-economic status of
resident communities are likely predictors of sporting talent development but in Australia, aside from a
few ad hoc and sport-specific case studies, little rigorous and longitudinal empirical data has been
collected and collated to advance causal claims in this area.
Most sporting commentary draws attention to The "Wagga Effect", a term that has been used
frequently in the Australian media to describe the disproportionately large number of elite
sportsmen and women that originate from the city of Wagga Wagga in southern New South Wales.
It is speculated that the phenomenon may arise in rural areas where the population is large enough
to sustain the presence of a large number of sporting codes, but small enough to ensure that
talented individuals are exposed to adult-level competition at an earlier age. However, this
speculation remains just that in the absence of rigorous data collection and analysis across a
range of sports.
To address this deficiency, an Australian Research Council (ARC) Linkage grant - awarded in late
2009 and including Cricket Australia as one of four industry partners – is focusing on multiple
aspects of sporting talent ID and development pathways. This research will be unique in aligning
key spatial, geographical and environmental characteristics with the development of sporting
talent. In particular, it seeks to scrutinise the ‘pyramid’ theory that the greater the relative
participation in sport, the more likely that sporting talent will emerge.
The first research stream on geospatial determinants has commenced in early 2010 and
specifically focuses on the key developmental location for elite Australian sportspeople. This
location is defined as where the athlete was resident for a majority of time in their late primary and
early high school period. The specific focus on elite cricketers will mimic the methodological
effectiveness of a pilot study conducted by the same researchers in 2009 of all Australian Football
League (AFL) draftees 1997-2009, using three banks of data:
(a) the main junior club and/or school of draftees as the basis for both defining talent and in locating
players to the key place of development;
(b) comparison with AFL Auskick (5-12 y.o.) annual participation data for relevant regions (95
across Australia) averaged between 2000 and 2008 participation figures;
(c) comparison with Census population figures for 5-12 y.o. matched to Auskick regions;
The data was thus able to draw attention to AFL talent ID ‘hotspots’ and ‘coldspots’ by using three
different calculations. These three different ratios, where Talent = the number of players drafted from
the Auskick region that matches to their main junior club and/or school, were:
(i) Talent / AFL Auskick (5-12 y.o.) participation averaged between 2000 and 2008;
(ii) Talent / 2006 Census population (5-12 y.o.) matched to Auskick regions;

45
(iii) Talent / (average annual AFL Auskick (5-12 y.o.) participation / 2006 Census (5-12 y.o.)).
The equivalent ‘talent tracking’ research applied to cricket will specifically focus on any Australian
player who has played a first-class cricket match over the equivalent time (i.e., 1997-2010)
matched against participation of 12-18 year old boys in each of CA’s 81 regions, in turn matched
(through Geographic Information Systems (GIS) technologies) to overall population of 12-18 year
old boys in these regions.
Data collation and analysis commenced in March 2010 and preliminary findings will be presented.
Acknowledgement: Cricket Australia provided funding for this project, along with other industry
partners, which is appreciated.
References
Abernethy, B & Farrow, D. (2005). Contextual factors influencing the development of expertise in
Australian athletes. In Proceedings of the ISSP 11th World Congress of Sport Psychology [CD].
Sydney: International Society of Sport Psychology (ISSP). [ISBN: 1 877040 36 3].
Baker, J. & Horton, S. (2004) ‘A review of primary and secondary influences on sport expertise’,
High Ability Studies, 15 (2), 211-228.

46
Past, Present and Future of Injury Surveillance in Australian and World Cricket
John Orchard, Trefor James, Alex Kountouris, Marc Portus
Sport Science Sport Medicine Unit, Cricket Australia
Correspondence: johnworchard@gmail.com
Injury surveillance has been undertaken continuously at elite adult men’s level (international and
List A) for the past twelve seasons (Orchard et al., 2006). In the last five of these seasons, there
has been more cricket played with most of the growth being the newest form of the game -
Twenty/20 cricket. Since the introduction of a regular Twenty/20 program, injury incidence rates in
each form of cricket have been fairly steady although prevalence (the amount of missed playing
time) has risen (Orchard et al., 2010). Because of the short match duration, Twenty/20 cricket
exhibits a high match injury incidence expressed as injuries per 10000 hours of play (Orchard et
al., 2006). Expressed as injuries per days of play, Twenty/20 cricket injury rates compare more
favourably to other forms of cricket. In addition, we now understand that many, if not the majority,
of non-contact injuries in cricket are gradual onset overuse injuries.
For pace bowlers, a total bowling match workload >50 overs in a first class match is associated
with a 1.8 times increased risk of bowling injury (per ball bowled) over the next 21 days (Orchard et
al., 2009). Similarly, for pace bowlers, a workload of >30 overs in the second innings of a first class
match is associated with a 2.3 times increased risk of bowling injury (per ball bowled) over the next
28 days (Orchard et al., 2009). For bowlers who play both first class and limited overs cricket, an
injury which has its genesis in overuse from first class cricket may seem to ‘occur’ during a limited
overs game. Although hard data has not been uncovered yet, it is likely that the reverse may occur
as well – that a bowling injury may ‘occur’ in a first class match which has its genesis in under-
preparation if the bowler has a poorly periodised workload plan or has been involved in a
Twenty/20 tournament and does not bowl more than 4 overs at any one stint for a prolonged
period.
Given the high numbers of injuries which are of gradual onset, perhaps seasonal injury incidence
rates (which typically range from 15-20 injuries per team per season) are the best measure of
injury incidence (Orchard et al., 2005). The rates for seasonal incidence should be viewed from the
perspective of an ‘injury’ definition requiring missed playing time and a ‘team season’ being 25
players and 60 days of participation. Thigh and hamstring strains have become clearly the most
common injury in the past three years (greater than 4 injuries per team per season) with a recent
increase probably associated with the increased amount of Twenty/20 cricket (Orchard et al.,
2010). Hamstring strains in particular can occur in all facets of the game – bowling, batting and
fielding – with the non-bowling hamstrings relatively common in Twenty/20 cricket. Expressed as
injuries per days of play, there is a trend that Twenty/20 cricket is leading to reduced numbers of
bowling injuries, but increased number of injuries batting and fielding, compared to other forms of
cricket.
Injury prevalence rates have risen in recent years in conjunction with an increase in the density of
the cricket calendar. Annual injury prevalence rates (average proportion of players missing through
injury) have exceeded 10% in the last few seasons, with the injury prevalence rates for fast
bowlers approaching 20% (Orchard et al., 2010). If the status quo persists – in particular a very
crowded cricket calendar with all three forms of the game prominent – injury prevalence rates will
probably remain high by historical levels.
Radical changes in thinking may be required to reduce the injury prevalence amongst pace
bowlers. Internally, a move towards a rotation mentality for fast bowlers in first class cricket would
represent a culture change but may prolong the health of fast bowlers. Such a mentality is

47
completely accepted in Major League Baseball. There are two barrier towards this change: 1) the
injury/match payment system in Australian cricket with the grey area of
rested/dropped/rotated/injured needing to be cleared up so that certain bowlers do not feel
financially disadvantaged by any change; 2) at the elite level there is a perceived performance
disadvantage not having the best 3 or 4 fast bowlers playing as many games as possible. With
carefully planned fast bowling talent management we believe that there would actually be
significant medium to long term competitive advantages stemming from the necessity of needing 8
fast bowlers of international calibre rotating through the team. To some extent, perhaps as much
through necessity than deliberate strategy, we are already slowly moving in this direction.
Externally, consideration should be given to rule changes which may reduce the impact of injury,
such as allowing the 12th man to play as a substitute in first class cricket. Cricket is the only major
sport which does not allow full injury substitution. It is clear now that over-bowling has significant
late injury consequences so there is a strong argument to modernise the rules of cricket in this
respect. Uptake of the AMS (Athlete Management System) represents an improvement in injury
management in terms of communication and standardised recording. It also sets a framework
under which injury surveillance could realistically be expanded in the near future to include junior
and women’s teams.
Although there are limitations to injury surveillance in Australia currently, we clearly remain the
world leader in this area and have published the majority of the cricket injury surveillance literature,
although other countries have also published limited surveillance data (Newman 2003; Stretch
2003; Mansingh et al., 2006). It is hoped that the time is not far away that the ICC is able to assist
the majority of Test playing nations in the development of injury surveillance. From an Australian
perspective it almost should be insisted that injury surveillance is conducted on our contracted
players playing in overseas competitions. An injury passport system whereby a report is prepared
and transferred between treating medical teams will become increasingly needed for players who
move between competitions and countries throughout the year.
References
Mansingh, A., L. Harper, et al. (2006). "Injuries in West Indies Cricket 2003-2004." British Journal
of Sports Medicine 40: 119-123.
Newman, D. (2003). A prospective study of injuries at first class counties in England and Wales
2001 and 2002 seasons. Second World Congress of Science and Medicine in Cricket, Cape Town.
Orchard, J., T. James, et al. (2006). "Injuries to elite male cricketers in Australia over a 10-year
period." Journal of Science and Medicine in Sport 9(6): 459-467.
Orchard, J., T. James, et al. (2010). "Changes to injury profile (and recommended cricket injury
definitions) based on the increased frequency of Twenty20 cricket matches." Open Access Journal
of Sports Medicine 1: May 2010.
Orchard, J., T. James, et al. (2009). "Fast Bowlers in Cricket Demonstrate Up to 3- to 4-Week
Delay Between High Workloads and Increased Risk of Injury." American Journal of Sports
Medicine 37: 1186-1192.
Orchard, J., D. Newman, et al. (2005). "Methods for injury surveillance in international cricket."
Journal of Science and Medicine in Sport 8(1): 1-14.
Stretch, R. (2003). "Cricket injuries: a longitudinal study of the nature of injuries to South African
cricketers." British Journal of Sports Medicine 37: 250-253.

48
The Relationship between Quadratus Lumborum Asymmetry and Lumbar Spine
Injury in Junior Cricket Fast Bowlers
Alex Kountouris1,2, Jill Cook², Marc Portus3, Howard Galloway4, John Orchard3,5
1 Physiotherapist, Australian Cricket Team, Cricket Australia, Melbourne
2 School of Exercise and Nutrition Sciences, Deakin University, Melbourne
3 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
4 Department of Medical Imaging, The Canberra Hospital, Canberra
5 School of Public Health, The University of Sydney, Sydney
Correspondence: alex.kountouris@cricket.com.au
Bowling side quadratus lumborum (QL) asymmetries have been previously reported in junior and
senior cricket fast bowlers using magnetic resonance (MR) imaging (Engstrom, Walker, Kippers, &
Mehnert, 2007; Hides, Stanton, Freke, McMahon, & Richardson, 2008; Ranson, Burnett,
O'Sullivan, Batt, & Kerslake, 2008). Additionally, Engstrom et al (2007) found a strong relationship
between bowling side QL asymmetries and lumbar spine stress fractures in junior cricket fast
bowlers that were followed over four consecutive seasons. Hides et al (2008) also reported that
adult fast bowlers with low back pain (not specifically lumbar stress fractures) had greater QL
asymmetries than bowlers who did not have low back pain. These studies have the potential to
promote funding and research into intervention programs targeting QL asymmetries. As such, the
purpose of this study was to investigate and confirm the presence of QL asymmetries in junior fast
bowlers and to determine whether there is a relationship between QL size and lumbar spine injury
over a single cricket season. Additionally, we aimed to evaluate the methods involved in measuring
paraspinal asymmetry using MR imaging and outline the potential problems associated with these
measures as this is a relatively new area of research.
Lumbar spine magnetic resonance (MR) imaging of 48 junior male fast bowlers, with a mean age
of 14.8 years (range 12-17 years), were performed prior to the beginning of the 2002/2003
Australian cricket season as part of a larger research project. All bowlers were injury free at the
time of MR imaging. These baseline MR images were performed at a single radiology clinic, using
the same MR machine and protocol. The MR protocol implemented was similar to that used in
previous research (Engstrom, et al., 2007). Each MR image slice was evaluated by a single
investigator (AK) to determine whether the QL muscle boundaries were clearly visible for inclusion
in the study. The cross-sectional area of each QL image was measured and compared with the
corresponding image on the other side of the spine to determine side to side difference
(asymmetries). A single musculoskeletal radiologist (HG) evaluated the baseline MR images of
each participant to determine whether there was radiological evidence of injury. Any player who
reported pain during the season was referred to an experienced sports medicine physician who
confirmed the injury diagnosis using a combination of clinical judgment and appropriate radiological
procedures. Only lumbar spine injuries were included in this study, with participants being placed in
one of the following three categories;
1. Lumbar spine bone stress injury (lumbar stress fracture or stress reaction);
2. Soft tissue lumbar spine injury (disc, muscle or ligament injury etc);
3. No lumbar spine injury .
Ethics approval for this project was obtained from Deakin University Human Research Ethics
Committee.
Of the 48 players who had lumbar MR imaging at baseline, 10 participants were excluded from the
final analysis because there were no MR image slices where QL could be measured. Only twenty-
five per cent of MR images, where QL was in the field of view, met the inclusion criteria. The

49
reasons for exclusion included the overlapping of muscle boundaries and movement artifact. In the
current study, QL could only be measured between the L2 and L4 vertebral levels, which is
consistent with previous research (Ranson, Burnett, Kerslake, Batt, & O'Sullivan, 2006; Ranson, et
al., 2008).
The 38 participants who remained in the study had a mean age of 14.9 years (range 12-17 years,
SD 1.34), height 176 cm (range 148-192cm, SD 9.79) and weight was 66 kg (range 35-92kg, SD
12.71). The average QL asymmetry was 13%, which is similar to previous findings in junior fast
bowlers (Engstrom, et al., 2007). Fifty-five per cent of participants had asymmetries greater than
10%. There was no significant difference in the number of participants with dominant and non-
dominant side QL asymmetry. However, there was a significant difference in the magnitude of
asymmetry between the dominant side (10.5%) and non-dominant (16.4%) asymmetries. Pearson
r correlation of repeat measurements of QL asymmetry for randomly selected images (18%) was
r=.968.
During the cricket season, a total of eight players (21%) developed pain due to lumbar spine bone
stress injury (stress fractures and stress reactions of the pars interarticularis or pedicle). The mean
age, height and weight of players who developed lumbar bone stress injuries was 15.5 years
(range 13.4-17.4 years, SD 1.51), 179 cm (172-189 cm, SD 5.23) and 73 kg (62-82 kg, SD 7.67),
whereas participants who did not sustain a lumbar bone stress injury were 14.8 years (12.5-17.1,
SD 1.29 ), 175 cm (148-188cm, SD 10.59) and 64 kg (35-92 kg, SD13.23). There was no
significant difference between participants who were injured and not injured in terms of age (Mann-
Whitney Z=-.950, p=.342) and height (Mann-Whitney Z=-.609, p=.543). Participants weight was
also not significantly different (Mann-Whitney Z=-1.755, p=.79), although there was a trend towards
significance, with those injured having the lower body weight.
There were four participants who had radiological evidence of lumbar bone stress (and
asymptomatic) at baseline and all four went on to become symptomatic during the cricket season
with lumbar stress fractures.
The magnitude of asymmetry was divided into three groups (0-10%, 11-20% & >20%) and
compared to injury status. There was no significant difference in the number of players in each of
three asymmetry groups when compared to injury status (χ2 (4) = 6.28, p= .180). Additionally, there
was no significant difference between the average asymmetry for players who sustained lumbar
spine injury (soft tissue and bone stress) and those players who were uninjured during the cricket
season ((χ2 (2) = 1.242, p=.537). When the participants were grouped as either having a lumbar
stress fracture (mean asymmetry 15.7%) or no lumbar stress fracture (mean asymmetry 12.4%),
there was still no significant relationship (Mann-Whitney Z= -1.11, p=.267).
Conclusion; Contrary to previous research, this study demonstrated that there was a similar
distribution of asymmetry between the dominant and non-dominant sides. As this study completed
image analysis only on clear QL images, the presence of only dominant side asymmetry must be
questioned. Additionally, we did not find a relationship between lumbar stress fractures and QL
asymmetry, as reported previously (Engstrom, et al., 2007).
Another important finding from the study was that quadratus lumborum was not adequately visible,
in a high percentage of MR images, to allow adequate measurement without creating doubt about
the validity of the measurement. This study demonstrated the inherent difficulties of measuring
three-dimensional structures using two-dimensional imaging methods.
Acknowledgements: This project was funded by Cricket Australia. The authors would like to
acknowledge David Pyne (Australian Institute of Sport) for anthropometry measurements in this
study and Patrick Farhart for his involvement in the injury surveillance of the participants.

50
References
Engstrom, C., Walker, D., Kippers, V., & Mehnert, A. (2007). Quadratus lumborum asymmetry and
L4 pars injury in fast bowlers: A prospective MR study. Medicine and Science in Sports and
Exercise, 39(6), 910-917.
Hides, J. A., Stanton, W. R., Freke, S., McMahon, S., & Richardson, C. A. (2008). MRI study of the
size, symmetry and function of the trunk muscles among elite cricketers with and without low back
pain British Journal of Sports Medicine, December.
Ranson, C., Burnett, A., Kerslake, R., Batt, M., & O'Sullivan, P. (2006). An investigation into the
use of MR imaging to determine the functional cross sectional area of lumbar paraspinal muscles.
European Spine Journal, 15, 764-773.
Ranson, C., Burnett, A., O'Sullivan, P., Batt, M., & Kerslake, R. (2008). The lumbar paraspinal
muscle morphometry of fast bowlers in cricket Clinical Journal of Sports Medicine, 18(1), 31-37.

51
Psychological Aspects of Workload Management in Elite Sport
Scott Cresswell
School of Sport Science, Exercise & Health, The University of Western Australia
Correspondence: scott@sportpsychology.co.nz
Elite players are, by definition, a valuable and limited resource. An elite player’s role routinely
exposes the athlete to mental and physical demands across the cricketing year that represent a
significant workload. Effective management of this workload can result in positive adaptation and
high performance, whereas ineffective management can have a number of negative outcomes for
both player performance and welfare. As a result effectively managing player workload is an
important and necessary task for all management and support staff involved with elite sport. This
presentation will summarise findings from a series of projects with professional sport highlighting
implications for the management of player workload.
Player workload involves the interaction of observable (often physical) as well as psychological
factors. The psychological aspects of player workload (i.e. antecedents, processes and
consequences) are often overlooked in favour of more visible, physical aspects of workload and
their directly observable consequences. This presentation will build on a model of these easily
observable workload factors introducing psychological aspects of workload management (Smith,
1986, Cresswell & Eklund, 2003).
Psychological and observable (physical) factors associated with workload don’t always work in
sync with each other as expected (e.g., Cresswell & Eklund, 2007a). The unpredictable nature of
these relationships is often due to psychological processes, such as the appraisal of demands, the
appraisal of ability to deal with these demands and attempts to cope. The presentation will use
case studies of elite athletes to demonstrate the nature and role of these psychological aspects
(Cresswell & Eklund 2007c).
The effective management of workload requires accurate and reliable measurement. Commonly
identified indicators of workload in elite sport include the number of games played and hours
trained. While easily quantifiable, past research suggests these indicators are not as consistently
and strongly associated with workload outcomes as first thought. Key factors researchers have
identified as being related to the impact workload has on performance and welfare include travel
demands, injury, non selection, playing position, experience, an anti-rest culture, pressure to
comply with demands as well as media/public expectations (Cresswell & Eklund, 2006a; 2007b;
2007c). Consistently across this research:
• Professional rugby players who reported higher levels of injury reported higher levels of
chronic exhaustion
• Players who were not consistent starters reported higher levels of reduced accomplishment
and frustration, sometimes resulting in exhaustion.
While a number of these findings are directly applicable to cricket, there are also some unique
challenges to consider, such as the length of competitive events and unique physical demands.
Factors that could be considered unique to cricket include the length of games and tours, travel
demands, repetitive physical tasks as well as emotional labour due to strict on-field conduct rules.
These unique demands highlight the need to measure and assess workload that is specific to your
cricket environment.

52
To avoid erroneous conclusions and poor management a comprehensive and integrated approach
to measuring workload is required. Past research also indicates the effective measurement of
workload needs to take regular seasonal variations into account (e.g., Cresswell & Eklund, 2006b).
The measurement of workload impact from a psychological perspective can be assessed through a
number of multi-choice questionnaires that assess different timeframe impacts. The Profile of
Mood States (POMS; McNair, Lorr & Droppleman, 1992) can be used as a relatively immediate
state measure of workload impact, where as the Athlete Burnout Questionnaire (ABQ; Raedeke &
Smith, 2009) is designed to assess more long term chronic impacts of workload. The Recovery-
Stress Questionnaire for Athletes (RESTQ Sport; Kellmann, 2002) is designed to assess the extent
to which athletes are physically and/or mentally stressed in order to manage the training process
and avoid negative workload impacts. Practical application of these measures is restricted by the
length of the questionnaires, RESTQ – 76 items, POMS – 12 items, ABQ – 15 items, and the
potential for regular or too frequent measurement resulting in poor data quality. As a result it is
recommended a mix of these three measures is applied, including an abbreviated version of the
RESTQ, and correlated with existing physical measures of training/plating load and fitness testing.
Currently there are no widely identified best practice solutions for monitoring and managing the
workload of elite athletes. Research, however, indicates players in some team environments report
significantly less negative workload outcomes (e.g., Cresswell & Eklund, 2005) highlighting that not
all current practice is equal. Team environments that encountered the least negative impacts from
workload employed a number of strategies including:
• Investment in a large number of high quality support staff. This was seen by players as a
positive and strategic move within player salary capped environments.
• High quality of physical training and individual monitoring
• Consistent and accurate management of weekly schedules (schedule posted in advance,
rare changes)
• Recovery team sessions and individual strategies such as time out from the environment
and extra individual recovery sessions
• Supporting meaningful work outside sport – designing weekly structures to enable outside
activities to be planned around sport
Interestingly, performance as measured by win/loss ratio was shown to impact on teams
differently. While some teams reported negative workload impacts others indicated positive
impacts in response to the same performance ratios. Performance expectations clearly play a role
in this relationship, but it was also noted that management teams often adjusted schedules in
different directions based on similar game results.
In this presentation the range of strategies applicable at the individual and organisational level to
prevent and manage player workload will be highlighted. Specifically, findings of a season long
study conducted with two professional English rugby clubs to assess the effectiveness of a player
level educational intervention designed to prevent negative consequences associated with
workload will be reviewed. A research based educational intervention was designed and
customised to the specific team environments in an effort to prevent and manage the negative
impact of player workload. Players were taught mental skills to cope with demands such as injury,
non-selection and physical training load. Specifically the program consisted of five group sessions
of 20 to 30 minutes duration, summarised in Table 1.

53
Table 1: Player intervention program – content summary
Session Program Content
1 - Introduction
 Extended surveyed including potential burnout causes
 Purpose of the program
 Burnout characteristics & causes
 Past Results for your team
2 - Results & Discussion  Positive Factors
o Social Support,
o Sense of community
o Rugby tasks***
o Rewards***
o Team atmosphere***
 Factors Contributing to Burnout*
o Workload
o Injury
o Money Hassles
o Contract Negotiation
o Non-selection
o
Time changes**
3 - Player Strategies  Cognitive Appraisal
 Thought Management
o Reframing
o Countering
 Scenarios/Role play
o Injury
o
Non-selection
4 - Team Strategies  Team values
 Team support
5 - Summary  Follow up on actions identified
 Application of strategies
 Review via case study
 Questions
Note* = as identified from extended survey items, **=London Irish only, ***=Wasps only
While the program received positive feedback from players and coaches it did not result in a
statistically significant reduction in negative consequences associated with player workload. The
London Irish and London Wasps teams were matched with other Premiership teams who had
similar performance outcomes during the season, but did not participate in the player program
designed to prevent and manage burnout. London Irish was matched with Harlequins, London
Wasps was matched with Gloucester. Results again indicated the player program failed to have a
beneficial effect on the level of exhaustion, sport devaluation or reduced accomplishment reported
by players. A statistical comparison was not possible between players who did and did not
participate within the same club because of the small numbers of players who did not complete the
program. It was concluded that in this environment a player level intervention is unlikely to prevent
the negative impact of player workload without corresponding organisation level strategies.
Finally, a review of the organisation level strategies employed in the English and New Zealand
rugby environment designed to reduce the potential negative impact of player workload will be
explained. Strategies considered in the England rugby environment are summarised in Table 2.

54
Table 2:Workload strategies considered by England Rugby
Stressor/Factor Strategy
Injury
 Implementation of a minimum ratio of support staff to players
 A review of injury management strategies, including the
appointment of an independent medical adviser
 Players develop Coping skills through the Professional
Development Program
High number of games with
little rest/recovery
 Minimum rest periods – between games and in the off
season
 Maximum game limit to apply to all players and a review of
monitoring (games less than 39 minutes should count)
 Decompression time pre and post Internationals
 Education for coaches and management regarding workload
management
Contract negotiation  Registration of player agents
 Negotiation skills training for players
Non-selection  Education for coaches and players regarding non-selection
Life outside the game  Improve Professional Development Manager ratio
 ½ a day for professional development fixed in the calendar
In conclusion, the monitoring and management of player workload is essential given the limited
resource that is the elite player talent pool. Strategies currently identified are limited in their
effectiveness if used in isolation. As a consequence a combination of these strategies fitted to your
unique situation is recommended.
References
Cresswell, S.L. & Eklund, R.C. (2003). The athlete burnout syndrome: A practitioner’s guide. New
Zealand Journal of Sports Medicine, 31(1), 4-9.
Cresswell, S.L. & Eklund, R.C. (2004). The athlete burnout syndrome: Possible early signs. Journal
of Science and Medicine in Sport. 7(4), 481-487.
Cresswell, S.L. & Eklund, R.C. (2005). Changes in athlete burnout and motivation over a 12-week
league tournament. Medicine and Science in Sports and Exercise. 37(11), 1957-1966.
Cresswell, S.L. & Eklund, R.C. (2006a). The nature of athlete burnout: Key characteristics and
attributions. Journal of Applied Sport Psychology, 18, 219-239.
Cresswell, S.L. & Eklund, R..C. (2006b). Changes in athlete over a 30-week “rugby year”. Journal
of Science and Medicine in Sport, 9, 125-134.
Cresswell, S.L. & Eklund, R.C. (2007a). Athlete Burnout and Immune Function. New Zealand
Journal of Sports Medicine, 34, 5-11.
Cresswell, S.L. & Eklund, R.C. (2007b). Athlete Burnout and Organizational Culture: An English
Rugby Replication. International Journal of Sport Psychology, 38, 365-387.
Cresswell, S.L. & Eklund, R.C (2007c). Athlete Burnout: A longitudinal qualitative investigation. The
Sport Psychologist, 21, 1-20.
Cresswell, S.L. (2009). Possible early signs of athlete burnout: A prospective study. Journal of
Science and Medicine in Sport, 12, 393-398.

55
Kellmann, M. (2002) Psychological Assessment of Underrecovery. In Enhancing Recovery:
Preventing Underperformance in Athletes. Champaign IL: Human Kinetics.
McNair, D., Lorr M. & Droppleman, L.F. (1992). Profile of Mood State Manual. San Diego:
Educational and Industrial Testing Service.
Raedeke, T.D. & Smith A.L. (2009). The Athlete Burnout Questionnaire Manual. Morgantown, WV:
Fitness Information Technology.
Smith, R.E (1986). Toward a cognitive-affective model of athletic burnout. Journal of Sport
Psychology, 8, 36–50.

56
Planning & Monitoring Workloads: Identifying Performance Limiting Factors and
Developing Solutions
Stuart Karppinen
Australian Team Strength & Conditioning Coach, Cricket Australia, Melbourne
Correspondence: stuart.karppinen@cricket.com.au
Most people involved in elite level sport would be aware of the concept of traditional periodisation,
which starts with identifying a number of discrete competitions over the year that the athlete or
team is required to peak for. Typically organisation of the annual training program starts with those
important competitions and works forwards from there. From a physiological perspective, it is not
possible to improve the level of conditioning in several areas at one time; so that at certain times of
year the emphasis is on improving one parameter while other areas are simply maintained.
Periodisation is usually partitioned into cycles (meso, micro & macro) to allow the prescription of
training to change from a broad to a more precise focus
But how does this model fit within the Australian cricket team that has recently completed a 47
week period of competition with the longest preparatory period being 13 days in length.
Opportunity to improve physical performance in a traditional manner is difficult, given the limited
time available.
The traditional approach of program progression was most suitable to preparing athletes for one
major event, but with the need for members of the Australian cricket team to compete at high levels
for extended periods of time over the competitive season, another approach is required.
Managing the playing and training workloads for the Australian cricket team requires an adaptive
and flexible approach that allows for individual skill preparation whilst reinforcing key physical
components. The impact of the stress recovery cycle as the result of competition and training can
vary enormously within the playing group, due to numerous factors such as age, position, fitness,
recent training history, and performance. The model of monitoring and managing player workloads
comprises three components: Match hardness, Workload monitoring and Physical monitoring.
This model may prove useful for other strength & conditioning professionals and coaches to
manage player workloads in team sports during competition periods.
Match hardness is an assigned numerical value that estimates the potential difficulty of scheduled
competition. Through the weighting and scaling of numerous factors, an estimate on the perceived
difficulty of upcoming competition directly determines the volume and intensity of physical and skill
preparation. This approach is achieved by identify factors that are rigid and cannot be influenced,
with the outcome resulting in modification to the factors that can be influenced.
Uncontrollable Factors
• Opposition.
• Country.
• Match format.
• Climate.
• Hours of travel.
• Number of days for recovery between matches
Controllable Factors
• Training duration.
• Training intensity.
• Training modality.

57
• Team selection.
Workload monitoring measures the actual impact of training and match performance of each
individual player, and comprises both objective and subjective information. Session RPE values
are taken for each match and training session, and heart rate data is used at all training and
conditioning sessions.
Objective Measures
• Number of deliveries.
• Heart rate (TE, EPOC).
• Duration.
Subjective Measures
• RPE.
Physical monitoring measures the change that occurs as a result of competition and the impact
that the match performance has on the individual athlete’s rate of recovery. As with the workload
monitoring, the process of physical monitoring comprises objective ad subjective ratings.
Objective Measures
• Knee to wall.
• Internal rotation.
• Counter movement jump (single & repeated).
• Reactivity test.
• Illness.
• Injury.
Subjective Measures
• Energy levels.
• Muscle soreness (passive).
• Muscle soreness (active).
The outcome of these measurements, combined with assessment of the match hardness,
determines what the primary objectives are for that period of preparation. During periods of high
match hardness and high workloads, training is modified to reduce low stress response with a high
focus on skill execution and recovery strategies. During periods of low match hardness and low
workloads, training is altered to elicit adaptive responses with a greater emphasis on improving
physical performance.
Conclusion: Numerous factors influence the management of athletic performance during period of
competition. By identifying potential performance limiting factors throughout the duration of season
both strength & condition professionals and coaches can potentially limit the adverse effects of
over training and overuse injuries in an attempt to improve performance.
Acknowledgements: Thanks to Mark Cameron from Elite Athlete Management Systems.

58
Day 2: Wednesday 2 June
FREE PAPER ABSTRACTS
PRESENTED IN ALPHABETICAL ORDER BY LEAD AUTHOR SURNAME

59
The Effect of Footwear on the Lower Limb Biomechanics during the Fast Bowling
Delivery Stride: A Single Subject Case Study
Chris Bishop1, Dominic Thewlis1, Wayne Spratford2,3, Simon Bartold4, Marc Portus2,
Nick Brown3
1 University of South Australia, Adelaide
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
3 Biomechanics Department, Australian Institute of Sport, Canberra
4 University of Melbourne, Melbourne
Correspondence: Christopher.Bishop@postgrads.unisa.edu.au
The effect of cricket footwear on fast bowling biomechanics and injury risk is not well understood.
As a result, ranges of footwear from custom made to sports-specific bowling shoes are commonly
used. A trend amongst elite fast bowlers is to “spike-up” cross trainer shoes instead of wearing
conventional commercially available cricket shoes. Although the intentions and reasoning for the
modification of cricket shoes remain poorly understood, more importantly, the biomechanical effect
of footwear on lower limb biomechanics of elite fast bowlers remains unknown.
Bishop & Thewlis 2009 explored the relationship between footwear and two-dimensional lead limb
knee and ankle joint orientation, and whether this changes between three cricket shoes commonly
worn by the fast bowler. The authors found a significant difference in front knee joint
extension/flexion angle between the three footwear environments at heel strike (P<0.05). There
was no significant difference noted in the front knee joint extension/flexion angle at mid-stance and
during the propulsive phase. The ankle plantarflexion/dorsiflexion angle did not change significantly
between the three shoes at heel strike, mid-stance or during the propulsive phase. Their results
support the technical notion that bowlers should land on their heel with a flexed front knee and
dorsiflexed ankle joint, proceed to extend their knee joint and plantarflex their ankle joint through
midstance, and remain at or near this position in preparation for propulsion and toe off. However,
the authors stated that any noticeable differences in knee joint sagittal plane kinematics found in
this study can be explained by experimental and methodological errors, whereby true joint
measurements are not recorded due to insufficiency in two-dimensional measurements and
perspective camera error. Realistically, three-dimensional analysis using a multi-segment lower
limb model is required to truly quantify this relationship. The study did provide an initial insight into
this relationship and identified that a change in knee joint angle as a result of differences in
footwear may prove important when considering an individual’s susceptibility to injury and the role
footwear may play in reducing the mechanisms of lower limb injury to the fast bowler in cricket.
The aim of this case study was to examine the effect of three types of cricket specific footwear on
the lower limb biomechanics of an elite fast bowler during the delivery stride. This study will
present a preliminary analysis using a single subject case study. The case used is from a larger
cohort study in progress.
Methods: This study was approved by the Australian Institute of Sport Local Ethics Committee and
Human Research Ethics Committee at the University of South Australia. A single male subject (age
23, height 202.5 cm, body mass 85.1 kg, BMI 20.7%) had 30 x 14 mm ∅ reflective skin-mounted
markers applied to lower limb anatomical landmarks as defined by Besier et al. (2003). The
markers on the foot were mounted to the shoe by palpation of underlying anatomical landmarks.
The subject performed their typical bowling action off their normal run-up, bowling at a stationary
projected batsman in an indoor laboratory. The subject was instructed to bowl so that he would not
bowl a no ball. This allowed the bowler to land on the force plates placed beneath the surface at
front and back foot strike of a legal delivery. Trials were excluded if both the front and back foot did
not strike the force plate. The bowler performed 6 trials in 3 different pairs of shoes; Shoe 1 was a

60
modified Cross Trainer (ASICS Gel 490TR), which had an external spiked sole added to the base
of the shoe (Figure 1a) Shoe 2 was a commercially available cricket shoe (ASICS Gel Strike Rate)
(Figure 1b) and Shoe 3 is the normal shoe of choice (which in the case was a highcut ASICS Gel 8
for 64) (Figure 1c). The order of the conditions was randomised.
a) b) c)
Figure 1: a) Shoe 1 – Modified ASICS 480TR, b) Shoe 2 – ASICS Gel Strike Rate, c) Shoe 3
ASICS Gel 8 for 64
Kinematic data were captured using a 20 camera VICON Mx motion capture system (Vicon Motion
Systems Ltd., Oxford UK) at 250 Hz. Ground reaction force (GRF) data were sampled using four
0.6 x 0.9 m piezoelectric force platforms (Type 9287BA, Kistler). Force data were sampled at 1000
Hz. Prior to collecting any dynamic data, a static calibration file was collected. This allowed for the
definition of the position and orientation in space of the segments of the lower limbs based upon
anatomical frames (Cappozzo et al (1995)). This process was performed for each condition.
Following this, the static markers were removed, leaving only dynamic markers required for
tracking the movement of individual segments. Motion of each segment was captured in Vicon
Nexus (Version 1.4, Vicon Motion Systems Ltd., Oxford, UK).
The kinematic and kinetic data were exported to Visual3D (C-Motion Inc, USA) for analysis. The
segments of the lower limbs were modelled in six-degrees of freedom based on the guidelines of
Cappozzo et al. (1995) and the recommendations of Thewlis et al. (2008). Right handed local
coordinate systems were defined for each segment. Joint kinematics was estimated using a XYZ
Cardan sequence, which is equivalent to the Joint Coordinate System (Grood & Suntay, 1983).
Joint moments were calculated using standard inverse dynamics.
The outcome measures extracted from the data for analysis were peak medial force, peak braking
force, peak vertical force, loading rate of the vertical GRF, front knee joint angle at heel strike, front
knee joint range of motion (ROM), front ankle joint angle at heel strike (HS), front ankle joint ROM,
peak knee joint extension and flexion moments at front foot strike. The means of all trials are
presented.
Results: A difference of 30% in peak medial GRF was noted between shoes 1 and 2. No difference
was noted in the peak braking force between any of the shoes. Shoe 1 accounted for a 0.3 BW
difference in vertical GRF component compared with shoes 2 and 3. The means and standard
deviations of these measures are seen in Table 1. Shoe 2 reported a higher loading rate than
shoes 1 and 3. A difference in knee joint angle at heel strike, ankle joint angle at heel strike and
peak knee joint flexion moment was noted between shoe 3 and shoes 1 and 2 as seen in Table 2.

61
Table 1: Front Foot Ground Reaction Force Components at Heel Strike (BW means ± SD)
M-L Force A-P Force Vert Force Loading Rate (BW.s-1)
Shoe 1 1.0 ± 0.2 5.2 ± 0.2 8.9 ± 0.3 316.8 ± 10.0
Shoe 2 0.7 ± 0.1 5.2 ± 0.2 8.6 ± 0.2 341.5 ± 27.3
Shoe 3 0.9 ± 0.1 5.2 ± 0.3 8.6 ± 0.2 306.6 ± 6.9
Table 2: Front Leg Lower Limb Joint Parameters
Knee @ HS Ankle @ HS Knee Jnt Moment @ HS Peak Knee Flexion Moment
Shoe 1 164.1 ± 0.6˚ -11.3 ± 0.5˚ 1.6 ± 0.1 N.m/kg - 0.8 ± 0.0 N.m/kg
Shoe 2 164.4 ± 1.2˚ -11.9 ± 0.2 1.6 ± 0.2 N.m/kg - 0.8 ± 0.1 N.m/kg
Shoe 3 159.8 ± 0.3˚ -18.3 ± 4.7˚ 1.7 ± 0.1 N.m/kg - 1.3 ± 0.1 N.m/kg
Discussion: This pilot study is the first three-dimensional analysis of the biomechanical effect of
footwear on the lower limb biomechanics of elite fast bowlers during the delivery stride. The three
shoes chosen in the study reflect the footwear trends of elite male fast bowlers in Australia. The
results of the kinematic analysis demonstrate the anticipated motion of the knee joint during the
delivery stride, where the knee joint will flex to attenuate forces at heel strike and then extend to
provide a rigid lever for the body to move forward. Likewise, it was expected that the ankle joint
would rapidly plantarflex upon heel strike to bring the foot to the ground and then dorsiflex as the
centre of gravity moves forward over the base of support through the rest of stance. There was a
difference between shoe 3 and shoes 1 and 2, especially in regards to front knee and ankle joint
position.
The results of the kinetic analysis did not provide any major differences amongst the shoes when
comparing individual components of the ground reaction force vector. The medial force data at
front foot strike were consistent with previous literature. Further, both the braking force and vertical
force were consistently higher than those previously reported. This was the first study to report
data on the knee joint moment at front foot strike. Although relatively consistent throughout stance,
Shoe 3 (bowler’s choice) resulted in a high knee joint flexion moment. This could be explained as a
coupling relationship, due to the increased plantarflexed position of the ankle joint in this footwear.
There was a considerable difference at the ankle joint between shoe 3 and shoes 1 and 2 at heel
strike. This is of particular clinical interest as shoe 3 (the bowler’s preference) should provide more
ankle joint support due to the lacing system extending proximal to the ankle joint. The total range of
motion for the ankle joint, as shown in Figure 2, did not differ substantially (Shoe 1 – 25.05˚, Shoe
2 - 24.4˚, Shoe 3 - 24.4˚). It appears that the hightop boots provides no different measured clinical
effect (i.e. support) than that offered by a lowcut alternative. However, the hightop boot held the
foot in a more plantarflexed position through the entire stance phase. Although the foot may remain
more plantarflexed and thus unstable, external stability will be provided by the shoe and thus it is
no surprise to see no difference in ankle joint ROM in the three footwear environments studied.
Conclusion: This study is the first study looking at the biomechanical effect of footwear on the elite
fast bowler during the delivery study. Although only a case study, the results highlight both the
kinematic and kinetic effects of different footwear designs. Further work examining the relationship
between footwear and lower limb biomechanics is currently in progress in a larger cohort trial.
Acknowledgements: The authors would like to acknowledge the funding support of ASICS Oceania
for providing the footwear and funding for this study. The authors would also like to thank Cricket
Australia for providing the subject and subject travel funding as part of their annual biomechanical
screening program. The authors are very thankful to the Australian Institute of Sport Biomechanics
and Performance Analysis Department for the use of their facilities and assistance in data
collection and in obtaining ethics approval.

62
References
Bishop C & Thewlis D. An exploratory 2-D kinematic. Journal of Science and Medicine in Sport,
Volume 12, Supplement 2, January 2010, Pages e132-e133
Cappozzo A., Catani F., Della Croce U., and Leardini A. Position and orientation in space of bones
during movement: anatomical frame definition and determination. Clinical biomechanics 10(4):171-
178. 1995.
Grood E. and Suntay W. A joint coordinate system for the clinical description of three-dimensional
motions: application to the knee. Transactions of the ASME. 105:136-144. 1983.
Thewlis D, Richards J, Bower J. Discrepancies in Static Knee Marker Placement based on
Different Anatomical Landmarks Produce Effects Greater than Previously Simulated. Journal of
Applied Biomechanics. 24:185-190. 2008.

63
The Biomechanics of the Initial Movement in Cricket Batting
Gerard Randika Dias and Rene Ferdinands
The University of Sydney, Sydney
Correspondence: randika@xlr8t20.com.au
The success of technical batting in the game of cricket generally depends on quick and precise
footwork. One important factor includes the initial reflexive movements of the feet that occur just
prior to ball release. These movements can take the form for three different motions: a backward
movement, a forward movement, or a combination of the two (Wololmer, 1993). Several greats of
the game, including Prince Kumar Ranjitsinjhi, Sir Garfield Sobers and Sir Donald Bradman, all
supported the notion that these preliminary initial movements are beneficial to a batsman’s
technique and performance (Bradman, 1958; Sobers, 1985).
With international bowlers achieving balls speeds up to 160 km/h, batters have approximately 500
ms-1 to perceive the position of the ball and react appropriately to execute the correct cricket stroke
(Abernethy, 1984; Muller, 2006). With only this short perception and reaction time, the ability of
batters to utilise initial movements to optimise foot work against fast bowlers should confer a
significant performance advantage. Hence, the aim of this study was to investigate the mechanical
differences in the initial movements between elite and amateur batters. The hypothesis is that elite
and amateur batters have different initial movement characteristics in terms of perception time,
force magnitude and direction, and stability
Eight batsmen (age range: 19-39 years) were recruited from the elite (N = 9: grade 2nd Xl and
above) and amateur levels (N = 9: premier club competition). Each batsman faced 30 deliveries
from one of two second grade opening fast bowlers. The deliveries were randomised to an equal
number of full pitched and short pitched deliveries so that the length of ball could not be predicted
beforehand. The testing took place on an artificial cricket pitch, enclosed within a suspended
cricket net in an indoor biomechanics laboratory at the University of Sydney.
The batsmen were fitted with a 48 retro-reflective marker set and their batting strokes captured by
a 14-camera Cortex Motion Analysis System (200 Hz). During the stance phase, each foot was
placed on a separate Kistler (Type 9287B) multi-component force platform to provide dynamic and
quasi-static measurements of body movements. Time of ball release by the bowler was recorded
by a TeleMyo 2400R Force Transducer, which was placed on the second finger of the bowling
hand. The motion analysis data was fed into a 16-segment three-dimensional kinematic model of
the batter and bat that was constructed in MATLAB (Version 7.0, Mathworks, Inc.). The model was
created by defining multiple kinematics chains based on the location and orientation of local joint
coordinate systems for each body segment according to the standard methodology of Grood et al.
(1983).
The initial movement was defined as the last foot movement made by a batsman prior to a decision
to actuate a final foot plant before impact. By observing the motion analysis video footage of
batsmen with synchronised ground reaction forces (GRF’s), two types of initial movement were
classified: the forward and backward initial movements. These were classified based on the
occurrence of the last maximum GRF prior to the initiation of final foot plant. Hence, if there was a
maximum GRF on the front force platform prior to a final back foot plant before impact, then the
batsman had a forward initial movement (Figure 1). Conversely, if there was a maximum GRF on
the back force platform prior to a final front foot plant before impact, then there was a back foot
initial movement.

64
Figure 1: Front Foot Initial Movement corresponds to MAX GRF on Back Foot
The range of reaction times from ball release to initial movement between elite batsmen and
amateur batsman were significantly different where F (1,39)=15.38, p<0.001 as seen in Table 1.
Elite batsmen had reaction times varying from 0.18s to 0.27s while amateur batsmen ranged from
0.33s to 0.43s. This timing difference lead to elite batsman producing their final foot plant 5%
earlier in the preparatory phase of a cricket shot when compared to amateurs.
Table 1: Average Reaction Times (Ball Release to Initial Movement) of Individual Subjects
Subject Initial Movement Average Reaction Time (s) Std. Dev. Reaction Time (s)
Amateur 1 Forward 0.34 0.06
Amateur 2 Forward 0.33 0.11
Amateur 3 Forward 0.42 0.08
Amateur 4 Forward 0.43 0.22
Elite 1 Forward 0.27 0.12
Elite 2 Forward 0.21 0.10
Elite 3 Forward 0.18 0.12
Elite 4 Backward 0.20 0.09
The front foot initial movement was completed after ball release and was easily observable during
back foot strokes. However, the forward initial movement could not be observed during front foot
strokes, since the front foot was off the ground prior to a decision to actuate a final foot plant. As a
result, the initial movement and final front foot movement were seamlessly integrated into one
continuous movement. By calculating the average of the front foot initial movement time during a
back foot shot, it was found that the end of the front foot initial movement occurred approximately
at the time of maximum GRF through back foot (Figure 1). Conversely, the opposite applied for the
one elite batsman with a back foot initial movement when playing forward: the back foot initial
movement was completed at the time of maximum GRF through the front foot.
It was observed that batsmen with a front foot initial movement had an advantage when playing
front foot shots, since their weight was transferred onto the back foot and the front foot was moving
forwards before the ball was released. However, when playing back these batsmen tended to
rapidly bring their front foot down and jump onto the back foot. Conversely, during back foot
strokes, it was observed that the batsman with the back foot initial movement was able to easily
shift weight onto the back foot by stepping backwards. In the case of a front foot shot, this batsman

65
still had time to plant his back foot down and step forward toward the ball to execute a stroke
efficiently. This is consistent with the hypothesis of Sir Garfield Sobers that the back foot initial
movement optimises the ability to play back without compromising the ability to move forwards
(Sobers, 1985)
Figure 2 shows that during the initial movement elite batsmen had a stable back lift angle, while
amateur batsmen were reducing their back lift angle. Hence, amateur batsmen tended to initiate a
second or double back lift prior to final ball contact, seen by the second peak in the graph. In
addition, the amateur tended to have a higher average back lift angle at all times prior to impact.
0
10
20
30
40
50
60
70
80
90
100
0 102030405060708090100
Angle(°)
PercentageofPreparatoryPhase(%)
FFShot‐ BatAngleComparison
EliteInitialMovement AmateurInitialMovement EliteBatAngle Ama tuerBatAngle
Figure 2 - Front Foot Shot Bat Angle from Vertical Plane
GRF’s at the time of the initial movement were also shown to be significantly different between
elites and amateurs (F = 16.35, p<0.001). Elite batsmen tended to have a more even weight
distribution and better stability to play both back foot and front foot shots (Table 2). At the onset of
the initial movement, elite batsman averaged 62.74% of weight on the back foot compared to
amateur batsmen who had 89.9%. This may indicate that elite batsmen may have a superior ability
to move either forward or backward in response to the perceived length of the delivery compared
to the amateurs. With almost all the weight on the back foot, amateurs may be able to move
forward easily. However, they would conceivably take extra time to produce a large shift of the
weight onto the front foot prior to moving back. With the limited reaction times in batting, optimising
specific weight distributions during the initial movements may be critical to the efficiency of
footwork cricket batting.
The purpose of this study was to analyse and determine whether there were differences in the
initial movement patterns between a sample of elite and amateur batsmen. Even though this is a
preliminary study with a relatively small sample number, there were data that tended to support the
hypotheses of this study. Elite batsmen had significantly different mechanical characteristics of
their initial movements compared to amateurs. If it is accepted that the initial movement is an

66
Table 2: Average x-Component GRF
Subject
Class
Back Foot
GRF Front Foot GRF Ratio % Weight on Back
Foot
Elite 140.69N -83.54N -1.68 62.74%
Amateur 234.80N -26.26N -9.28 89.94%
important determinant of footwork, then elite batsmen may be utilizing more effective initial
movement patterns to promote efficient footwork than amateurs. This study also showed that the
game of cricket need not only rely on coaching opinion to guide the advancement in batting
techniques. Three-dimensional kinematics analysis may contribute substantial knowledge to the
practical theory of batting technique. Discovering the critical factors that influence the preparatory
phases in batting, including further analysis of the initial movement, will provide technical
information that batters (male and female) can potentially use to improve their batting performance.
Acknowledgements: Paran Thangarajah, Dr Peter Sinclair, Martin Warren.
References
Abernethy, B. (1984). Advanced Cue Utilisation by Skilled Cricket Batsmen. Australian Journal of
Science and Medicine in Sport , 2-10.
Bradman, D. (1958). The Art of Cricket. Imprint.
Grood, E. S. And Suntay, W.S. (1983). A Joint Coordinate System for the Clinical Description of
Three Dimentional Motions: Applications to the Knee". Transactions of ASME 105 , 136-144.
Muller, S. A. (2006). How Do World-Class Cricket Batsman Anticipate a Bowler's Intention? The
Quarterly Journal of Experimental Psychology, 2162-2186.
Sobers, G.. (1985). Garry Sobers' Way of Cricket. The Five Mile Press.
Wololmer, B. (1993). Skillful Cricket. A&C Black Publishers Ltd.

67
Kinematic Correlates of Lumbar Spine Loading in Fast Bowling
René E.D. Ferdinands, Max Stuelcken, Andy Greene, Peter Sinclair, Richard Smith
Faculty of Health Sciences, University of Sydney
Correspondence: edouard.ferdinands@sydney.edu.au
Fast bowling in cricket requires the bowler to rapidly flex, laterally bend and rotate the lumbar spine
in order to produce ball speeds up to 45 m s-1. This leads to fast bowlers having a high incidence
and prevalence of lower lumbar injury (Orchard, 2006). Bowlers may sustain pars interarticularis
stress fractures and have various abnormalities of the intervertebral discs such as reduced height,
degeneration, bulging and herniation (Elliott et al., 1993; Portus et al., 2004). Values of shoulder
counter-rotation, shoulder-hip separation greater than 30°- 40° have been associated with a higher
incidence of lumbar injury (Elliott, 2000; Portus, 2004). More recently, lateral bending has also
been proposed as a potential lumbar spine risk factor (Portus et al., 2007).
The established method of determining lumbar injury risk in fast bowlers is mostly based on the
measurement of shoulder counter-rotation. However, shoulder counter-rotation is not a causal
mechanism of lumbar injury. By establishing the kinematic correlates of lumbar spine loads and
prospective injury data collection, it may be possible to develop a more accurate assessment of
lumbar injury risk. The identification of these kinematics characteristics may have implications for
the development of safer bowling techniques, particularly with respect to younger bowlers.
Therefore, the purpose of this study was to use three-dimensional motion analysis and inverse
dynamics to investigate the relationships between kinematics and lumbar spine kinetics within an
elite sample of young fast bowlers. The hypothesis is that there are associations between lumbar
loads and kinematic variables, particularly with those variables that have been associated with an
increased incidence of lumbar injury.
Method: Thirteen fast bowlers (17.4 ± 1.9 years) recruited from Cricket New South Wales
development squad were tested in a biomechanics laboratory, which permitted a full length run-up.
A 14-camera Cortex Motion Analysis System (Version 1.0, Motion Analysis Corporation Ltd., USA)
was used to capture three-dimensional (3D) motion (200 Hz) and force plate (1000 Hz) data on six
trials for each bowler, while front and rear foot contact were made on two Kistler force plates. Each
subject was instructed to bowl at maximum effort as in match conditions. Five trials in which the
ball landed within an area demarcated by two white lines 13 m and 19 m from the stumps at the
bowler’s end were selected for analysis. The video capture volume encompassed back foot
contact, front foot contact, ball release and follow through phases of the bowling action. The Cortex
system was calibrated according to the manufacturer’s recommendations resulting in a residual
error of marker position of less than 1 mm.
Motion analysis capture was performed on each subject wearing a full body marker set comprising
forty-five 25 mm spherical markers, which were attached to bony landmarks (Ferdinands, 2009).
Markers were strategically located to define joint centres and local joint coordinate systems that
are anatomically meaningful. The 3D motion analysis data of the markers were imported into a 22
segment rigid body model of the cricket fast bowler in Kintrak (V.7.0, University of Calgary), which
is a software programme designed to calculate kinematic and kinetic variables from motion
analysis and force plate data. The lumbar spine segment (LSS) was defined as a single segment
having its caudal end located half-way between the hip joint centres at the level of L5/S1. The
rostral end was located at the mid-point between the markers on the xiphoid and T10. A recursive
fourth-order low-pass Butterworth filter was used to smooth the data. The cut-off frequencies (8 –
15 Hz) were determined from residual analyses.

68
Kinematic and kinetic data were calculated during the arm acceleration phase defined from the
time of maximum vertical front foot ground reaction force to the time of maximum hand velocity.
Net joint moment was calculated about the caudal end of the lumbar spine segment for lateral
bending, flexion/extension and rotation. Right lateral bending, extension, right rotation and open
stride angle were defined as positive. Posterior-anterior, mediolateral and compressive forces were
defined as positive. Pearson’s correlation coefficients were calculated in SPSS (Version 17, SPSS
Inc.) to assess the relationship between selected kinematic and kinetic variables.
Results: Table 1 shows the Pearson correlation coefficients between the lumbar spine kinetic and
kinematic variables. There were also other kinematic variables that were not significantly correlated
with any kinetics variables: kinematic crunch factor (thoracic lateral bending x pelvic rotation),
stride length, and centre of mass horizontal and vertical velocities at back foot contact. Bowling
hand velocity was not correlated with any of the above kinematic variables. Counter-rotation was
correlated with stride angle (r = -0.57, p = 0.042).
Table 1: Pearson's correlation coefficients between lumbar spine kinetic and kinetic variables
Thoracic and pelvic kinematics were calculated at ball release. (Significance level p < 0.1).
Compressive
Force
Posterior-
anterior
Force
Medio-
lateral
Force
Flexion
Moment
Rotation
Moment
Lateral Bend
Moment
Thoracic
flexion
r = 0.82
p < 0.001
r = 0.52
p = 0.067
Thoracic
rotation
r = 0.57
p = 0.040
r = 0.59
p = 0.033
r = 0.59
p = 0.034
r = -0.83
p < 0.001
Thoracic
lateral bend
r = 0.59
p = 0.035
Pelvic
rotation
r = 0.58
p = 0.037
r = 0.49
p = 0.093
r = 0.112
p = 0.056
r = -0.92
p = 0.001
Counter-
rotation
r = 0.52
p = 0.067
r = 0.61
p = 0.028
Pelvic-
shoulder
separation
r = -0.54
p = 0.053
Front knee
angle
r = 0.82
p < 0.001
r = 0.91
p < 0.001
r = -0.81
p = 0.001
Stride angle r = -0.61
p = 0.028
Hand
velocity
r = 0.50
p = 0.082
Front foot
GRF Z
r = 0.59
p = 0.03
The sample had the following mean kinematic values during the arm acceleration phase: shoulder
counter-rotation (39.3 ± 12.7°), pelvic-shoulder separation angle at back foot contact (-23.1 ± 8.2°),
maximum thoracic lateral bending (-41.5 ± 8.5°), maximum thoracic rotation (-119.2 ± 14.6°),
maximum pelvic rotation (-107.4 ± 13.2°), front knee angle at beginning of acceleration phase (-
17.0 ± 6.5°), stride angle (5.0 ± 5.8°) and bowling hand velocity (23.8 ±1.2 m s-1). Maximum

69
thoracic and pelvic kinematics occurred at the end of the acceleration phase, i.e. at ball release.
Forces and moments were normalised in terms of body weight (BW) and body weight x height (BW
m). The highest ground reaction forces were the vertical components (5.3 ± 0.8 and 2.3 ± 0.4 BW)
at the front foot and back foot. The mean maximum lumbar forces were 8.0 ± 1.2 BW along the
inferior-superior long axis, 1.5 ± 0.6 BW along the posterior-anterior axis and 0.4 ± 0.5 BW along
the lateral-medial axis. The mean maximum lumbar torques were 3.1 ± 0.5 BW m (flexion), 0.9 ±
0.4 BW m (left lateral bending) and 0.2 ± 0.2 BW m (right rotation).
Discussion: The aim of this study was to investigate the relationships between lumbar spine
kinematics and kinetics in a sample of elite young fast bowlers. A relatively young cohort was
selected because this age group has a high incidence of lumbar injury (Portus et al., 2007).
Interestingly, the kinematic variables that were most strongly correlated with lumbar spine kinetics
were the pelvic and thoracic rotations at ball release. These variables were correlated with four
kinetics variables: posterior-anterior force and all three lumbar spine moments. The position of the
thorax in bowling and related motions such as throwing all require the pelvis and thorax to face the
target at the time of release. However, there is no need for these segments to rotate beyond this
as these variables were not correlated with bowling hand speed.
Conversely, the kinematic variables that have previously been associated with an increased
incidence of lumbar injury had fewer associations with lumbar kinetics. Shoulder counter-rotation
was strongly correlated with the lumbar spine rotation moment, but this moment was very small in
magnitude and may not be of clinical significance. In addition, shoulder counter-rotation was
correlated with the medio-lateral force, but this may just result from an unbalanced position during
delivery stride since counter-rotation was also correlated with a closed stride angle. Pelvic
shoulder separation angle was only correlated with the lumbar lateral bending moment. The crunch
factor is used as an index of shear stress loads at the lumbar vertebrae. However, this factor was
not associated with any lumbar spine moments.
The front leg acts as a shock absorber to attenuate the ground reaction forces upon front foot
contact. Knee flexion angle had no effect on the attenuation of the large compressive forces, which
were strongly correlated with vertical ground reaction force acting through the front foot. However,
the front knee flexion angle did have strong correlations with both the lumbar spine rotation and
lateral bending moments and therefore has an important effect on lumbar spine loading.
In general, the data shows that there were a number of kinematic variables associated with lumbar
spinal loads. A combination of these variables may work additively to build up the lumbar spine
loads. For instance, the data suggests that a bowler landing with an extended front knee, large
pelvic-shoulder separation angle and large range of lateral thoracic bending has three factors that
would contribute to the generation of a lateral bending moment. This suggests that a risk of
assessment of lumbar injury in bowling may need to consider multiple variables.
There were several limitations in this study. The segment end points and inertial characteristics of
the lumbar spine segment were based on general anthropometric regression equations, which are
subject to errors that will be propagated to the calculation of spinal loads. There were also a
relatively small number of subjects in this study and so the significance level was increased to p =
0.1.
Conclusion: This study supports the hypothesis that lumbar loading in fast bowling is associated
with kinematic variables. As the causal mechanisms of lumbar injury are ultimately linked to spinal
loading, the identification of kinematic variables associated with such spinal loading can lead to an
improved assessment of lumbar injury risk in young fast bowlers. However, a prospective
longitudinal study is needed to compare a wide range of kinematic and lumbar spine kinetics
variables to assess their predictive ability of lumbar spine injury. The researchers are currently
evaluating the MRI scans taken of all bowlers towards the end of the cricket season to

70
quantitatively assess their injury status. In addition, the data of another five bowlers will be
analysed. This type of research has the potential to yield an accurate multi-index assessment of
lumbar injury risk. This would give coaches the ability to more accurately screen young bowlers for
injury risk and also suggest changes to the kinematics of bowling techniques to reduce lumbar
spine loads.
References
Elliott, B. C., Davis, J. W., Khangure, M. S., Hardcastle, P., & Foster, D., 1993. Disc degeneration
and the young fast bowler in cricket. Clinical Biomechanics, 8(5), 227-234.
Elliott, B.C.(2000).Back injuries and the fast bowler in cricket. Journal of Sports Sciences, 18, 983–
991.
Ferdinands (2009).Three-dimensional lumbar segment kinetics of fast bowling in cricket. Journal of
Biomechanics, 42, 1616-1621.
Orchard, J.W., James, T., & Portus, M.R. (2006). Injuries to elite male cricketers in Australia over a
10-year period. Journal of Science and Medicine in Sport, 9, 459–467.
Portus, M.R. et al., (2004). Technique factors related to ball release speed and trunk injuries in
high performance cricket fast bowlers. Sports Biomechanics, 3(2), 263-284.
Portus, M.R. (2006). Technique mechanisms involved in lumbar spine injuries and ball release
speed in cricket fast bowlers. Unpublished doctoral dissertation, University of Western Australia.
Portus, M.R. et al. (2007). Inter-segment trunk kinematics and lower back injuries in junior and
senior fast bowlers. In 3rd World Congress of Science and Medicine in Cricket (Abstract, p. 42).
Barbados

71
Proprioception and Throwing Accuracy after Exercise
Jonathan Freeston, Roger Adams, Kieron Rooney
Exercise, Health and Performance, The University of Sydney, Sydney
Correspondance: jonathanfreeston@hotmail.com
Introduction: Throwing is a vital skill within many sports, yet carries inherent injury risks. These
risks have been demonstrated to be increased in the presence of fatigue. Shoulder
proprioception, that is, the ability to locate ones arm in space in the absence of vision, has been
linked with injury to the throwing athlete through its role in joint stability. That is, decreased
shoulder proprioception is indicative of decreased joint stability and therefore increased injury risk.
Furthermore, shoulder proprioception is negatively affected by throwing specific activity in
baseball players. Little is known however, regarding the effect of general exercise on shoulder
proprioception. Additionally, the relationship between shoulder proprioception and performance,
specifically throwing accuracy, has yet to be determined in a group of throwing athletes. The aim
of this study therefore, was to compare the effect of general and throwing specific exercise on
throwing accuracy and shoulder proprioception in a group of throwing athletes.
Methods: Participants were required to attend the facility on two occasions separated by a
minimum of seven days. Testing was conducted within an indoor laboratory. Participants were
assessed for shoulder proprioception using a specifically designed apparatus consisting of a disc
protruding from a drive motor on an adjustable mount. Participants were required to stand, with 90
degrees of shoulder abduction and zero degrees of shoulder external rotation. Participants were
required to externally rotate at the shoulder until their hand made contact with the protruding disc.
The disc was randomly placed in one of five different locations, the position of which had to be
determined by the participant without vision. Participants performed 21 familiarisation trials before
performing 50 test trials. Both the throwing and non-throwing arm of each participant was
assessed, the order of which was randomised. Following the shoulder proprioception assessment,
participants performed a general warm-up routine consisting of 5-10 minutes of moderate intensity
running followed by 5-10 minutes of general stretching of the major muscle groups. This was
followed by 10-15 minutes of throwing, which began with light intensity throwing and progressed
to high intensity throwing at completion.
Following the warm up procedure, participants performed a total of twenty throws towards a target
at a distance of 20 m from the participants back foot. The target consisted of a white circle
measuring 3.5 cm in diameter, painted onto a black rubber mat that was suspended on a
specifically designed metal frame. The centre of the target circle was at a height of 0.70 m from
the ground. Using a regulation weight baseball (Details; Wilson 142g), ten (10) throws were
performed at 100% of maximal effort and ten (10) throws were performed at 80% of maximal
throwing speed, with a Cordless Speed Radar Gun (Jugs Corporation, Tualatin, Oregon, USA)
positioned behind the target to ensure that the correct speed was maintained. Throws were
performed in blocks of five throws, the order of which was randomised. A video camera recording
at 50Hz was used to determine the point of ball contact relative to the target. Participants were
permitted one stride forward with the front leg while maintaining the front foot behind the line until
ball release, commonly referred to as throwing from the ‘set position’. This relatively stationary
starting position was employed to minimise the influence of outside factors on throwing
performance, such as approach speed, approach angle and ball pick up.
Following the initial throwing accuracy test, participants were required to complete an exercise
protocol which consisted of either a throwing specific protocol (1) or a running protocol (2) as
outlined below:

72
1. The throwing protocol consisted of 60 near maximal effort throws (>95% MTS as
determined by a cordless radar gun) over a distance of 20 m towards no specific target
with each throw being separated by 10 s. The total time for this protocol was 10 min.
2. The running protocol consisted of one 20 m Shuttle Run Test to exhaustion. The total time
for this protocol was dependant on the fitness level of the individual.
Participants performed one of the exercise protocols during session one and performed the
remaining protocol during session two with a minimum of seven days rest between sessions. The
order that each participant performed the exercise protocols was randomised using
randomization.com. Following the exercise protocol, participants were then re-assessed for
throwing accuracy and shoulder proprioception as outlined above.
Results: Shoulder proprioception increased in the non-dominant arms by a similar magnitude in
response to the two interventions (Fig 1). Proprioception of the dominant arm increased following
run but did not change following throwing specific exercise. The magnitude of change in the
dominant arm was not comparative to the non-dominant arm (Fig 1). Throwing accuracy
decreased following throwing specific exercise (-8.9%) and increased in response to running
exercise (19.0%, Fig 2). This decrease in accuracy was facilitated primarily through changes to
Constant rather than Variable Error, and was observed in the Horizontal rather than Vertical
direction. No correlation was observed between change in proprioception and change in
accuracy.
Proprioception Changes Following Exercise
-2.00%
-1.00%
0.00%
1.00%
2.00%
3.00%
4.00%
Throwing Arm Non-Throwing Arm Throwing Arm Non-Throwing Arm
Throw Run
% Change in Proprioceptive Ability
Figure 1: Proprioception changes following exercise
Discussion: Throwing accuracy and shoulder proprioception of the throwing arm were both
negatively affected by throwing specific exercise. There was however, no relationship between
the magnitude of change in accuracy and the magnitude of change in proprioception. These
findings indicate that although proprioception and accuracy are negatively affected by a throwing
specific stimulus, there is no evidence to suggest that proprioception plays any significant role in
the ability to throw with a high degree of accuracy. Additionally, this finding suggests that although
throwing specific exercise affects accuracy and proprioception in a similar way, the underlying
mechanisms for these changes may be of differing natures.

73
Exercise vs Accurac
y
-20%
-10%
0%
10%
20%
30%
40%
100 80 100 80
Throw Run
Percentage Change in Total Error
Figure 2: Changes in Throwing Error following exercise
Interestingly, the reductions in proprioception and accuracy were not observed following general
exercise. Within the current study an exercise bout of high intensity and a central focus, had nil
negative effects on proprioception or accuracy, whereas an exercise bout of lower intensity and a
peripheral focus reduced accuracy and proprioception significantly. This finding highlights the
importance of the peripheral sensorimotor system in the maintenance of shoulder proprioception
as well as throwing accuracy.
These results suggest that fatigue induced as a result of repeated bouts of throwing is far more
detrimental to injury risk and performance than fatigue induced by general exercise. This has
significant practical implications for overhead throwing athletes, particularly those who engage in
sports that require both general and throwing specific exercise to be performed. These athletes
should focus on building capacity to throwing specific exercise, while limiting exposure to
dangerously high volumes of throwing whenever possible.
Future investigation should explore the mechanisms responsible for the decrements to
proprioception and throwing accuracy observed within this study. Furthermore, investigations
should explore methods to increase the capacity of throwing athletes, to reduce the injury risk and
performance reduction associated with bouts of repeated throwing.
Conclusion: Repeated throwing reduced proprioceptive ability in the throwing shoulder and
reduced throwing accuracy. No relationship was observed however, between change in accuracy
and change in proprioception, suggesting the exercise bout had a similar effect but was mediated
by differing mechanisms. Throwing specific exercise was demonstrated to be more detrimental to
injury risk and performance compared with general exercise.
Acknowledgments: Mr Ray Patton

74
Validity of GPS for Measuring Distance Travelled in Cricket
Adrian Gray1, David Jenkins1, Mark Andrews2, Dennis Taaffe1and Megan Glover1
1 School of Human Movement Studies, The University of Queensland
2 Queensland Academy of Sport
Correspondence: adriangray@uq.edu.au
GPS technology is now widely used to monitor the training and competition demands in several
sports, including cricket (Petersen, Pyne, Dawson, Portus & Kellet, 2010). However, relatively few
studies have assessed the validity of GPS distance measurement under sport-specific conditions
(Petersen, Pyne, Portus & Dawson, 2009). Furthermore, the influence of varied movement
demands in field sports (from standing to sprinting with varying degrees of direction changes) on
GPS distance accuracy is yet to be determined. The present study determined the effects of
movement intensity and path linearity on the validity of estimates of distance derived by GPS.
One participant wore eight 1 Hz GPS receivers (WI SPI, GPSports, Canberra, ACT) while walking,
jogging, running and sprinting over linear and non-linear (Figure 1) 200 m courses. Five trials were
completed at each movement intensity on each 200 m course. GPS distance was compared with
actual distance using the Bland-Altman method (Bland & Altman, 1986). In addition, the same eight
receivers were each placed on a permanent geodetic mark where they remained stationary for 60
minutes. During this time, cumulative distance travelled (according to the receivers) was recorded
over 15 s, 30 s, 60 s and 120 s time periods (n=100, each).
Figure 1: Non-linear course as it was marked on a flat grass field.
The results showed the mean ± SD and percent bias of the GPS distance values on the 200 m
linear course were 205.8 ± 2.4 m (2.8%), 201.8 ± 2.8 m (0.8%), 203.1 ± 2.2 m (1.5%) and 205.2 ±
4 m (2.5%) for the walk, jog, run and sprint trials respectively (Figure 2, ● circles). Walk and sprint

75
distances were significantly different from jogging and running distances (p<0.05). GPS distance
values from the 200 m non-linear course were 198.9 ± 3.5 m (-0.5%), 188.3 ± 2 m (-5.8%), 184.6 ±
2.9 m (-7.7%) and 180.4 ± 5.7 m (-9.8%) for the walk, jog, run and sprint trials respectively (Figure
2, ■ squares); these were significantly lower than values for corresponding speeds on the linear
course (p<0.05). Differences were significant between all non-linear movement intensities
(p<0.05). Despite remaining stationary, the GPS receivers reported travelling mean distances of
2.7 m, 5.6 m, 10.9 m & 21.7 m during 15 s, 30 s, 60 s and 120 s periods of data collection,
respectively.
Figure 2: Plot of mean GPS distance error for each movement intensity during linear (●, circles)
and non-linear (■, squares) locomotion. Error bars represent the 95 % Limits of Agreement.
The slight but consistent overestimation of distance travelled over the straight path may reflect an
accumulation of position errors (the result of inherent system errors). This difference has been
observed in previous research and is the most likely cause of distance accumulation during
stationary periods. The increasing degree of underestimation with movement intensity on the non-
linear path may be the result of an insufficient update rate and leaning associated with bend
running. GPS distance measurement in curvilinear movement can be described as the sum of
measured chords between position estimates. Theoretically, as the sampling rate increases, the
path delineated by the chords approaches the actual curve. Thus, an increased update rate of the
GPS devices may be of benefit. Additionally, when leaning was accounted for in the running and
sprinting trials, bias reduced by more than half to -4.3 m and -8.5 m respectively. This observation
indicates the receivers can differentiate between the path of the feet and the upper back and that
lean angle may account for a significant proportion of the underestimation reported.
Arguably much of the distance covered by players in cricket is linear in nature; bowlers approach
the wicket in a straight path, batsman run direct paths and fielders would typically run the shortest
route to intercept the ball. Further, low intensity activities (standing, walking and jogging) make up
much of the time spent/distance travelled during competition (Duffield & Drinkwater, 2008). Given
this, the present findings suggest 1 Hz GPS receivers would tend to overestimate the total distance
travelled by players during competition and game specific training. Notably, where speed zones
are used, the accumulation of distance during stationary periods will only skew the lowest zone.
Further, this phenomenon suggests the magnitude of distance error will increase with duration of
play and it seems unlikely that the brief periods of non-linear movement in cricket (which typically
underestimate the distance) would off-set this tendency.

76
The present findings are specific to the GPS receivers evaluated and their associated algorithms.
Nonetheless it is recommended that coaches and support staff consider the movement demands
of the activity being monitored when interpreting GPS data.
References
Bland, J. M., & Altman, D. G. (1986). Statistical methods for assessing agreement between two
methods of clinical measurement. Lancet, 327(8476), 307-310.
Duffield, R., & Drinkwater, E. (2008). Time–motion analysis of test and one-day international
cricket centuries. J Sport Sci, 26(5), 457-464.
Petersen, C.J., Pyne, D., Dawson, B., Portus, M. & Kellet, A. (2010). Movement patterns in cricket
vary by both position and game format. J Sport Sci, 28(1), 45-52.
Petersen, C. J., Pyne, D. B., Portus, M. R., & Dawson, B. (2009). Validity and reliability of GPS
units to monitor Cricket-Specific Movement Patterns. Int J Sport Phys and Perf, 4(3), 381-393.

77
Multidisciplinary and Multivariate Approaches to Problem
Solving in Exercise and Sport Science
Ian Heazlewood
School of Environmental and Life Sciences, Charles Darwin University, Darwin
Correspondence: ian.heazlewood@cdu.edu.au
Many researchers in the movement, sport and exercise sciences are aware that the human
organism and the variability between humans are multivariate constructs. Authors of various texts
for conducting research in human movement, sport and exercise sciences invariably recognise that
the discipline is complex and advocate that the discipline is composed of many parts that are
multidisciplinary and multivariate in nature (Abernethy et al., 1996; Baumgartner & Hensley, 2006;
Thomas et al., 2005). This paper focuses on the application of multivariate statistical approaches to
analysing and understanding data sets that can be generated in human movement, sport and
exercise research. According to Abernethy et al., (1996) the discipline can be divided into
biophysical and sociocultural foundation studies. The biophysical foundations include the traditional
subdisciplines of functional anatomy, biomechanics, exercise physiology, motor control/learning,
sport and exercise psychology. The inclusion of research design, research method and statistics
enhances the interpretation of data collected from the various subdisciplines.
The subdisciplines reflect areas of human endeavour focused on specific characteristics of the
human organism. Researchers in the movement, sport and exercise sciences understand that
these constructs are operating and integrated within the human organism and expressed as both
human function and behaviour. More advanced texts on coaching reflect many levels of
organisation, such as subdisciplines that operate when coaches are attempting to enhance motor
behaviour (Martens, 2004). However, specific coaching guidance to integrate these constructs that
should optimise training is not necessarily forthcoming. An identical situation exists with texts that
contain research methods to measure constructs related to movement (Abernethy et al., 1996;
Baumgartner, Strong & Hensley, 2002; Baumgartner & Hensley, 2006; Thomas et al., 2005).
These texts are partistic in the methods of measurement; however they often do not indicate
clearly how the disparate pieces of information can be integrated into the whole human organism,
which is the holistic approach to describing and predicting sports and exercise performance.
Abernethy et al. (1996, p.8) stated in the mid-1990’s “...there is increasing concern in many
quarters that the growing differentiation and specialisation within human movement studies may
produce fragmentation and an inevitable loss of integrity within the discipline base.” This appraisal
is still appropriate in 2010 of ‘the current state of play’ in sport and exercise science. Abernethy et
al (1996) contend further that “...human movement studies is probably most accurately depicted as
being currently multidisciplinary, whereas the desirable direction is to make it more cross-
disciplinary and ultimately interdisciplinary.” The intention of this paper is to examine how effective
the cross-discipline and interdisciplinary approaches have been in providing insights into
understanding and solve movement-related problems.
A coach training an athlete can induce physiological changes within the athlete at many levels
simultaneously, such as enzyme concentrations, muscle recruitment patterns, blood volume, heart
rate at submaximal loads, muscle cell (fibre) size, fat cell size, maximal aerobic power, and
anaerobic power production. These physical adaptations together with improvements in
psychological and skill attributes can result in, and be expressed as, enhanced motor performance.
The changes might be identified as biomechanical, exercise physiological, sport psychological,
motor learning/control or functionally anatomical in nature. In coaching, as in human movement
research, many variables or constructs are often required to describe and understand the outcome
of an experiment or training effect. “When observed variables are interrelated, as is usually the

78
case, statistical analyses that treat the variables one at a time”, or in isolation, “may ignore much
useful information” (Norusis, 1985, p.1), as well as the many complex and revealing relationships
between the independent variables in an experiment. According to Biddle et al., (2001) multiple
measures allow for a more complete description of the investigated phenomena, maximizing the
information obtained. Multivariate statistical methods have been developed to address research
problems where several interrelated variables or constructs, which can be based on
interdisciplinary constructs, might operate and produce motor behaviour (Norusis, 1985). A number
of common multivariate statistical methods (Hair et al., 2006; Norusis, 1985; Thomas et al., 2005)
can be applied to multivariate and interdisciplinary research
Multiple linear regression is a method that develops a mathematical equation to summarise the
relationship between a continuous dependent variable and a set of continuous independent
variables. It identifies the most useful subset of variables to predict the dependent variable and
predicts values of the dependent variable based on values of the independent variables. A
predictive model applying this approach from the sport of triathlon illustrates that an
interdisciplinary set of predictor variables can be reduced to a subset of significant variables that
explains the majority of competitive performance (Heazlewood & Burke, 1996). In the study, 88
participants (male=79; females=9) were tested prior to competing in the Ironman Triathlon, using
exercise physiological (VO2max, adiposity (skinfolds), height and weight), biomechanical (peak and
relative values for torque, work and power at isokinetic leg extension/flexion at 600, 1800 and 3000
s-1) and sport psychological (self-efficacy, sport confidence, state anxiety, and cognitive and
somatic anxiety) constructs to predict total triathlon time and the three competition stages (swim,
cycle and run). Block method enter and stepwise regression indicated self-efficacy statements
were the most predictive whereas with the block method VO2max, state confidence, state anxiety,
adiposity (skinfold scores) self-efficacy total time were significant predictors. However, based on
the stepwise method, when self-efficacy was included using statistical criteria, the contribution of
the other variables to the explained variance was statistically nonsignificant. The summary
regression equations indicate how the different variables from the different disciplines fit into a
multivariate model, which explained 71% and 69% of the variance between the predictors and total
triathlon time.
Block Method Equation
Total Time = -.833(VO2max) - .471(State Confidence) - .271(STAI) - 1.467(Skin Fold %) +
.792(Self Efficacy Total) + 292.09
R=0.842, R2=0.709, p<0.0001
Stepwise Method Equation
Total Time = .792(Self Efficacy Total) + 142.83
R=0.831, R2=0.690, p<0.0001
Factor Analysis is a method to identify underlying constructs that explain correlations among a set
of measured variables, to test the structure of the variables and to reduce a large number of
measured variables to meaningful factors (Norusis, 1985; Hair et al., 2006). For example, many
variables can be measured in a motor fitness test such as strength, torque, power, speed, agility,
endurance, range of motion (ROM) and so on. However, how many different factors of fitness are
these variables actually measuring? The following study methodology illustrates this technique.
Sixty healthy, physically fit, active males (N=32) and females (N=28) participated in the study.
Maximal performance on the experimental dependent variables of isokinetic torque, work, power,
isokinetic fatigue, plyometric power and sprint acceleration were assessed. The CYBEX 340
isokinetic muscle evaluation system was used at speeds of 60, 180 and 300os-1 using leg
extension/flexion to assess torque, muscular work and fatigue index. Acceleration at 10m, leg
power indices of contact time, flight time and height were assessed with the Speed Light Sports
Timing System using jump mode. The most interpretable solution was a three significant factor
model derived by principal component analysis using the eigenvalues greater than 1 followed by

79
varimax rotation. It explained 74.1% of common variance in the data set. Factor 1 loaded
significantly with peak torque and total work across the three isokinetic speeds (loadings .803 to
.919). Factor 2 loaded significantly with jump height, 10m acceleration and flight time (loadings
.698 to .861). Factor 3 loaded significantly with isokinetic fatigue index across the different
isokinetic speeds (loadings .782 to .829). The only variable that displayed factor complexity was
vertical jump contact time, which loaded across factors 1 and 2. The three factor solution identified
factor 1 as isokinetic peak torque and total work ability, factor 2 as plyometric power/acceleration
ability and factor 3 as isokinetic fatigue ability. These findings indicate that many measures of
strength, work, power, speed and isokinetic fatigue index, although measured using different
protocols, can be redundant. It follows that the concept of measurement parsimony should be
evaluated in study designs, which can lead to far greater measurement efficiency for sport science
researchers.
Discriminant analysis is a method to classify one of severally mutually exclusive groups on the
basis of various continuous data. The procedure identifies which characteristics are important to
distinguish groups and to evaluate how accurately the statistical model can distinguish the groups.
The primary objective of discriminant function analysis is to maximise the between group
differences (explained variance), based on the available data. It can address questions such as
what constructs of fitness best distinguish males from females in athletic performance, or, players
in team sports that have been assigned to different grades by selectors. In the sport of cricket
motor fitness and kinanthropometric characteristics related to player position of under 19 high
performance cricketers was addressed by the application of discriminant analysis. NSWCA under
19 pre-season squads undertook fitness tests where forty-two male players (n=42) were tested
over a two year period. The players completed the following physical fitness and kinanthropometric
assessments: 1. Body composition, height, weight and skinfolds (8 sites), 2. Endurance task of
predicted VO2 max test based on 20 metre shuttle run (multi-stage fitness test), 3. Muscular
Performance in the form of seven stage sit-up test, vertical jump, sit and reach. 4. Speed and
agility tasks of 6 x 40 metre sprints decrement test, run a three and turning time.
Those players recognised as batters (n=23) and fast bowlers (n=7) were grouped by these skills
and were included in two discriminant analyses. The first analysis was the blocked variable
method, where all the significant variables in the initial ANOVA solution were included. The second
analysis was the Wilks’ stepwise method, where a subset of significant variables was statistically
derived. The other players such as wicket-keepers, all-rounders and slow/spin bowlers were not
categorised in the initial analysis, however they were classified by the statistical model
(discriminant analysis) to assess which group the slow/spin bowlers, wicket-keepers and all-
rounders had the greatest similarity. This was to answer the research question whether these other
players were more similar to the batters or fast bowlers? This multivariate discriminant analysis
approach derived a highly significant discriminant function (canonical correlation =.791, R2=.625
explained variance, p=.037) with the between group difference between batters and fast bowlers
attributed to height, run a three, weight, turning speed and predicted VO2 max., ordered from the
highest to the lowest significant construct. Using the derived discriminant function scores, the
batters could be classified correctly into their correct group at a rate of 95.7% (22 out of 23) and
the fast bowlers into their correct group at a rate of 71.4% (5 out of 7). The total classification rate
for both groups combined was 90.32%. All classification rates were highly significant and it must be
emphasised that a perfect classification rate is 100% for discriminant analysis. The other player
positions (wicket-keepers, slow bowlers and all-rounders) were more similar to the fast bowlers (6
out of 8 classified as fast bowlers) based on their kinanthropometric and motor fitness
characteristics.
The results indicated that the NSWCA U19 batters and fast bowlers from the 2 year period could
be differentiated into their respective player positions predominantly on the basis of their
kinanthropometric and motor fitness measures. These were height, weight, sports specific agility

80
and predicted VO2 max. These types of results may prove to be beneficial for talent identification
and development.
Multivariate Analysis of Variance, abbreviated as MANOVA, is a method to test hypotheses
concerning a set of interrelated dependent variables and one or more grouping variables. A
researcher may design a resistance training program that he/she believes will enhance strength,
power, work output and speed of movement in both adult males and females. In MANVOA, the
collective multivariate outcomes can be analysed as compared to one variable at a time, the
univariate approach. Training in reality has many effects within the athlete and frequently
researchers focus on a limited univariate set of variables, while ignoring the more complex
outcomes. Statistically the MANOVA takes precedence over the ANOVA approach.
Path Analysis and Structural Equation Modelling methods attempt to reveal the complex
relationships between a set of independent and a set of dependent variables. For example, what is
the effect of strength, power, work output and aerobic power (independent variables) on sprint
acceleration, maximum running speed and speed endurance (dependent variables)? Structural
equation modelling can be applied to the variables of isokinetic torque, isokinetic work, power and
acceleration to investigate the structural relationship that isokinetic torque-isokinetic work and
power-acceleration have as discrete constructs.
Neural Networks are the preferred tool for many predictive data mining applications because of
their power, flexibility, and ease of use. Predictive neural networks are very useful in applications
where the underlying processes or interactions between constructs are complex (SPSS Neural
Networks™ 17.0, 2009). Neural networks utilised in predictive applications, such as the multilayer
perceptron (MLP) and radial basis function (RBF) networks, are supervised in the sense that the
model-predicted results can be compared against known values of the target variables, such as a
pre-existing group, injury state or performance outcome. Thus, a neural network can approximate a
wide range of statistical models without requiring a hypothesis in advance regarding certain
relationships between the dependent and independent variables. Applications to determining ability
levels (classification) in the sport of karate indicated that ability level, black belts compared to non
black belts, could be accurately predicted to 100% using MLP neural networks. These results were
based on a subset of general motor fitness and karate specific motor fitness tests of power, speed,
agility strength, ROM, peak aerobic power and reaction times (Heazlewood & Keshishian, 2010).
Conclusion: The sport of cricket is a multidisciplinary pursuit, requiring a wide range of capacities
such as mental skills, motor skills and motor fitness. It is a sport that lends itself to well planned
multivariate research designs using many of the statistical techniques discussed.
References
Abernethy, B., Kippers, V., Mackinnon, L.T., & Hanrahan, S. (1996). The biophysical foundations of
human movement. Melbourne: MacMillan Education Australia.
Baumgartner, T., Strong, C. & Hensley, L. (2002). Conducting and reading research in health and
human performance. (3rd ed.). Sydney: McGraw Hill.
Baumgartner, T. A., and Hensley, L. D. (2006). Conducting and reading research in health and
human performance. (4th ed.). Sydney: McGraw Hill.
Biddle, S., Markland, D., Gilbourne, D., Chatzisarantis, N. & Sparkes, A. (2001). Research
methods in sport and exercise psychology: Quantitative and qualitative issues. Journal of sports
Sciences. 19, 777-809.
Hair, J. F., Black, W., C., Babin, B. J., Anderson, R. E., and Tatham, R. L. (2006). Multivariate
data analysis. (6th ed.). Upper Saddle River: Pearson-Prentice Hall.

81
Heazlewood, I. & Burke. S. (1996). Exercise physiological, sport psychological and biomechanical
predictors of the triathlon. Conference Proceedings the 20th Biennial National, International
ACHPER Conference. 14-19 January, 1996, 114-117.
Heazlewood, I. & Keshishian, H. (2010). A comparison of classification accuracy for karate ability
using neural networks and discriminant function analysis based on physiological and
biomechanical measures of karate athletes. Conference Proceedings Australasian Conference on
Mathematics and Computers in Sport, Crowne Plaza, Darwin, Australia. 5-7 July, 2010. In Press.
Martens, R. (2004). Successful coaching (3rd ed.). Lower Mitcham: Human Kinetics.
Norusis, M. (1985). Advanced statistics guide: SPSSX. Sydney: McGraw-Hill Book Company.
SPSS Inc. (2009). SPSS neural networks™ 17.0. Chicago: SPSS Inc.
Thomas, J. R., Nelson, J. K., and Silverman, S. J. (2005). Research methods in physical activity.
(5th ed.). Lower Mitcham: Human Kinetics.

82
Development and Implementation of a Simulated Cricket Batting Innings
for Testing and Training
Laurence Houghton1, Brian Dawson1, Jonas Rubenson1 and Martin Tobin2
1 School of Sport Science, Exercise and Health, The University of Western Australia
2 University Cricket Club, Western Australia.
Correspondence: houghl02@student.uwa.edu.au
There have been two recent time-motion analysis studies of batting (Duffield & Drinkwater, 2008;
Petersen et al., 2010). Duffield & Drinkwater (2008) reported a work: recovery time ratio of high
(running, striding, sprinting) to low (stationary, walking, jogging) intensity activity as 1:47 s and 1:67
s for one-day and multi-day cricket respectively. Petersen et al. (2010) collected global positioning
system (GPS) data from first class, Twenty20 (T20), one-day and multi-day cricket games, finding
similar ratios of high to low intensity activity to Duffield and Drinkwater (2008) (1:50 and 1:61, for
one-day and test match hundreds, respectively).
There is a need to develop a valid and reliable cricket batting simulation that can be used to
progress scientific research into the unique demands of prolonged batting in cricket (Duffield &
Drinkwater, 2008; Petersen et al., 2010). Due to the unpredictable length of a batting innings and
the limitations of data collection during a match, a Batting Exercise (BATEX) simulation has been
developed.
Firstly, the purpose of this paper is to document the rationale for the development of BATEX, and
secondly, to compare initial GPS data collected during BATEX with movement pattern data from
match play (Duffield & Drinkwater, 2008; Petersen et al., 2010).
BATEX requires running of 1, 2, 3 and ‘4’ (match equivalent to running 1 run, turning and running a
½ run, before the ball has crossed the boundary) 17.68 m shuttle runs during six, 21 min, five-over
stages. Decision making scenarios are associated with each 21 min stage. Balls may be delivered
by a bowling machine or thrown. On completion of 1 or 3 runs the batsman is required to walk back
to the stumps to be ready to face the next delivery. An audible CD track is used to inform the
batsman of the running demands for the proceeding 6 balls. The 6 stages, with rest periods, link
together to form a 2 h 20 min simulated innings – the length of a typical One Day International
(ODI) hundred (Duffield & Drinkwater, 2008). For training purposes, stages may be used
independently or in any chosen combination.
The physical demands during each BATEX stage are based on typical running demands
ascertained from descriptive statistics of team innings during the 2007 (n = 20) and 2009 (n = 20)
T20 World Cups; the 2003 (n = 24) and 2007 (n = 24) ODI international World Cups and the home
and away Test match series between Australia and South Africa (2008/2009, n = 21). All statistics
were collated from CricInfo (available for public access at: http://static.cricinfo.com/db/ARCHIVE/
accessed 24/04/09). In order to reflect different stages of a ODI innings, data were also collated for
the maximum distribution of run frequency, individual innings of batsmen 1 to 4 and when
individuals scored 50 to 100 runs.
Table 2 presents the running patterns for each BATEX stage. Although each stage is based on a
different format/phase of the game, overall, the workload is designed to reflect the mean run
distribution of a ODI. To promote a better decision making environment, a match scenario has
been integrated with each BATEX stage (Table 2). Each decision making scenario is based on
theoretical phases of play that occur during a match (MT).

83
Table 2: The number of runs required during each 21 min (5 over) Batting Exercise (BATEX)
simulation scenario. Movement requirements are as if batting in a partnership and therefore
incorporates the runs made by both batsmen. Shaded scenarios are completed at maximal effort
whereas unshaded scenarios are completed at a ‘self-selected cruise’ pace. See in text for running
requirements for a ‘4’. ODI – One day international (50 over); T20 – International 20 over cricket;
WC – World Cup.
Stage of innings Statistical basis 1s 2s 3s ‘4s’
Building momentum
Establishment of the batsman’s
innings and the first partnership
(low risk)
ODI WC 2003/2007, mean
(×2) of batsmen 1-4, n =
191
6 1 0 2
Taking initiative
Acceleration (medium risk)
ODI WC 2003/2007, mean
(×2) of batsmen scoring
between 50 and 100 runs,
n = 58
10 2 0 2
Fighting back
Re-establishment after loss of
wicket/s (low risk)
Test match series
2008/2009, overall innings
mean, n = 21
3 1 1 2
Continuous acceleration
Powerplays, T20 cricket (high risk)
T20 WC 2007/2009,
overall innings mean, n =
20
10 2 1 3
Maintaining tempo
Conservation (low risk)
ODI WC 2003/2007,
overall innings mean, n =
48
9 2 1 2
Closing out the game/innings
‘Finishing’ (high risk) ODI WC 2003/2007,
overall innings maximum,
n = 48
11 3 1 4
Overall BATEX 49 11 4 15
In order to ascertain whether the movement demands of BATEX reflects the movement demands
of one-day cricket, GPS data were collected. Six healthy, 2nd-4th grade players were recruited
(mean ± SD: Age: 21 ± 3 years; height: 179 ± 6 cm; weight: 81.0 ± 9.3 kg; all batted in positions 1
to 6). Ethical clearance for this study was granted by the university’s human research ethics
committee. All participants provided informed, written consent. All testing was conducted at an
outdoor, synthetic net facility. Each volunteer wore a 1 Hz GPS unit (GPSports, SPI 10) and heart
rate belt (Polar Team System™). Participants wore full protective batting gear (leg pads, helmet,
gloves). All deliveries during BATEX were thrown by the investigator. Water was freely available
during the trial. Gatorade (300 ml) and marshmallows (25 g) were provided after stage 3 of
BATEX. Environmental conditions averaged 25 ± 1°C (20.2 - 31.8 °C. QUESTemp° 32 Thermal
Environment Monitor, Quest technologies).
Overall BATEX heart rate (HR) averaged 131 ± 8 bpm, which is slightly lower than that collected
during one day games (139 - 154 bpm) (Nicholson, Cooke, O'Hara, & Schonfeld, 2009; Petersen

84
et al., 2010). However, average HR in each BATEX stage varied from 113 ± 3 bpm in stage 1, to
149 ± 13 bpm in stage 6. During BATEX 94% of time was spent walking and standing which
concurs with time-motion analysis of ODI hundreds (Duffield & Drinkwater, 2008). BATEX GPS
data were compared to that collected by Petersen et al. (2010) and is presented in Table 2.
Overall, the ratio of high: low intensity activity in BATEX was 1:30, although this ranged from 1:95
to 1:16 in stages 1 and 6 respectively. The average distance covered during BATEX was 5096 ±
38 m.
Table 3: Comparison of movement patterns of the Batting Exercise (BATEX) simulation with that of
different formats of the game from Petersen et al. (2010). To allow comparison with the published
data the collected BATEX data have been presented as metres per hour and the same movement
categories have been used. Effect sizes (using average standard deviation) have been used to
compare movement demands of BATEX with different formats of the game. Data are means ± SD.
Walking Jogging Running Striding Sprinting Overall
Speed
(kph) 0-7.2 7.2-12.6 12.6-14.4 14.4-18 > 18
BATEX
(m.h-1) 1355 ± 171 231 ± 36 99 ± 8 220 ± 28 265 ± 68 2170 ± 165
One-day
batting
(m.h-1)
1808 ± 400b 279 ± 119a 86 ± 37 154 ± 70b 149 ± 94b 2476 ± 63a
T20
batting
(m.h-1)
1638 ± 352a 332 ± 103b 97 ± 35 187 ± 70a 175 ± 97b 2429 ± 606a
Multi-day
batting
(m.h-1)
1604 ± 438a 200 ± 90 67 ± 18c 107 ± 33c 86 ± 28c 2064 ± 630
a moderate effect size (0.6-1.2); b large effect size (1.2-2.0); c very large effect size ( > 2.0). All effect sizes
are in comparison to the movement demands in the BATEX simulation.
The BATEX simulation has the foundation of a similar running between the wicket distribution as a
typical ODI innings, yet distance covered per hour was lower in BATEX when compared to one-day
batting. This was a result of less distance covered walking (Table 2). However, the need for full
pace running during stages 2, 4 and 6 resulted in BATEX demanding more high intensity running
than during a typical one-day innings. Moreover, overall, BATEX provided less recovery time (1:30)
when compared to one-day batting (1:50) (Petersen et al., 2010). The higher physical demands of
BATEX suggest that the simulation may be suitable for inducing training overload during
preseason. Specifically, BATEX may aid developing players who do not have the experience of
combining the skill, concentration and fitness demands required for a prolonged batting innings.
There was considerable variation in the physical demands of each BATEX stage. The higher
intensity stages (2, 4 and 6) may be used in isolation as a time-efficient, fitness and skills drill.
BATEX can be used to increase the specificity of net training whilst providing physical conditioning
that is often neglected during a cricket season. Further, provided that physiological measures
during BATEX are found reliable, the simulation is to be used as a tool for future research into the
physiological demands of batting and the effectiveness of strength and conditioning programs.
Acknowledgements: Thanks to miSport Ltd. for the loan of their PitchVision™ system throughout
this research project. This research was carried out whilst the author was in receipt of a University
International Postgraduate Awards International Student (UAPIS) and an Endeavour International
Postgraduate Research Scholarship (IPRS).

85
References
Duffield, R., & Drinkwater, E. (2008). Time-motion analysis of test and one-day international cricket
centuries. Journal of Sports Sciences, 26(5), 457-464.
Nicholson, G., Cooke, C., O'Hara, J., & Schonfeld, A. (2009). Heart rate of first-class cricket
batsmen during competitive 50-over and 20-over match play. Journal of Sports Sciences, 27(S2),
S98.
Petersen, C., Pyne, D., Dawson, B., Portus, M., & Kellett, A. (2010). Movement patterns in cricket
vary by both position and game format. Journal of Sports Sciences, 28(1), 45-52.

86
Strength Training for Fast Bowlers: Resistance to Resistance Training
Stuart Karppinen
Australian Team Strength & Conditioning Coach, Cricket Australia, Melbourne
Correspondence: stuart.karppinen@cricket.com.au
The fast bowling action is explosive in nature; whereby a large amount of force must be generated
over a very short period of time. A fast bowler must also overcome large ground reaction forces
generated at delivery, added to the equation is that this maximal effort must be repeated over long
periods of time. In extreme circumstances this action may need to be repeated as frequently as
150 occasions over the course of a day’s play.
Not surprisingly, fast bowlers have consistently been identified as the category of cricket players at
the greatest risk of injury (Orchard et al., 2008). Recently modern training techniques, and in
particularly strength training, has been perceived to be a major contributing factor to the recent
injuries sustained at a national level. It could be argued that most of these allegations lack
fundamental understanding of the current approach taken to strength training for fast bowlers.
However the scarce amount of scientific evidence, in the form of research into strength training and
its effects upon the fast bowling population, make it hard for right of reply. Perhaps a further
contributing factor to strength trainings recent negative image has been the application of non-
specific strength programs. These were typically hypertrophy focused and required the athlete to
perform a high volume of repetitions at moderate intensity, more often than not for prolonged
periods. This approach of generating high force at low velocity was indicative of the training
approach from previous eras, and is vastly different to the approaches taken today.
From a technical view point the bowling action is a highly skilled activity, which is acquired over
years of fine tuning. Equally from a neuro-muscular perspective the bowling action is a complex
activity; optimal performance is a result of highly tuned intramuscular and intramuscular
coordination, which is governed by the central nervous system. The application of appropriate
resistance training programs can induce adaptive alterations in nervous system function, along
with changes in the structure and architecture of the trained muscles (Zatsiorski et al., 2006), which
ultimately help to improve performance.
In particular, neural adaptation mechanisms play important roles for the training induced increase
in maximal eccentric strength and contractile rate of force development (RFD); importantly both
properties are evident in the specific demands of the fast bowling action. This adaptation response
in some circles is often misunderstood, where by increasing mass is thought to be the sole means
in which strength and power is improved. Whilst there is enough research to suggest that this is
applicable for many sports, greater investigation in to the specific demands of fast bowling highlight
the fact that this previous approach is not necessarily appropriate. It is possible to design
resistance training programs aimed at maximizing neural components that will induce gains in
muscle strength with no or only minor increases in muscle and body mass, an important
consideration for all strength & conditioning professionals , physiotherapists and coaches alike.
Development of appropriate strength programs require understanding of the what type of forces
the athlete must overcome, how is force developed & used and how much time is available to
apply it. For the fast bowler there are two distinctly different forces to deal with, overcoming their
own body mass in different states of performance and how they impart maximal velocity on a
cricket ball weighing between 142-156gm.
During the run-up phase, where some bowlers can reach up to 95% of maximum running velocity
(Phillips et al., 2010), vertical forces are typically three times body weight. Research indicates that

87
a fast bowler potentially has to deal with impacts upon delivery between 5-7 times’ body weight, in
the form of both vertical and braking forces (Phillips et al., 2010).
Investigation of fast bowling biomechanics highlights the proposed significance of the lower body’s
contribution to ball release; recent research (Phillips et al., 2010) showed significant contributions
by the lower extremities in the way in which bowling speeds were generated. A study in 2006
(Pyne et al., 2006) highlighted the fact that the higher velocity bowlers had greater lower body
strength levels. A review with other sports involving overhead throwing actions and the relevant
contributions of the lower body also highlights some important implications.
When compared to other sporting activities involving projectile implements, fast bowling displays
similar proximal-to-distal firing patterns (Grimshaw et al., 2006) exhibited in Javelin and Baseball
pitching, where by the largest body parts actively accelerate and decelerate smaller body parts.
Both anecdotal and research evidence (Bartlett et al., 1998; Bartonietz, 2000; MacWilliams et al.,
1998; Matsuo et al., 2001) suggests that a proximal-to-distal firing pattern is the most effective
method of increasing the velocity at release. In such a sequencing pattern, the stronger more
heavily muscled proximal joints should become active before the weaker but faster distal joints
(Gambetta, 2007).
The performance for this movement is of very short duration, with the action completed from back
foot contact to ball release generally range from 0.20 to 0.40 (Phillips et al., 2010) of a second.
While the external resistance that the lower body must cope with at the point of impact is quite
high, the relative resistance that the upper body must overcome is very low.
The combination of low external resistance at high velocity requires thought to be given to the
training modality for the upper extremities, as its mechanics is very specific in how force is
generated.
It takes time to develop maximum force for a given motion, time to peak force varies for individual
and type of action , but on average, time to peak force is 0.40 of a second (Phillips et al., 2010).
The relative contributions during the fast bowling action of the upper extremities allow about 0.15 -
0.18 of a second for force to be generated, as a result training maximum strength development in
the upper extremities may be pointless as the fast bowler may not have sufficient time to reach
maximum force capability.
Adding to the factors associated with force development of the upper extremities involve the
biomechanics of the action it’s self, that of a long pull ,with the arm extended as a long lever
beyond the horizontal. Whist the implement is relatively light the inertia that results from a long
lever moving at high velocities is in excess of 2000 degrees per second (Wixted et al., 2010), this
is important when considering exercise prescription. Ballistic and explosive type training aimed at
removing the deceleration phase of traditional resistance training exercises can increase maximum
rate of force development, whereas slow, heavy resistance training may even decrease maximum
rate of force development (Zatsiorski et al., 2006).
Ideally strength training programs for fast bowlers should adopt a long term athlete development
approach (LTAD), where in the formative years there is a general training approach with a focus on
technique and achieving physical competency. As the athlete progresses training should begin to
be more specific in nature, and progress from injury prevention to performance improvement.
Outlined during this presentation will be an example of how the following strength systems are
applied to the international level fast bowler, and how the process of technical, tactical and physical
development is integrated by the Strength & Conditioning coach, Fast Bowling coach and the
individual athlete.

88
Training systems for strength:
1. Eccentric loading.
2. High load speed strength.
3. Low load speed strength.
4. Ballistic training.
5. Plyometrics training.
Conclusion: Whilst there has been recent negativity surrounding strength training for fast bowlers
this perception has been because of a lack of understanding of what current methods are being
used. A common misconception is that strength training means making athletes bigger. When a
sport-specific demands analysis is completed correctly, strength training programs can greatly
assist fast bowling performance. In fact, with the modern international schedule, its absolutely
necessary to maintain sustained high performance.
Acknowledgements: Many thanks to Dr Mike McGuigan from the New Zealand Academy of Sport,
Dr Marc Portus for his assistance with this paper, as well as the numerous members of the fast
bowling technical panel for their collective thoughts and ideas over the past 4 years.
References
Pyne,D.B., Duthie,G.M., Saunders,P.U., Petersen,C.A., & Portus,M.R. Anthropometric and
strength correlates of fast bowling speed in junior and senior cricketers. Strength Cond Res.
20(3):620-6.2006.
Bartlett, L.R., Storey, M.D., & Simmons, B.B. Measurement of Upper Extremity Torque Production
and its relationship to Throwing Speed in the Competitive Athlete. American Journal of Sport
Medicine.17: 89-96, 1998.
Bartonietz, K., Javelin Throwing: An approach to performance development. In Zatsiorsky, V. (Ed),
Biomechanics in Sport. London: Blackwell science LTD. Pp.401-434, 2000.
Gambetta, V. Athletic Development; The Art & Science of Functional sports conditioning. Human
Kinetics. Lower Mitcham. South Australia. 2007.
Grimshaw, P., Lees, A., Fowler, N. & Burden, A. Sport & Exercise Biomechanics. Taylor & Fransis
group. New York. US. 2006.
Mac Williams, B, Choi, T., Perezous, M., Chao, E., & McFarland, E. Characteristic Ground
Reaction Forces in Baseball Pitching. The American Journal of Sports Medicine, 26(1): 66-71,
1998.
Matsuo, T., Escamila, R.F., Fleisig, G.S., Barrentine, S.W., & Andrews, J.R. Comparison of
kinematic and temporal parameters between different pitch velocity groups. Journal of Applied
Biomechanics, 17(1): 1-13, 2001.
Orchard, J., James, T., Kountouris, A., Portus, M. Injury Report 2008. Cricket Australia Centre of
Excellence. 2008.
Phillips, E., Portus, M., Davids, K., Brown, N., Renshaw, I. (2010). How do our ‘quicks’ generate
pace? A cross sectional analysis of the Cricket Australia pace pathway. In M. Portus (Ed.)
Conference proceedings from Conference of Science, Medicine & Coaching in Cricket, Cricket
Australia, Brisbane.

89
Wixted, A., Spratford, W., Davis, M., Portus, M., James, D. (2010) Wearable sensors for on field
near real time detection of illegal bowling actions, In M. Portus (Ed.) Conference proceedings from
Conference of Science, Medicine & Coaching in Cricket, Cricket Australia, Brisbane.
Zatsiorsky, V. Kraemer, W. Science and Practice of Strength Training.2nd Ed. Human Kinetics.
Lower Mitcham. South Australia. 2006.

90
Training Responses of AIS Cricket Scholars to an Elite Cricket off Season Program
Aaron Kellett1, Kevin Sims1 and Kieran Young2
1 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
2 Queensland Academy of Sport, Brisbane
Correspondence: akellett@coe.cricket.com.au
It is well established that a well designed training program can elicit positive adaptations that result
in improved performance (Smith, 2003). In the construction of the training plan, practitioners must
aim to balance all elements of physical preparation programming to achieve the main goal of
training, namely the improvement of the athlete’s skills and performance (Bompa, 1983). This
requires that training load, competition loads, off field stressors and recovery are managed to avoid
excessive fatigue that may lead to overtraining, while still ensuring that training stimuli are
appropriate to elicit positive physiological adaptations. While there is some research that has
examined the longitudinal physiological responses to training and competition in a variety of sports
over the course of a season (Kraemer et al., 2004; Hoffman et al., 2005; Cormack et al., 2008),
little is known about the training response of cricketers to an off season preparation program, and
the impact of weekly training load variations on physical performance and musculoskeletal
function.
Methods: Twelve (12) full time Australian Institute of Sport (AIS) Cricket Scholars were analysed in
this study over an eleven week period. Using the RPE method for session intensity (Foster et al.,
2001), arbitrary training load units (AU) for each subject were quantified by multiplying the session
intensity by the session duration in minutes (Foster 1998; Foster et al., 2001; Hoffman et al., 2005).
These measures were taken after every session and were classified according to training type.
They were then summed to determine total weekly training load (TL). Two components of TL were
also investigated. ‘Cricket’ load (CL), comprising skills training and match load units, and ‘weights’
load (WL), quantifying accumulated training load from resistance training sessions.
On the first training day of each week, prior to training that day, a motor fitness assessment and
musculoskeletal profile were undertaken by each athlete. Hip internal (Hip IR) and external (Hip
ER) rotation range in neutral, along with ankle dorsiflexion (KTW) ranges were assessed using a
cricket specific protocol described by Dennis et al. (2008). Vertical jump measures were those
previously shown to have the highest reliability when using single (1CMJ) and 5 repeat (5CMJ)
counter-movement jump protocols (Cormack et al., 2008). Subjects performed this vertical jump
(VJ) testing protocol on a commercially available force plate (400 Series Performance Plate,
Fitness Technology, Adelaide, Australia) connected to a computer running the Ballistic
Measurement System software (Fitness Technology, Adelaide, Australia).
The previous weeks workload measures were pair matched with the following Monday’s vertical
jump (VJ) force/power characteristics and range of motion data (11 weeks of pair matched data).
Data were pooled according to jump type. Variables were analyzed using the effect statistic (ES)
with 90% confidence intervals (CI) to determine the magnitude of effects using a custom
spreadsheet (Hopkins, 2007). Pearson correlations (r) were calculated using SPSS for Windows
(version 18) to assess the relationships of absolute values between TL, CL and WL from the
previous week with VJ measures and ROM scores. A statistical significance level of p ≤ 0.05 was
used for these correlations.
Results: Total load from the previous week indicated only a moderate correlation with 5CMJ Peak
Force (r = .343). When divided into training types, further relationships emerged between load
variables and both vertical jump and musculoskeletal function. CL the previous week showed a

91
small correlation with Peak Force for 5CMJ (r = .26) but also a small correlation with lower Hip IR
on both left and right sides (r = -.273 and -.242 respectively).
WL the previous week showed large correlations with Peak Power for both 1CMJ and 5CMJ (r =
.565 and .536 respectively) and Relative Peak Power for both 1CMJ and 5CMJ (r = .565 and .545
respectively). A moderate correlation was observed with 1CMJ Peak Force (r = .321), as well as a
small correlation with 5CMJ Peak Force (r = .275). When analysed with range of motion scores,
WL showed small positive correlations with both left and right Hip IR range values (r = .282 and
.245 respectively).
Figure 1 shows the variations in weekly training load variables (expressed as mean ± SD) and the
mean of the most significantly correlated response variable for musculoskeletal function – right hip
internal rotation (Hip IR Right) – as well as VJ performance.
Figure1: Weekly changes in mean load, vertical jump and right hip internal rotation (Hip IR Right).
Cricket Load and Weights Load are shown in bars and 1CMJ Peak Power and Hip IR Right are
shown as lines.
Discussion: This study found that increases in the training loads had varying motor performance
and joint range of motion responses the following Monday. It was evident that this was according to
the type of training stimulus imposed. Increases in the amount of resistance training were strongly
associated with improvements in lower body force and peak power variables in vertical jump
performance at the beginning of the next training week. This could be related to a number of
factors. Firstly, the developmental nature of the athletes involved in the program meant that a
large proportion of the sample had limited experience with training methods for strength and power
development for elite athletes. It is therefore likely that these athletes had considerable
neurological adaptations to the stimulus imposed in resistance training sessions, predominantly
due to learning effects and changes in inter and intra muscular coordination of the muscles
involved in jumping (Folland and Williams, 2007). Secondly, each athlete’s training load and
programming interventions were prescribed according to the results of a thorough needs analysis.
This often resulted in day to day manipulations for each individual’s program based on the
observed capabilities for the athlete to complete and handle the stimulus imposed. This is
highlighted by the often large standard deviations for the training load variables in Figure 1.

92
Along with varying performance responses, this study found that musculoskeletal function
responses were also dependent upon the dominant training stimulus imposed the previous week.
Specifically, increases in WL were linked to higher values of Hip IR, and that increases in CL were
associated with lower Hip IR range of motion scores over the eleven week period. While the links
between hip range of motion and injury have not been clearly established, it has been suggested
by some researchers that greater available hip rotation ranges are associated with exertional
medial tibial pain in defence force personnel (Burne et al., 2004), as well as increased risk of injury
in cricket fast bowlers (Dennis et al., 2008). However, this is in conflict with findings by Ibrahim et
al. (2007) that reductions in hip rotation range are correlated with increases in adductor strains in
professional soccer players and a study of Australian Footballers that found reductions in both hip
internal and external rotation was linked to players exhibiting pubic bone stress and current groin
pain (Verrall et al., 2005).
There are a number of limitations to this study that should be noted. Firstly, no links with injury
were investigated in response to the stimulus imposed, only injury risk inferred from changes in
musculoskeletal function. Secondly, due to the nature of players starting and finishing the program
at different times, there was a dropout rate between 3 and 6 subjects during the 11 week study
period.
Conclusion: The varied ROM responses as a result of different types of training allows strength &
conditioning practitioners and physiotherapists the opportunity to implement strategies to
proactively manage range of motion changes in athletes. This can be useful during different
phases of an off season program that often shifts emphasis from non cricket specific physical
preparation to a heavy cricket focus as the competitive season approaches. Understanding the
fatigue response is a critical element to determining the effectiveness of the training loads being
imposed on the individual athlete. The elite developmental athletes in this study responded
positively to increases in training load, which highlights the importance of incorporating well
planned and progressive phases of overload into the athlete’s overall off season preparation
program. This study has indicated that a full day of recovery following a six day training week can
be enough to elicit positive training adaptations when coupled with cyclical changes in training
load, and an individualized approach to the prescription of exercise.
Acknowledgements: The authors would like to thank Dr Marc Portus and Associate Professor Ian
Heazlewood for assistance with statistical analysis, as well as Mr. Stuart Karppinen for ongoing
assistance with interpretation of data and budgetary support for the equipment used in this project.
References
Bompa, T. (1983). Theory and Methodology of Training: The Key to Athletic Performance,
Kendall/Hunt Publishing.
Burne, S. G., K. M. Khan, et al. (2004). "Risk factors associated with exertional medial tibial pain: a
12 month prospective clinical study." British Journal of Sports Medicine 38: 441-445.
Cormack, S. J., R. U. Newton, et al. (2008). "Neuromuscular and Endocrine Responses of Elite
Players During an Australian Rules Football Match." International Journal of Sports Physiology and
Performance 3: 359-374.
Cormack, S. J., R. U. Newton, et al. (2008). "Reliability of Measures Obtained During Single and
Repeated Countermovement Jumps." International Journal of Sports Physiology and Performance
3: 131-144.
Dennis, R. J., C. F. Finch, et al. (2008). "Use of field-based tests to identify risk factors for injury to
fast bowlers in cricket." British Journal of Sports Medicine 42(6): 477-482.

93
Folland, J. and A. Williams (2007). "The Adaptations to Strength Training: Morphological and
Neurological Contributions to Increased Strength." Sports Medicine 37(2): 145-168.
Foster, C. (1998). "Monitoring training in athletes with reference to overtraining syndrome."
Medicine & Science in Sports & Exercise 30(7).
Foster, C., C. Heimann, et al. (2001). "Differences in Perceptions of Training by Coaches and
Athletes." Sports Medicine June: 3-7.
Hoffman, J. R., J. I. E. Kang, et al. (2005). "Biochemical and Hormonal Responses during an
Intercollegiate Football Season." Medicine & Science in Sports & Exercise 37(7): 1237-1241.
Hopkins, W. (2007). "A spreadsheet to compare means of two groups." Sportscience 11: 22-23.
Ibrahim, A., G. Murrell, et al. (2007). "Adductor strain and hip range of movement in male
professional soccer players." Journal of Orthopaedic Surgery 15(1): 46-49.
Kraemer, W. J., D. N. French, et al. (2004). "Changes in Exercise Performance and Hormonal
Concentrations over a Big Ten Soccer Season in Starters and Non Starters." Journal of Strength &
Conditioning Research 18(1): 121-128.
Smith, D. (2003). "A Framework for Understanding the Training Process Leading to Elite
Performance." Sports Medicine 33(15): 1103-1126.
Verrall, G., I. Hamilton, et al. (2005). "Hip joint range of motion reduction in sports-related chronic
groin injury diagnosed as pubic bone stress injury." Journal of Science and Medicine in Sport 8(1):
77-84.

94
The Effect of a Formalised Goal Setting Program on Perceptions of Quality of
Performance in Training in an Elite Cricket Sample
Michael Lloyd
Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: mlloyd@coe.cricket.com.au
Personal goals have the potential to powerfully influence how people behave and how they
experience their lives. Nowhere is this influence more evident than for athletes and how they
experience their training and competition environments. Extensive psychological research has
been conducted with the results indicating that goal setting clearly and consistently facilitates
performance (see Deci & Ryan, 2001). Furthermore, the literature suggests that the beneficial
effect/s of goal setting on task performance are amongst the most robust and replicable findings in
the psychological literature (Locke, Shaw, Saari, & Latham, 1981; Sheldon, Kasser, Smith, &
Share, 2002).
When coaches and/or sport psychologists regularly set and evaluate performance goals with
athletes there are many physical and psychological performance related benefits (Patchell &
Foster, 2008). The psychological benefits include enhanced effort and confidence at training as a
result of achieving identified performance goals, directing the performer’s attention to important
aspects of a task, increasing persistence, and developing and employing new task-related
strategies (Lee, Sheldon, & Turban 2003). The simple yet effective process of setting and
evaluating performance goals also promotes athletes self-awareness and ability to enhance their
technical development (Patchell & Foster, 2008).
Despite the apparent efficacy of the technique, unfortunately goal setting is not always effectively
employed by athletes, coaches and/or sport psychologists. It is often falsely assumed, for example
that because athletes set goals on their own, these goals will automatically facilitate performance.
This is seldom the case, however, given that many athletes set ineffective goals or do not set goals
in a systematic fashion. Similarly, coaches and sport psychologists often fail to initiate the follow-
up and evaluation processes that are necessary if goal setting is to be effective (Gould, 1993).
The purpose of the current study was to utilise a formalised goal setting program (AIS Acton Goal
Setting Program) to facilitate the development of focus, confidence and effort at training, and
important critical analysis and problem solving skills. To explore how goal setting may assist in
facilitating the achievement of these objectives, a two-week training goal intervention was
conducted with the 2009 AIS Men’s Cricket Scholars (n=12, ages=17-26 yrs.) whilst they attended
the Cricket Australia Centre of Excellence (CACOE).
One week after the commencement of the scholarship period the AIS Scholars attended a goal
setting workshop at which they were asked to rate their perceptions on a range of physical and
psychological factors related to their current training quality using the Perceptions of Quality of
Performance at Training (adapted from Ebbeck & Weiss, 1988). Participants were asked to rate
(out of 5) how they felt physically, their quality of technique, concentration, effort, mental attitude,
self-confidence, and performance expectations at training. They were then introduced to the
concept and process of Action Goal Setting (i.e., a set of specific skills required to examine and
break down skill areas into specific actions and evaluate progress within a specified timeframe).
As part of the process, each scholar was asked to identify and list a series of short (i.e., coming
days/weeks), medium (i.e., coming season/scholarship period) and long-term (i.e., dream) goals.
They were also provided with an Action Goal template (see Figure 1) and asked to reflect and
create a list of specific (i.e., action-oriented, positively worded, measurable, etc.) skills they
believed needed to be worked on to help achieve their identified goals.

95
Date What specific thing/s am I going to do
to achieve my goal?
Did I
Achieve
my Goal?
If not achieved, what do I
need to do to achieve it
in the future?
Yes/No
Yes/No
Yes/No
Yes/No
Yes/No
Figure 1: AIS Action Goal Setting Template.
To investigate how Action Goal Setting could impact player’s perceptions of physical, technical,
and psychological aspects of training, players were encouraged to apply the action goal setting
process at each of their training sessions over a 2 week period. Coaches provided an opportunity
prior to the commencement of training for players to identify and write down at least one specific
action goal for the session, and then further time at the completion of training to evaluate their
performance, whether they achieved their stated goal, and if not what they had to do in order to
achieve the goal in the future. Whilst players were solely responsible for identifying and evaluating
their own performance goals, coaches were present and available to provide feedback and
technical information when requested by players.
At the completion of the two week training intervention, players were again asked to rate how they
felt physically, their quality of technique, concentration, effort, mental attitude, self-confidence, and
performance expectations at training. They were also asked to rate how often they set and
evaluated their training goals, and the effectiveness of setting and evaluating training goals at
improving overall cricket performance.
On average, players reported setting goals for most sessions and that the process of identifying
and evaluating personal training goals to be an effective method of improving their overall cricket
performance. Players reported significant increases in their overall quality of technique (p<.01),
amount of effort given (p<.05), self-confidence (p<.01), and how they were feeling physically during
training (p<.05), after only two weeks of regularly identifying and evaluating performance training
goals. Despite a turn-over of some players and coaches during the scholarship period, a general
commitment was shown to adhering to the goal setting process. Therefore, a follow-up measure
was conducted that indicated that the perceived performance benefits had been maintained over a
six week period and leading into the competition phase of the scholarship period (see Figure 2).

96
Figure 2: Perceptions of Quality of Performance at Training, pre-intervention, post-intervention,
and follow-up for 2009 AIS Scholars.
In addition to the findings displayed in Figure 2, the 2009 AIS Scholars also provided detailed
qualitative feedback outlining their views on how the use of training goals helped their
performance. These views had a number of consistent themes and provided important insights
into the nature of the quantitative results. A representative sample of the scholars’ qualitative
feedback is outlined below:
“Setting goals helped me get exactly what I wanted out of the session”
“Gave me a direction and purpose”
“Especially useful in the "free expression" sessions”
“Have a focus for each session and helped motivation by thinking about big picture”
“Helped me to concentrate harder”
“I had clearer plans of what to achieve”
“Made it more specific and get more out of it”
“Give me something to achieve during training and work on”
“Setting some small goals helped me to understand what I was trying to get out of each
session”
While goal setting is certainly not a new concept in sport, the effectiveness of goal setting
interventions have yet to be thoroughly explored and utilised in applied settings. The quantitative
and qualitative data outlined within this paper suggests that brief structured goal setting
interventions that are supported by coaching staff can help to facilitate the development of focus,
confidence and effort at training, and that players can experience enduring effects if provided
adequate opportunity and effective goal related processes.
Given the promising nature of the results outlined within this paper, similar future research is
encouraged to investigate whether such findings can be replicated within different populations

97
(e.g., genders, age groups, levels of expertise, etc.) across different time frames both within cricket
and across different sports. Similarly, incorporating coaches’ evaluations of players performances
at training, along with specified performance measures, would provide a more holistic
understanding of the impact of goal setting interventions on performance and assist in the
development of more effective mental and physical skill training programs.
Tips for Coaches when implementing a goal setting intervention:
• Do not assume shared understanding and/or competence within a group regarding goal
setting and associated processes
• Take the time to explain and discuss the concept of goal setting and the Action Goal
Setting process, including encouraging athletes to identify and articulate short, mid and
long-term goals, and the specific actions required to achieve these goals
• Provide athletes with time and opportunity before and after training to set and evaluate
technical performance goals using the template provided above
• Provide technical information when asked by athletes
• Encourage athletes to break skills down and develop action plans to improve specific skills
• Ask athletes to write their performance goals down and keep a record of their progress
Acknowledgements: The author would like to gratefully acknowledge the assistance and
cooperation of the 2009 AIS Male Cricket Scholars, Aaron Kellett (CA Sports Scientist – Strength &
Conditioning Coach), and Stephen Timms (CA Research Assistant).
References
Deci, E. L., & Ryan, R. M. (2001). The “what” and “why” of goal pursuits: Human needs and the
self-determination of behaviour. Psychological Inquiry, 11, 227-268.
Ebbeck, V. & Weiss, M. R. (1988). The arousal-performance relationship: task characteristics and
performance measures in track and field athletics. The Sport Psychologist, 2(1), 13-27.
Lee, F. K., Sheldon, K. M., & Turban, D. B. (2003). Personality and the goal-striving process: The
Influence of achievement goal patterns, goal level, and mental focus on performance and
enjoyment. Journal of Applied Psychology, 88, 256-265.
Locke, E. A., Shaw, K. N., Saari, L. M., & Latham, G. P. (1981). Goal setting and task
performance. Psychological Bulletin, 90, 125-152.
Patchell, J. Foster, P. (2008). Goal Setting: Training athletes to ‘think like coaches’. Sports Coach,
30(1): 21-24.
Sheldon, K. M., Kasser, T., Smith, K., & Share, T. (2002). Personal goals and psychological
growth: Testing an intervention to enhance goal attainment and personality integration. Journal of
Personality, 70, 5-31.

98
Can Your Players See the Ball? What a Cricket Coach Needs to Know about the
Eyes and Vision of their Players
David Mann
Skill Acquisition Department, Australian Institute of Sport, Canberra
School of Optometry and Vision Science, University of New South Wales, Sydney
Correspondence: david.mann@ausport.gov.au
A considerable number of Cricket Australia/State contracted players have been found to be playing
with uncorrected visual problems over the past 12 months. Good vision is clearly a critical part of
the vast majority of skills exhibited by accomplished cricketers, yet visual screenings have rarely
been performed within the Cricket Australia developmental pathways. This shortcoming has
resulted in State players (at both a junior and senior level) being found to have problems which are
likely to directly impact on their playing performance. This presentation will outline how
deficiencies in a number of critical components of vision can adversely affect cricketing
performance, how the observant coach can detect these problems, and what a coach and/or vision
specialist can do to correct these shortcomings.
Skilled athletes do not appear to possess ‘supra-normal’ vision. In other words, elite sportspeople
do not have better vision than lesser-skilled athletes, nor do they have superior vision to the
general population. What is apparent, though, is that skilled athletes do need normal vision, and
that any visual problem or deficiency is likely to adversely affect sporting performance. Possessing
‘normal vision’ requires a lot more than just the ability to read letters on a chart. When performing
sport-specific tests, the vision specialist (Optometrist or Ophthalmologist) will test a wide variety of
different visual skills; deficiencies in a particular selection of these skills can significantly impact on
cricketing performance. The aspects of vision most critically related to cricketing performance are
considered below:
Visual acuity: this test seeks to evaluate how clearly a player can see, and the amount of muscular
work that the eyes must do to see clearly. Three particular conditions are important to address
when considering cricket: short-sightedness, long-sightedness, and astigmatism. Each can be
corrected by spectacles, contact lenses, or potentially by laser surgery.
Short-sightedness is the visual problem elite players are most likely to subjectively notice and/or
report, resulting in poor distance vision in the presence of good near vision. The prevalence of
short-sightedness is increasing, as it is strongly associated with high volumes of near-work (like
reading and computer use). Accordingly, players who perform well academically are more likely (i)
to be short-sighted, and (ii) to increase their short-sightedness at a faster rate. This condition is
particularly important in a cricketing population as it develops and progresses either in the early to
mid-teens, or in the player’s mid-20’s. Recent work has shown that short-sightedness may
(somewhat surprisingly) have little impact on cricket batting, though it is unmistakably important for
tasks like searching for the ball in a bowler’s hand, observing the seam when batting, fielding in the
deep, or even reading a scoreboard.
Long-sightedness is a relatively common condition, though players who have it are much less likely
to notice its presence. A player who is long-sighted will generally be able to see well; however
their eyes will have to work harder to do so. For a young player, this means that they are unlikely
to complain of being unable to see the ball; rather, their eyes are likely to fatigue after prolonged
periods of concentration. For batters, this can have a significant impact when seeking to play a
long innings – the intense visual concentration required over a long period of time can result in
fatigue and tiredness.

99
Players with astigmatism experience distorted vision, compromising both their distance and near
sight. It is relatively uncommon for players with undetected astigmatism to complain of poor vision
as many have adapted to the relatively poor vision over a long period of time. This condition does,
though, particularly result in poor night vision – a concern for those likely to be playing or training
for cricket in artificial light.
Contrast Sensitivity: in cricket, visual contrast is generally maximised; for example a red ball is
used against a white background, or a white ball is used at night. In reality, less favourable
situations are experienced: in the outfield the red ball will be observed against a coloured
background, and at night the white ball will become dirty. Tests of contrast sensitivity seek to
address how well a player can see when visual conditions are sub-optimal – this is particularly
important for athletes playing at night. A number of different conditions can impact on sensitivity to
contrast (e.g., astigmatism, amblyopia, and corneal diseases), though many are more likely to
impact on performance with age.
Eye dominance: the human brain prefers to use one eye when perform aiming tasks like those
required for snooker and darts. Similarly when batting, it is thought that one of the two eyes will be
‘dominant’ when the brain coordinates the visual-motor movements that are required to hit a ball.
This can mean that different batting stances are required depending on which eye is dominant.
For example, a right-handed batter with a dominant left eye will be able to maintain a relatively
side-on stance to align the left eye with the ball, whereas a right-hand batter with a dominant right
eye may require a more open stance to rotate the head so that the right eye can be aligned with
the ball. It is important for the informed coach to be able to test for the dominant eye, and to allow
for the kinematic changes that may be required to guide the player towards a most favourable
batting stance. Furthermore, the role of the dominant eye will be addressed in relation to the
somewhat high prevalence of modern-day elite players who bat in a stance which is at odds with
the throwing hand (e.g., those who bat right-handed but throw/bowl with their left-hand). The
dominant eye tends to match the dominant hand, but this is not always the case.
Eye movements: quick and accurate eye movements are critical in cricket. When facing fast
bowling, the ball moves too quickly for the batter’s eyes to accurately follow the movement of the
ball; as a result, the batter sees the ball out of the bowler’s hand, predicts where it will bounce, and
makes a fast eye movement forward to the anticipated location of the ball. Skilled batters make a
quicker assessment of where the ball will land, allowing them to be in a good and balanced
location earlier than lesser-skilled batters.
A number of different conditions can affect a batter’s ability to accurately perform this fast
movement of the eyes. Some players have a tendency to under- or over-converge their eyes,
meaning that extra muscular work is required that can lead to visual fatigue. Other players are
found to perform inaccurate eye movements, particularly when they are placed under pressure.
Deficiencies in eye movements can generally be corrected by eye exercises.
Colour vision: approximately 1 in 11 males (and 1 in 200 females) have a deficiency in their colour
vision. Typically this causes difficulties in discriminating red from green - a noteworthy problem
considering that cricket requires the perception of a red ball from what is often a green
background. An under-representation of colour vision deficiencies has been shown to exist in first-
class cricketers, inferring that it is more difficult (though not impossible) to make it to the elite level
with defective colour vision. Unfortunately colour vision deficiencies cannot be corrected; rather it
is important for the coach to be aware of the situations and scenarios in which these athletes are
most likely to be affected, and to allow appropriate adaptations to be made in both the game and in
the daily training environment.
The Cricket Australia Sport Science Sport Medicine program has instigated appropriate vision
screenings on both men and women in the National high performance pathways. This process

100
ensures that players (at or above the U18 level) undergo a vision screening, most frequently at the
Centre of Excellence in Brisbane. Those players who fail the screening are referred to a
recommended Optometrist in their home state, ensuring that ongoing care can be provided on a
regular basis (where required).
This presentation will address what can be done by local, regional, and state coaches to ensure
that their players possess the level of vision required to provide most favourable cricketing
performance. A conventional eye-test is unlikely to sufficiently address the problems expected to
adversely impact on cricketing performance; rather, a sport-specific test of vision is most desirable.
The relative benefits and costs of vision screenings and/or full individual eye-tests will be
discussed.

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Back Injuries in Pace Bowlers – An Under-use Injury?
Graeme Nuttridge1 and Peter Milburn2
1 PhysioSouth, Christchurch, and University of Otago, Dunedin
2 Griffith University, Gold Coast
Correspondence: p.milburn@griffith.edu.au
The majority of research into pace bowling injuries has focused on bowling technique and
associated biomechanical risk factors (Finch et al., 1999) and little formal research has been
completed on the overuse or loading aspects of these injuries. The observation of a bowler having
poor technique and no history of injury suggests that factors other than biomechanical may be
highly relevant to solving the problem of overuse injuries to pace bowlers. Previous injury, under-
use and sudden changes in bowling volume have all been discussed in the literature as being
linked to injury (e.g. Dennis, et al (2003), Orchard, et al. (2002, 2009).
The Australian Cricket Board stated that bowling for extended periods will predispose a bowler to
lower back injury (Finch et al., 1999). As a result, Cricket Australia limits Under19 year-old bowlers
of medium pace and above to a maximum of eight consecutive overs in one spell and a maximum
of 20 overs per day. Foster et al. (1989) found 59% of the bowlers who bowled in excess of the
mean number of matches for a group suffered a stress fracture or soft tissue injury to the lower
back compared to 38% of the total group. The authors commented that poor preparation (not being
physically prepared for bowling) was also associated with the risk of injury. Also, fatigue due to
over-bowling has been cited as a possible risk factor due to possible alteration in technique when
bowlers tire (Burnett et al., 1995) although they found the biomechanics of bowling technique
throughout the duration of a 12-over spell did not vary.
Despite applying the best practice and standards to the problem of overuse injuries to bowlers, the
high injury rate and prolonged periods of disability associated with these injuries continue
unabated. The purpose of this paper is to examine the relationship between the incidence of injury
and bowling workloads and previous injury.
Method: A prospective cohort design was used to document the nature and prevalence of injuries
to pace bowlers and to analyse the relationship between the incidence of injury to bowling
workload. Forty-three senior club pace bowlers in Christchurch, New Zealand and six first class
pace bowlers (provincial level or higher) were included (mean 23.4 ± 3.0 years) and data were
obtained from questionnaires and player diaries. Injury rate was defined as the number of injured
bowlers expressed as a percentage of the total population of the group in which comparisons were
being made. Players were divided into injured and non-injured group, with further subdivision into
the type and duration of injury, but each separate injury could have only one classification.
Analysis of variance (ANOVA) was used to compare injury type with workload and non-parametric
tests (Mann-Whitney) were used to compare whether the player was injured or not during the
season with the type of injury
Results: In this study of 49 pace bowlers, less than half (20 of 49 or 41%) remained injury-free
during the season and the majority (29 or 59%) sustained at least one injury. There were 38
injuries recorded during the study affecting the 29 players with some players recording as many as
three separate injuries. The most commonly injured body region recorded was the lower limb (58%
Table 1), which accounted for 45% of all bowlers. The next most commonly injured body region
was the trunk (29%), which involved 22% of all bowlers and included lumbar spine stress fractures
(7 of 38 injuries or 14%). The least injured region was the upper limb (13%), which represented
10% of all bowlers.

102
Table 1. Injury Rates Classified by Nature and Region
Injury Group Definition Number of
Bowlers (N=49) Incidence (N=49)
(Percentage)
Group One Uninjured 20 41
Group Two A Injured 29 59
Type One 24 (31 Injuries) 49
Type Two 5 (7 Injuries) 10
Upper Limb Injuries 5 10
Trunk Injuries 11 22
Lower Limb Injuries 22 45
Group Two B Stress Fractures 7 14
For the purpose of this study, the severity of injury was defined in terms of disability, measured as
the length of time taken to fully recover bowling volume or intensity. Bowlers who were prevented
from bowling (Type One injury) accounted for 31 of 38 injuries and the average time taken to return
to restricted bowling was 5.6 weeks. Injuries that did not stop bowling (7 of 38 injuries) but
reduced bowling workload (Type Two injuries) affected bowlers for an average of only 4.1 weeks.
Of the 38 Type One injuries, the time required to return a bowler back to full workload averaged 3.1
weeks per injury after they commenced bowling. The average injury time to recover to a full
bowling workload was 9.8 weeks, which represents approximately one third of the entire season
available to the 49 subjects and approximately one half of the season of the injured bowlers. Forty-
two percent of all injuries took 13 weeks or longer to fully recover (when the bowler reported no
restriction on pace bowling workload), and only 33% (11 of 38 injuries) recovered within the first
month of injury, which demonstrates the serious nature and prolonged disability associated with
pace bowling injury
During this study 61% of injuries occurred within the first month of the competitive season. The
trend towards injury so early in the season may relate to inadequate pre-season build-up and
management in the off-season. Bowlers with existing injuries were not accepted into the study;
however some may have been unaware of an injury, or chose not to declare it. The time taken for
stress fracture of the lumbar spine to be identified ranged from 1-4 weeks (average 2.1 weeks) and
no stress fracture occurred later in the season. Importantly three of these stress fractures were
reported almost immediately and may have represented unhealed stress fractures or an injury
caused during pre-season bowling. All stress fractures were in the lumbar spine between L2/3 to
L5/S1 and were opposite to the dominant (delivery) side. All fractures were diagnosed with a bone
scan or other radiological imaging other than a plane x-ray. The average time taken for bowlers
who sustained a stress fracture injury to recover to limited bowling was 11 weeks. One bowler
continued to bowl, unaware of the injury (or chose not to declare it), and was diagnosed after the
season was completed. The remaining bowlers with diagnosed stress fractures took an average of
13 weeks to return to a reduced bowling workload. Also, the majority (6 out of 7) of bowlers who
sustained a stress fracture had also suffered a stress fracture of the lumbar spine within the
preceding two seasons. Twenty bowlers reported no injury during the season and of these, only
five (25%) had a previous injury and the majority (75%) had been injury-free for the previous two
years.
The relative risk of injury during the season if the bowler had a previous history of injury was 2.56
(Chi-Square = 12.38, p-value = .001) and supports the observation that a bowler who had a
previous injury was more likely to get an injury to the same location during the season. There was
a statistically significant positive relationship between the previous history of injury and current
Type One (F1, 47 = 38.248, p = .001) and Type Two (F1, 47 = 14.916, p = .001) injuries. Players
injured in the previous two years were significantly more likely to sustain an injury. There was a
highly statistically significant negative relationship between previous history and all workload
variables, including average balls bowled per week (F1, 47 = 1.30, p = .003), balls bowled in training

103
over the season (F1, 47 = 9.967, p = .027) and balls bowled in games over the season (F 1, 47 =
5.185, p = .001). The average number of balls bowled per week has been adjusted to exclude
weeks in which bowlers could not bowl due to injury and, therefore, reflects weekly bowling rates
when uninjured. This suggests that bowlers with a previous history of injury were likely to bowl with
reduced workloads during the season. There was no statistically significant relationship between
perceived effort and previous injury, either in training effort (F1, 47 = 2.276, p = .138) or in effort
during games (F1, 47 = 2.434, p = .126).
Bowlers who became injured during the season bowled significantly (p<. 05) less preseason (mean
= 1161) than those who remained injury free (mean = 1533). This trend showed a moderate
relationship between the volume of preseason bowling and whether an injury was sustained during
the season analysed, but was not statistically significant (F1, 47 = 1.388, p = .246) between the
injured and uninjured groups. As expected, bowlers who remained injury-free during the season
bowled significantly (p < .05) more deliveries than the injured group (2359 deliveries compared to
1515), and the stress fracture group bowled the least (993 deliveries). The weekly average
‘normalises’ the time spent bowling and reflects the total number of balls bowled divided by the
number of weeks the bowler was able to bowl and is a more accurate reflection of bowler
workload. Consistent with the total average deliveries, the weekly average of deliveries was higher
in the bowlers who remained injury-free during the season compared to those who were injured.
The results show a statistically significant difference between the numbers of balls bowled for
uninjured bowlers (mean = 150.2) and injured bowlers (mean = 113.9, U = 185, p = .033). The
results of ANOVA tests also show a strong negative relationship between the volumes of bowling
in training only (F1, 47 = 3.90, p= .011) and overall (F1, 47 = 5.625, p = .022) to the incidence of injury,
including stress fractures. However, these results should be treated with caution due to the early
onset of the injuries and the association with recurrent injuries. Both factors could influence the
bowler’s ability and confidence to bowl with heavier workloads.
Group One (non-injured bowlers) bowled more balls in training and in competition than Group Two
(injured bowlers). Statistical analysis of balls bowled during games and during training sessions
shows a statistically significant relationship between training volumes and injury (F1,47 = 7.06, p=
.011). This trend continued with balls bowled in games, but the difference was not statistically
significant (F1,47 = 3.31, p= .054). However, the Mann-Whitney test showed a moderately weak
association (p = .093) in balls bowled in training (weekly) between the uninjured (mean = 80.6) and
injured bowlers (mean = 61.0). This has more relevance than total workload as the mean balls
bowled per week excludes weeks in which injured bowlers were unable to bowl. It is difficult to
draw any meaningful conclusions between the risk of injury and workload during training and
competition because the risk appears to be similar for both. However, increased training should
condition the bowler to the rigours of competition and therefore the negative relationship between
training and injury could be partially explained by this.
The incidence rate was slightly higher than previous studies and this difference could be explained
by the use of different methodology, the effects related to a smaller population (N = 49) and the
effects related a different geographical region, but demonstrates a unique pattern of injury of which
stress fracture of the lumbar spine is a feature. The disabling nature of pace bowling injuries are
serious in nature in that they will only heal after prolonged periods of rest (Crisp, 1992) and the
results of the current study support this. The reasons for the prolonged disability associated with
pace bowling injury has not been fully explored but reflects upon the high impact and repetitive
nature of pace bowling, which presents challenges when returning bowlers back to such a high
level of function after injury.
Conclusion: The early onset of injuries in season has previously been reported and Stretch (1995)
reported another peak near the end of the season. Previous articles (McGrath & Finch, 1996) have
focused on the overuse nature of pace bowling injuries, implying increasing risk with increasing
exposure to bowling. However, Leary & White (2000) retrospectively analysed injuries over a

104
decade in England and found injuries were more prevalent in April, which had the least amount of
cricket and was early in their season. This suggests the transition from off-season training to in-
season bowling may have been poorly managed or that old injuries may not have fully recovered
during the off-season and re-injury thresholds could be lowered. The results of this study suggest
that previously injured bowlers were 21/2 times more likely to be injured (to the same anatomical
region) during a season and that overuse was not a factor in the relationship between previous and
current injury. Therefore, pace bowlers should be fully rehabilitated before returning to bowling and
managed in terms of workload during the first month of competition and when returning from injury
in order to reduce the high injury rate.
Pace bowling injuries were not related to workload in the current study and bowlers and coaches
may consider higher workloads when there is a low previous injury rate. This study suggests high
volume, low intensity progressive pace bowling workloads to precondition the bowler’s body to
promote specific physiological adaptation. Training workloads therefore need to adequately
prepare bowlers for the rigorous workloads associated with competition bowling.
References
Burnett A, Khangure M, Elliott B, Foster D, Marshall R & Hardcastle P. Thoracolumbar disc
degeneration in young pace bowlers in cricket: A follow-up study. Clinical Biomechanics. 11:6:305-
310. (1996).
Crisp T. Fast bowler’s back and thrower’s shoulder. Practitioner. 233:(May):790-792 (1989).
Dennis R, Farhart P, Goumas C, & Orchard J. Bowling workload and the risk of injury in elite
cricket fast bowlers. Journal of Science & Medicine in Sport. 6(3):359-67, (2003).
Finch C, Elliott B & McGrath C. Measures to prevent cricket injuries: an overview. Sports Medicine.
28:4:263-272. (1999).
Foster D, John D, Elliot B, Ackland T & Fitch K. Back injuries to fast bowlers in cricket: a
prospective study. British Journal of Sports Medicine. 23:3:50-4. (1989).
Leary T & White J. Acute injury incidence in professional county cricket club cricket players (1985-
1995). British Journal of Sports Medicine. 34:2:145-7. (2000).
McGrath A & Finch C. Bowling cricket injuries over: a review of the literature. Report Number 105.
Monash University Accident Research Center, Victoria, Australia. (1996).
Orchard, J, James, T, Alcott, E, Carter, S,& Farhart, P. Injuries in Australian cricket at first class
level 1995/1996 to 2000/2001. British Journal of Sports Medicine. 36: 270-75. (2002)
Orchard, JW, James, T, Kountouris, A & Dennis, R. . Fast bowlers in cricket demonstrate up to 3-
to 4-week delay between high workloads and increased risk of injury. American Journal of Sports
Medicine. 37(6):1186-92. Epub 2009 Apr 3 (2009).
Stretch R. The seasonal incidence and nature of injuries in schoolboy cricketers. South African
Medical Journal. 85:11:1182-1184. (1995).

105
Practical, Field-Based Pre-Cooling for Medium-Fast Bowling in Hot Environmental
Conditions
Geoffrey Minett1, Rob Duffield1, Marc Portus2 and Aaron Kellett2
1 School of Human Movement Studies, Charles Sturt University, Bathurst
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: gminett@csu.edu.au
Introduction: Medium-fast bowling performance places significant strain on all physiological
systems (Burnett, Elliot & Marshall, 1995; Duffield, Carney & Karppinen, 2009). When performed in
hot conditions, this physiological load may be exacerbated as efficiency of heat transfer to the
surrounding environment is reduced. Accordingly, high environmental temperatures and/or
humidity may present conditions that are problematic for optimal physical performance. Given the
well documented negative effects of hot ambient conditions or high core body temperature on
exercise performance, pre-cooling methods to counter these effects have become popular. Whole-
body cold water immersion within laboratory settings can improve exercise performance in the heat
(Marino, 2002). However this procedure can be logistically difficult in field environments, where
access to water, water quality, pre-game routines and the number of players restricts effective
implementation. Recent studies report the use of mixed-method pre-cooling utilising multiple,
smaller cooling interventions may offer ergogenic properties and greater practicality than cold
water immersion (Duffield, Steinbacher & Fairchild, 2009; Quod et al., 2008). Given the ecological
validity of these methods, practical interventions may be a viable pre-cooling intervention for both
training and competitive environments, without the constraints, difficulties and interruptions of
whole-body techniques. Therefore, the current study aimed to investigate the effect of practical
pre-exercise cooling methods on performance and physiological responses during a 6-over
medium-fast bowling spell in the heat. This evaluated the effects of a practical, mixed-method
intervention that is inexpensive, transportable and requires minimal intrusion on pre-game
preparation for any cricket team playing or travelling to hot environments.
Methods: Using a randomised, repeated measures cross-over design, ten male (22.9 ± 7.7 yrs;
189.8 ± 8.8 cm; 84.9 ± 12.6 kg) club to state level medium-fast bowlers completed two sessions to
examine the effects of a field-based pre-cooling intervention on physiological, perceptual and
performance responses during a 6-over medium-fast bowling spell in the heat (32°C, 63%RH,
31°C WBGT). Following resting measures, participants completed either the pre-cooling
intervention or control condition. The pre-cooling intervention consisted of 20 min mixed-method
cooling. Players wore an ice-vest (Arctic Heat, Brisbane, Australia), along with a cold, wet towel
placed over the head and neck and exposed regions of the arms. Additionally, players placed their
non-bowling hand in a container of ice-cold water (10°C) and had frozen ice-packs (Techni Ice,
Frankston, Australia) placed on each quadriceps muscle. During the control condition, players sat
passively in the warm conditions. Subsequently, participants completed a 10 min warm up
consisting of jogging, sprints, and 10 deliveries to reach normal bowling speeds. Each session
consisted of a 6-over spell, based on the CA-AIS bowling skills test. Bowling in pairs, participants
performed simulated fielding activities between overs, including; walking in with the bowler 10 m
each ball and performing a 20 m sprint on the 2nd and 4th balls, respectively. Bowling performance
was measured via ball speed (Stalker ATS, Applied Concepts, USA) and ball accuracy by the CA-
AIS skills test accuracy target system. Run-up speed was measured with an infra-red timing
system (Speedlight, Swift, Australia) to determine overall and final 5 m run-up speed. Before and
after the spell, peak lower body power was recorded during repeated (10) counter movement
jumps (BMS, Fitness Technology, Australia). During the spell, movement distance and velocity of
the bowler was recorded by a 1 Hz Global Positioning System (SPI elite, GPSports Systems,
Australia). Further, core temperature (Vital Sense, Mini Mitter, USA), skin temperature
(ThermoScan 3000, Braun, Germany), nude mass, heart rate (FS1, Polar Electro Oy, Finland) and

106
perceptual measures of exertion (RPE) and thermal stress (TSS) were recorded throughout the
spell. A repeated measures ANOVA (condition x time) was used to determine differences between
conditions. Analysis was performed using the Statistical Package for Social Sciences (SPSS
v16.0, Chicago, IL). Significance was accepted when P < 0.05. Effect size analysis (ES; Cohen’s
d) was used to determine the magnitude of effect of cooling. An ES of d<0.2 was classified as
‘trivial’, 0.2 – 0.4 as ‘small’, 0.5 – 0.8 as ‘moderate’ and >0.8 as a ‘large’ effect.
Results: No significant differences (P=0.12-0.86; d=0.05-0.42) were detected between pre-cooling
and control conditions for mean bowling speeds (114.5 ± 7.1 v 114.1 ± 7.2 km.h-1), accuracy (43.1
± 10.6 v 44.2 ± 12.5 au), run up speeds (19.1 ± 4.1 v 19.3 ± 3.8 km.h-1) and running distances
(4336 ± 666 v 4328 ± 707 m). However, a moderate ES (d=0.74) was observed for increased
mean peak speeds reached during the between-over sprints in the pre-cooling condition (22.1 ±
2.2 v 20.6 ± 3.3 km.h-1). Further, no significant differences (P=0.40-0.87; d=0.07-0.55) were
apparent for CMJ peak power pre- (2971 ± 649 v 2677 ± 853 W) or post-spell (3099 ± 393 v 3082
± 321 W). No significant difference was recorded for post-over heart rates between conditions
(P=0.08-0.52; d=0.13-0.54; Figure 1, A). However, heart rates prior to each over were significantly
lower in the pre-cooling condition (P=0.01-0.04; d=0.96-1.74; Figure 1, A). Mean core temperature
was significantly suppressed in the pre-cooling condition, with core temperature reduced during the
6-overs in the pre-cooling condition (P=0.03; d=1.46; Figure 1, B). Mean skin temperature was
significantly reduced pre-spell in the pre-cooling condition (31.8 ± 0.8 v 33.1 ± 0.8 °C; P=0.00;
d=2.19), nevertheless no significant difference in mean skin temperature was apparent following
the 6-over spell (32.3 ± 1.2 v 32.5 ± 1.6 °C; P=0.23; d=23). Significant reductions in nude mass
were evident in the pre-cooling condition (1.5 ± 0.6 v 1.9 ± 0.4 kg; P=0.01; d=0.34), while no
significant differences in pre-spell USG were apparent between conditions (1.016 ± 0.006 v 1.016
± 0.007; P=0.81, d=0.04). Finally, significant reductions in mean RPE (4.7 ± 0.9 v 5.3 ± 1.1 au;
P=0.03; d=0.77) and mean TSS (4.3 ± 0.2 v 5.2 ± 0.6 au; P=0.00; d=3.13) were evident throughout
the 6-over bowling spell in the pre-cooling condition.
A.
B.
Figure 1: Mean ± SD A) core temperature and B) heart rate during a 6-over spell of medium-fast
bowling in the heat with and without pre-cooling. * Significant difference compared with control
(P>0.05). #Large effect size compared with control (d>0.80).

107
Discussion: This study aimed to determine whether inexpensive, easily portable, cooling
techniques could be used in a real-world training or competition environment to assist performance
and reduce the physiological effects of bowling in hot environmental conditions. Bowling
performance, as measured by accuracy and ball speed, during a 6-over spell in the heat was not
substantially altered by pre-cooling. Limited variation was detected in bowling speeds and
accuracy between conditions and minimal decline was present throughout the 6-over spell.
Further, the absence of change in run up speed corroborates with previous research using 12 over
spells in mild environmental conditions (Burnett, Elliot & Marshall, 1995; Duffield, Carney &
Karppinen, 2009). Accordingly, these findings add to the limited availability of research on the
effects of pre-cooling on sports-specific skill performance (Hornery et al., 2007). Whilst running
distances and activity patterns were partially standardised due to set bowling run up length,
increased peak 20 m running speeds were apparent within pre-cooling trial. This apparent self-
selected higher sprint speed during between-over efforts is in agreement with previous laboratory-
based pre-cooling research (Booth, Marino & Ward, 1997; Duffield et al., 2010). Accordingly, while
the set pace of bowling is not altered by pre-cooling, the lower physiological and perceptual loads
during bowling performance may result in increased selected speeds between overs.
Consequently, the acute benefits of pre-cooling for fast bowling performance may not be evident in
short singular spells; rather it may be possible for increases in intensity of work performed “off the
ball” or during fielding between overs.
Despite having negligible effects on acute, single-spell bowling performance, pre-cooling effectively
reduced the physiological and thermoregulatory load experienced by the players. Core
temperature was not reduced by the cooling intervention; however, the rise in internal thermal load
during the warm up and bowling spell was suppressed following pre-cooling. Thus, a pre-cooling
intervention prior to a bowling spell blunts the ensuing rise in core temperature, and hypothetically,
with repeated exposures during the day may result in protection against excessive increases in
core temperature. This maintenance of thermoregulatory balance after pre-cooling allows for an
efficient heat transfer gradient, hence observed reductions in body mass changes attributed to
evaporative sweat loss during pre-cooling conditions. These effects allow for the maintenance of
blood volume, reducing cardiovascular load and related rise in heart rate (Quod, Martin & Laursen,
2006). As such, the observed reduction in pre-over heart rate may represent a reduced
physiological stress, faster recovery and confirm the reduced thermoregulatory effects of pre-
cooling on medium-fast bowling performance in the heat. Given access and consumption of
adequate volumes of fluid may be difficult, especially in extremely hot climates, pre-cooling may
reduce sweat rate, limiting the extent of fluid loss and when combined with a hydration strategy,
provide some buffer against excessive levels of dehydration. Pre-cooling may allow players to
finish the day or session in an improved physiological state than otherwise expected.
Conclusions: Pre-cooling did not substantially influence bowling performance (speed, accuracy or
run up speed), however self-selected peak running speeds during non-bowling activities were
improved. Further, pre-cooling reduced the physiological and thermoregulatory load of the 6-over
spell, as evidenced via reduced core temperature, heart rate and sweat loss. Finally, pre-cooling
also reduced the perceptual load of the bowling spell, with lower subjective ratings of RPE and
TSS observed in the cooling condition. As such, pre-cooling may have physiological and
perceptual benefits for medium-fast bowling during training or competition in hot environments.
Given the reduced load and improved physiological state following a single-spell of bowling, it is
feasible that performance benefits may become evident as bowling spells become longer or are
repeated in hot environmental conditions. Finally, apart from explicit bowling performance
improvements, pre-cooling may be used in a protective fashion to blunt the load of performing in
hot conditions; ensuring players finish a session and commence recovery in a better physiological/
perceptual state.

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Acknowledgement: The authors wish to acknowledge Cricket Australia for their generous funding
of this project. We sincerely thank Stephen Timms, Rian Crowther, Craig McDermott and all
participants for their assistance throughout the study.
References
Booth, J., Marino, F., & Ward, J. J. (1997). Improved running performance in hot humid conditions
following whole body precooling. Medicine & Science in Sports & Exercise,29(7), 943-949.
Burnett, A., Elliott, B. C., & Marshall, R. N. (1995). The effect of a 12-over spell on fast bowling
technique in cricket. Journal of Sports Sciences, 13, 329-341.
Duffield, R., Carney, M., & Karppinen, S. (2009). Physiological responses and bowling
performance during repeated spells of medium-fast bowling. Journal of Sports Sciences, 27(1), 27-
35.
Duffield, R., Green, R., Castle, P., & Maxwell, N. (2010). Precooling can prevent the reduction of
self-paced exercise intensity in the heat. Medicine & Science in Sports & Exercise, 42(3), 577-584.
Hornery, D. J., Farrow, D., Mujika, I., & Young, W. (2007). Fatigue in tennis: Mechanisms of fatigue
and effect on performance. Sports Medicine, 37(3), 199-212.
Marino, F. E. (2002). Methods, advantages, and limitations of body cooling for exercise
performance. British Journal of Sports Medicine, 36, 89-94.
Quod, M. J., Martin, D. T., Laursen, P. B., Gardner, A. S., Halson, S. L., Marino, F. E., Tate, M. P.,
Mainwaring, D. E., Gore, C. J., & Hahn, A. G. (2006). Practical precooling: Effect on cycling
performance in warm conditions. Journal of Sports Sciences, 26(14), 1477-1487.
Quod, M. J., Martin, D. T., & Laursen, P. B. (2006). Cooling athletes before competition in the heat:
Comparison of techniques and practical considerations. Sports Medicine, 36(8), 671-682.

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Transfer of Motor Skill Learning: Is it Possible?
Sean Müller1 and Simon Rosalie2
1 Murdoch University, School of Chiropractic and Sports Science, Perth, Australia
2 RMIT University, Discipline of Exercise Sciences, Melbourne, Australia
Correspondence: S.Muller@murdoch.edu.au
Learning of motor skills through effective practice is critical for successful performance in all sports.
Equally critical is the capability to transfer what has been learned during practice (the learning
context) to a match. Together, the level of skill learning and the success of skill transfer can have a
potent influence on competitive performance. For example, will batting practice against a bowling
machine maximise skill learning and will this transfer to an enhancement of batting performance
when competing against a bowler in a match? Knowing the factors that can enhance motor skill
learning and transfer can have important implications for the investment of time and financial
resources in the design of practice by coaches, sports scientists and sport practitioners. In turn,
this knowledge is valuable to cricketers across the skill spectrum, but particularly for emerging elite
and established elite players as the sport is their profession.
A considerable body of research exists on transfer of learning in cognitive (thinking) skills. Topics
that have been investigated include analogical transfer (i.e., learning of skills in one context and
application of the skills to different context(s), namely problem-solving), formal discipline (i.e.,
transfer of memory skills, reasoning and logic learned from studying mathematics or language to
the completion of everyday tasks), teaching intelligence (i.e., analytical, creative and practical
thinking) and the impact of schooling (i.e., influence on IQ and success in securing jobs) (Barnett &
Ceci, 2002). Whilst some studies have reported positive transfer, considerable debate exists in the
literature about the extent of transfer and whether transfer of learning is even possible.
In relation to sport, little or no scientific evidence exists concerning the transfer of motor skill
learning or the mechanism(s) underpinning transfer. This dearth of evidence is surprising given
that a key aspect of the athlete-coach relationship is the ability of the coach to maximise learning in
the practice context to prepare athletes to an optimal level for match conditions. Anecdotal
evidence of transfer exists in cricket folklore, which proposes that elite batsmen with a background
in baseball may transfer perceptual-motor skills that are beneficial to expert performance in cricket
batting (so called talent transfer). Whether skills can be transferred between baseball and cricket,
however, is yet to be confirmed through scientific investigation.
Barnett and Ceci (2002) provide a classification system that can be used to better understand the
context, extent and underpinning mechanisms of motor skill transfer. Practice contexts that may be
of primary importance to cricket include physical context, functional context, temporal context,
modality and knowledge domain. Motor skill learning tasks can be structured across a near and far
continuum to the match (target) for each practice context in order to understand transfer (see table
1). Physical context refers to the structural aspects of the learning environment. Functional context
refers to the purpose of the practice task. Modality refers to the mode of practice. Temporal context
refers to the time period between practice and transfer to a match. Knowledge domain refers to the
transfer of motor skills between sports. More than one context can interact to facilitate or negate
transfer.
Table 1 provides a model to examine important questions that are directly related to the structure
of the learning and practice environment created by coaches. In relation to physical context, does
batting practice indoors against a bowler on a synthetic grass pitch transfer to a match situation
resulting in an improvement in batting performance? This would appear to be a critical question for
the southern states where outdoor practice can be restricted by inclement weather till nearer to the

110
start of the cricket season. In a related manner, does the functional context of indoor batting
practice against a bowling machine transfer to a match? Research examining the execution of
component skills in batting such as foot movements has reported delays in the initiation of foot
movements when batting against a bowling machine in comparison to a bowler (Adams & Gibson,
1989). This would appear to be due to the inability to utilise visual cues normally available from a
bowler’s action, which have been reported to guide foot movements (Müller et al., 2009). Likewise,
the modality (or mode) of training pick-up of visual cues in batting such as with or without a
movement response (latter due to fatigue or injury) may have different benefits.
Table 1: Transfer Contexts with a Continuum of Near to Far Tasks Relative to a Match
Transfer Continuum
Contexts Target Near Far
Physical Match Centre wicket Outdoor nets Indoor nets
Functional Match
Modality Match
Batsman-bowler
With-movement
Throw downs
Bowling machine
Without-movement
Temporal Match Morning before match Two days before
match
One week before
match
Domain Match Baseball Football (Aust. Rules)
Note. Table adapted from Barnett and Ceci (2002).
In relation to temporal context, it would be valuable to determine if practice immediately preceding
(few hours) or several days before a match best facilitates transfer. This could determine how
psychological/physical fatigue and rest may contribute positively or negatively to transfer. As
mentioned earlier, investigation of domain transfer would validate anecdotal claims of motor skill
transfer across sports such as baseball to cricket, which could, in turn, be valuable for increasing
the talent identification pool. It has been reported that experts from field hockey, netball and
basketball (invasion sports) are superior to non-experts at ‘reading’ the position of defensive
players, with this capability of experts transferred across the aforementioned sports (Abernethy,
Baker & Cote, 2005). This capability of experts maybe related to their broader learning experiences
that may allow them to transfer skill learning to a greater extent and with greater success (see
figure 1). Further investigation, however, is required to determine the extent of expertise transfer
between and within sports to confirm this claim.
Figure 1: Transfer of learning success relative to skill level and learning experiences
Poor Moderate High
Learning
Experiences
Novice Intermediate Expert
Skill Level
Transfer Success

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The scientific evidence of whether transfer of motor skill learning is possible is inconclusive. A
better understanding of the transfer of motor skill learning has the potential to guide evidence-
based coaching practice in high performance cricket programs. Some of the direct applications to
coaching from investigating transfer include; how to best structure the learning context and task for
transfer, what equipment and instruments to use during practice, how frequently to schedule
practice and whether to structure practice with or without a motor response. Guided by scientific
evidence it may be possible to maximise the learning experience of current and future cricketers.
References
Abernethy, B., Baker, J., & Cote, J. (2005). Transfer of pattern recognition skills may contribute to
the development of sport expertise. Applied Cognitive Psychology, 19, 705-718.
Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for
far transfer. Psychological Bulletin, 128 (4), 612-637.
Gibson, A. P., & Adams, R. D. (1989). Batting stroke timing with a bowler and a bowling machine:
A case study. The Australian Journal of Science and Medicine in Sport, 21(2), 3-6.
Müller, S., Abernethy, B., Reece, J., Rose, M., Eid, M., McBean, R., Hart, T., & Abreu, C. (2009).
An in-situ examination of the timing of information pick-up for interception by cricket batsmen of
different skill levels. Psychology of Sport and Exercise, 10(6), 644-652.

112
CA/AIS/UWA GPS PhD Scholarship: Findings, Conclusions and Future Directions
Carl Petersen1,2,3, David Pyne1, Marc Portus2, Brian Dawson3
1 Physiology Department, Australian Institute of Sport, Canberra
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
3 School of Sport Science, Exercise & Health, The University of Western Australia, Perth
Correspondence: carlpetersen2003@yahoo.co.uk
Cricket Australia recognising the relative absence of physiological research in Cricket, looked to
remedy this by offering a Global Positioning System (GPS) PhD scholarship in 2007. Three years
on this scholarship had now been completed. The current paper summarises the main findings of
the research into positional movement patterns in training and competition, as well as strategies for
preparing cricketers to perform in hot / humid environments. Suggestions for future investigation
are also offered in order to continue the momentum the PhD had developed.
The main focus of the PhD was to investigate the physiological demands of elite cricketers in both
training and competition. To achieve this objective in practical terms it was important to measure
the movement characteristics of more than one player simultaneously, and to employ a
methodological approach that was time-efficient to cope with the vast quantity of GPS-derived
information collected from games that ranged in duration from 3 to 30 hours. The relatively recent
development of GPS technology for sporting applications provided a novel method for investigating
the time-motion characteristics of cricketers during actual competition. Yet, the reliability and
validity of this technology across a range of movement velocities had yet to be quantified. The
undertaken GPS validation and reliability study indicates the magnitude of the error in the
estimation of distance increases non-linearly as a function of the underlying movement speed. The
key finding was that GPS units have acceptable validity and reliability for measuring distances in
walking to striding movement patterns (1-3%), but substantially underestimated (by up to 30%) the
true distance of short distance (20 m) sprinting (>5ms-1) movements. Over the last three years the
specified frequency of GPS chip sets in commercially available GPS devices for sporting
applications has increased from 1 to 15 Hz. The increased frequency has improved (and rectified)
many of the earlier limitations in estimations of the distance covered in the short higher speed
movement categories.
Cricket is unique with three different formats of the game played at the elite level (Twenty20, One
Day, Test match). The four studies undertaken make inferences on the demands of modern cricket
in its various forms. These studies provide a comprehensive analysis of movement characteristics
across player positional types, game formats and performance levels. The major findings from
these studies show an international fast bowler would cover ~24 km (with at least 1.8 km of this
distance covered sprinting), during a day’s play (3 x 2 hour sessions) in multi-day cricket.
International cricket had a ~23% greater physical demand than academy level cricket. Multi-day
cricket had a greater overall physical load; the shorter formats (Twenty20 and One Day) were more
intensive per unit of time and had 50-100% more sprinting per hour. Together these outcomes
provide a comprehensive reference resource for cricket conditioning coaches to use in the
prescription of game-specific conditioning and other training or intervention strategies.
Improvements in the fitness of players will require individualised estimates of player’s workload
based on the game format and type of position.
Contemporary training demands were analysed by classifying 28 training drills into three different
types of sessions (i.e. conditioning sessions, game simulations and skill drills). A combination of
GPS, blood lactate and heart rate measurements were employed to quantify the physiological
demand of each of these drill types. Conditioning drills were typically twice as intense as game
simulation and cricket skill drills. The conditioning sessions matched or exceeded the peak game

113
heart rates by ~5%, whereas the skill and simulation drills only replicated game demands for some,
but not all positions. Coaches can use this information to determine the training effect of a
particular type of training drill, activity or session. Depending on the coaches training objective
these data can also be utilised to modify particular training drills to more closely match or exceed
the likely game demands.
Elite cricketers are often required to travel and compete in environmental conditions considerably
more challenging than at home. Two studies were conducted to quantify the effectiveness of two
commonly used adaptation strategies designed to enhance the preparation for playing cricket in
hot/humid environments. A short duration high intensity cycling protocol conducted inside a
portable heat tent provided a novel but practical strategy to induce heat acclimation. The first study
investigated the physiological responses of the acclimation protocol in the heat compared to a
group that performed matched cycle training in moderate conditions. This intervention study was
conducted with Queensland club level cricketers and demonstrated that partial heat acclimation
could be achieved with only four 30-45 min sessions. Physiological responses indicative of heat
adaptation included a reduced submaximal heart rate, reductions in sweat electrolyte
concentrations and athlete perceptions of increased comfort while exercising in the heat. A greater
number of sessions are probably required to elicit meaningful changes (full adaptation) in core
temperature (reduction) and sweat rate (increase).
The second heat study, employed elite cricketers and repeated the acclimation protocol, but this
time the group that performed matched training undertook their sessions in hot conditions (in
Chennai, India). Utilising a logistically challenging parallel groups design, the training for each
group was performed simultaneously in two different countries. The study design allowed a
comparison of elite cricketers undergoing heat acclimation (Brisbane based group) or
acclimatisation (Chennai based group). In practical terms, this study quantified the likely
physiological adaptations that elite cricketers would achieve after four days of heat acclimatisation
or acclimation. The acclimatisation group displayed slightly superior reductions in the concentration
of all three sweat electrolytes compared with the acclimation group which had a substantial
reduction in only one sweat electrolyte (a moderate 17% reduction in sweat chloride). For elite
cricketers undergoing an acclimation program, it is recommended that a greater or longer stimulus
is applied to ensure a more complete adaptation. Logistical decisions regarding travel and
preparation time before a tour to environmentally challenging destinations can now be made with
an evidential basis.
Future research could describe and rank the physiological effect of commonly employed training
practices to provide strength and conditioning coaches with a practical resource to aide in the
prescription of training drills. Current cricket specific strength and conditioning practices should be
evaluated and challenged including the recovery practices, pre-cooling and heat adaptation
strategies utilised by touring cricketers.
Acknowledgements: Cricket Australia, the AIS and the UWA provided a scholarship and
considerable financial support. Funding from the ARC and an award provided by Pam Withers in
memorial of Australian Sport Scientist Prof. Robert Withers are also greatly appreciated. Numerous
individuals have been fundamental to the comprehensive two-year data collection, incorporating all
six state cricket teams, the AIS Centre of Excellence squad and the Australian Test, One Day and
Twenty/20 sides. We express our gratitude to the coaches, athletes and support staff that have
made this possible. International collaboration was required and we would like to especially thank
Mr. Senthilnathan and the MRF Pace Foundation for access to their facilities in Chennai; and also
to Mr. Dav Whatmore and Mr. Paul Chapman of the Board of Control for Cricket in India (BCCI) for
access to the facilities of the National Cricket Academy in Bangalore. Access to the most elite of
athletes often evades sport researchers; making this possible we would like to especially express
our gratitude to Tim Nielsen, Alex Kountouris, Stuart Karppinen, Aaron Kellett and Justin Cordy.

114
What the Experts Think: Fast Bowling Expertise Acquisition and Talent
Development
Elissa Phillips1,2,3, Keith Davids1, Ian Renshaw1 and Marc Portus2
1 School of Human Movement Studies, Queensland University of Technology, Brisbane
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
3 Biomechanics and Performance Analysis, Australian Institute of Sport, Canberra
Correspondence: elissa.phillips@ausport.gov.au
For applied sport scientists charged with developing talented performers an essential requirement
is to identify components contributing to the development and maintenance of expertise. Previous
qualitative analysis has revealed several psychological (e.g., mental focus, goal-setting and self-
evaluation), socio-cultural (e.g. community and family support, cultural influence), physical (e.g.,
strength, height) and environmental (e.g., access to facilities and climate) constraints on successful
Olympian development (Abbott et al., 2005). Open-ended interviews with expert athletes and/or
expert coaches have been used to reveal competencies of elite performers to derive factors
associated with success (Durand-Bush et al., 2002). However, the influence of these factors is
likely to be sport-specific due to different task constraints and the changing nature of the
performer-environment relationship through practice, coaching and competing (Vaeyens et al.,
2008). So far, only one study on expertise acquisition in cricket has been undertaken.
Weissensteiner, et al. (2009) found that development of expertise in cricket batting in Australia may
be facilitated by early unstructured play (i.e. ‘backyard cricket’), a wide range of sport experience
during development, and early exposure to playing with seniors.
In this study we adopted a multi-dimensional framework of expertise acquisition and tapped into
the experiential knowledge of expert players and coaches in an attempt to identify components of
fast bowling expertise in cricket. Currently, the factors contributing to acquisition of expertise in
cricket fast bowling have not been identified in empirical research or from experiential knowledge
of elite performers/coaches. Therefore, the aims of this study were to: a) identify components
considered critical during the development of fast bowling talent; and b), consider relative
weightings of talent components at developmental phases and when bowlers reached ‘expert’
level.
Twenty one, past or present elite cricket fast bowlers and coaches of national or international level
were interviewed using an in-depth, open-ended, semi-structured approach. The athletes consisted
of eleven past or present Australian international, male elite fast bowlers who had taken more than
2,200 international test wickets in over 570 international test matches. Individually they had taken
at least 100 test wickets and bowled in at least 25 test matches. The coaches’ group consisted of
ten individuals with past or present experience at Australian and State level. All semi-structured
qualitative interviews were conducted by the primary researcher and lasted between 40-70
minutes. The interview guide was based on previous expertise and talent development research in
sport (Côté, et al., 2005). Participants were asked about specific factors which they considered
‘markers’ of fast bowling expertise potential. Self reported data were collected without prescribing
categories for describing expertise components. All interviews were transcribed verbatim with
grammatical changes to improve text flow if needed. Data were analysed by the main researcher in
NVivo (QRS NVivo 8). Open coding of each participant’s transcript allowed concepts and themes
to emerge from the data. Ideas or concepts were coded and used to conceptualize categories
and/or sub-categories. Themes expressed by two or more participants were considered significant.
This process allowed categories to be adjusted and refined during analysis, until theoretical
saturation occurred and the themes conceptualized all of the data. In line with previous work
procedures were used to maximize reliability and control research bias through engaging in peer
concept mapping sessions and verification of data by participants (Côté, et al., 2005). For

115
simplification, findings will be discussed in relation to the ‘developmental phase’ along with
attempts to identify potential experts and examine components that contribute to actual expert
performance.
Identification of future experts: Experts believed that a primary requisite for future success was a
true passion for the game. It was felt that only youngsters who demonstrated this love for the game
were likely to continue into the investment phase (Côté, et al., 2002) and put in the hours of
commitment for developing their expertise. Conversely, coaches were cautious about the merits of
early talent identification programmes;
“You can see talent in kids. But to see talent in a twelve year old kid, mate, talent’s one thing but
how far are they going to go is probably parental wishing in that they want their kids to go as far as
they can. I’m of a different view, we’ll just wait and see.”
Rather than waste valuable resources on talent identification, experts suggested that the primary
focus of early development programmes in cricket should be centred on three common emergent
themes of fun and enjoyment (100%), participation (85.7%) and general skill development (71.4%).
Later in development (e.g. after the age of 16-18), fast bowling experts listed a range of factors
believed to be important in identifying expertise potential of young bowlers. Overall, expertise
potential components could be categorised in terms of: a) personality characteristics (e.g. good
attitude, love of the game, motivation and training ethic); b) psychological skills (e.g. dealing with
setbacks, mental toughness); c) physical competencies (e.g. coordination, athleticism); d) technical
skills (e.g. generation of pace, skill control, technique fundamentals); and e) other factors (good
timing, genetics, luck).
Issues with skewed early talent identification programs were identified as reasons for drop out in
fast bowling talent or those perceived to be talent; emphasising different rates of maturation and
development exist across individuals.
“Well I think there are a lot of things going on and it depends on each athlete. I think often it’s very
good young fast bowlers who are successful in terms of how people perceive them. Their results
and so on, are sometimes generated from the fact that they’re actually physically big compared to
the rest of their age group. A couple of things happen with that type of athlete, often, everybody
else catches up to them so they can’t dominate the way they did at a younger age. So they and
possibly the people around them, meaning their parents, generally find that difficult to deal with or
understand. The other side to that is that generally because they have dominated, coaches or
teachers or parents haven’t worried too much about them technically, their skill set, because
they’re going so well and what happens a lot of times is that their skill set remains at that level
where as others, apart from catching up physically in size have also learnt a few other skills along
the way.”
Components of Expertise: Individual differences were an important consideration in fast bowling
expertise due to the existence of different types of fast bowlers and unique components of talent. A
common perception was that experts are able to make up for weakness in some components (i.e.
height) in other components or areas (i.e. physical strength), and that these constraints alter with
development:
“The thing that I will say is that what works for me won’t work for everyone, and that’s very
important. The way that I train, the things that I do prior to a game is what I know works for me as a
person. A guy like Mitchell Johnson might be slightly different; Shaun Tait might be slightly different
again.”
Several reasons were perceived to set experts apart from their peers or talented young bowlers
who do not progress to the elite level. The most prominent issues associated with drop out factors

116
included: a) psychological attributes: self doubt, lack of motivation or drive, general attitude, lack of
dedication, not prepared to do hard work; b) physical constraints: different rates of maturation
(early TID), injury, workload demands, genetics; c) technical factors: lack of athleticism or ability,
lack of fundamental skill; d) other factors: bad timing, limited spots in squads, preference for
participation in other sports (e.g., Australian Rules Football), lifestyle choices (poor diet, alcohol
overuse), lack of support network. Experts spoke of the importance of skill proficiency and having
control of technique as vital to performance. Some underlying physical attributes and fundamental
skills were identified as being required by aspiring fast bowlers (anthropometrics, strength,
coordination etc.) Experiential knowledge recognized that individual differences existed in the ways
that bowlers physically generate pace; some attributes were identified as optimal (such as height
for creating bounce).
“Tall is a must. If you’re not…well its not a written rule but if you’re not over six foot you’re
struggling to become a good fast bowler. There’s been a couple obviously that have done really
well but the real good ones are up around the six three plus mark, just for getting bounce. And
again the guys that are really good are tall and athletic.”
These attributes were described as useful, but not essential, weapons in the pace bowler’s artillery.
However, a baseline level of physical attributes was suggested to not only produce ball velocity
within the range classified as fast bowling (>130km/h), but also to survive the large workloads
placed on elite pace bowlers in all forms of the game. It was noted that workloads (number of balls
bowled per game/ training etc.) increased with level of competition at the elite level.
Conclusions: Experiential knowledge of elite athletes and coaches was investigated to reveal
insights on expertise acquisition in cricket fast bowling. The importance of intrinsic motivation early
in development was highlighted, along with physical, psychological, and technical attributes.
Experts’ insights recognised the importance of appropriately designed talent development
programmes which emphasise individual differences in expert performance. Data also suggested
that the object of such programmes should be talent development, rather than talent selection.
Additionally, drop-out rates in potential experts were attributed to misconceived talent identification
programs and coaching practices, early maturation and physical attributes, injuries and lack of key
psychological attributes and skills. Experiential knowledge of experts in sport revealed a source of
information which could complement empirical knowledge of expertise acquisition to aid in the
design of talent development and high level training programmes.
References
Abbott, A., Button, C., Pepping, G.-J., & Collins, D. Unnatural selection: talent identification and
development in sport. Nonlinear Dynamics Psychol Life Sci 2005; 9(1): 61-88.
Côté, J., Ericsson, K. A., & Law, M. P. (2005). Tracing the Development of Athletes Using
Retrospective Interview Methods: A Proposed Interview and Validation Procedure for Reported
Information. Journal of Applied Sport Psychology, 17(1), 1 - 19.
Durand-Bush, N., & Salmela, J. (2002). The Development and Maintenance of Expert Athletic
Performance: Perceptions of World and Olympic Champions. Journal of Applied Sport Psychology,
14(3), 154-171.
Vaeyens, R., Lenoir, M., Williams, A. M., & Philippaerts, R. M. Talent Identification and
Development Programmes in Sport: Current Models and Future Directions. Sports Med 2008;
38(9): 703-714.

117
How do our ‘Quicks’ Generate Pace? A Cross Sectional Analysis
of the Cricket Australia Pace Pathway
Elissa Phillips1,2,3, Marc Portus1, Keith Davids2, Nick Brown3 and Ian Renshaw2
1 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
2 School of Human Movement Studies, Queensland University of Technology, Brisbane
3 Biomechanics and Performance Analysis, Australian Institute of Sport, Canberra
Correspondence: elissa.phillips@ausport.gov.au
Technique and physical contributions to ball delivery speed in fast bowling have been popular
research topics in sports science. However, a common limiting factor of this work is the level of
expertise of participants and lack of within-bowler investigations (Salter et al., 2007). The
relationship between technique, anthropometry and ball speed has not been comprehensively
investigated among elite fast bowlers. The purpose of this study was to examine the relationship
between technique, anthropometric variables and ball speed using both within- and between-
bowler analyses in a cross section of the Cricket Australia high performance pace pathway.
Thirty, Australian nationally-contracted (NAT, n = 8, age 29.1 + 3.2 yrs), centre of excellence and
emerging (EMG, n = 11, age 20.8 + 3.1 yrs) and junior pace squad (JNR, n = 11, age 17.4 + 0.6
yrs), fast bowlers performed 30 trials of good, short and full length deliveries at match intensity.
Bowling action and coordination were measured from three-dimensional full body movement data
captured using a 22-camera VICON motion analysis system (Oxford Metrics Ltd., Oxford, UK)
sampling at 250 Hz. The University of Western Australia cluster-based model was used to
calculate three-dimensional joint kinematic measures (Lloyd et al., 2000). Full body global and
relative joint angles were measured using the conventions outlined in Portus et al. (2004). Ground
reaction force (GRF) for back foot contact (BFC) and front foot contacts (FFC) were collected at
1000 Hz (Kistler, Amherst, USA). Anthropometric measures were taken (skin folds, girths, and
breadths) following the protocols used by Pyne et al. (2006). A one way analysis of variance with
post hoc Scheffé tests was used to determine any differences in anthropometrics between the
groups. Pearson’s product moment correlation coefficients (r) were calculated to establish the
relationship between selected anthropometric, kinematic, temporal and kinetic parameters and ball
release speed both within individual fast bowlers and between skill groups (significance set at p <
0.05). Correlations were classified in accordance with Hopkins et al. (2000) as follows: r<0.01
trivial, small 0.1 – 0.3, moderate 0-3 – 0.5, large 0.5 - 0.7, very large 0.7 – 0.9, nearly perfect > 0.9.
Table 1: Comparison of selected anthropometrics and correlations to ball speed (r).
JNR EMG NAT Anova
Mean + s r Mean + s r Mean + s r p value
Height (cm) 188.5 + 3.4 -0.30 190.6 + 6.4 -0.45 188.9 + 7.7 -0.42 0.68
Body mass (kg) 83.4 + 5.2 0.37 89.5 + 8.2 -0.33 92.1 + 5.3 -0.38 0.01* b
% Muscle mass (BW) 45.7 + 1.4 0.31 46.2 + 1.8 -0.26 46.6 + 1.4 -0.34 0.01* a,b
% Fat mass (BW) 9.2 + 1.1 0.13 9.9 + 2.8 -0.25 9.4 + 1.8 -0.73+ 0.67
Humerus breadth (cm) 7.3 + 0.3 0.62+ 7.4 + 0.4 -0.49 7.4 + 0.2 -0.27 0.77
Gluteal girth (cm) 57.4 + 3.2 0.41 60.4 + 3.2 -0.10 62.5 + 2.2 -0.33 0.00* b
Chest depth (cm) 18.7 + 2.1 0.60+ 20.9 + 1.4 0.17 22.0 + 1.6 -0.46 0.00* a,b
Ball Speed (km/h) 120.0 + 3.9 - 123.1 + 2.5 - 125.6 + 6.7 - 0.01 *b
* Post hoc comparisons: significant difference (p<0.05) between JNR - EMGa, JNR - NATb, and EMG - NATc.
+ Significant correlations to ball speed (p<0.05)

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Anthropometric Differences and Correlations to Ball Speed: There was no significant difference in
height between groups. The greatest number of statistically significant differences existed between
the NAT and JNR groups. The NAT group had significantly greater body mass, % muscle mass,
gluteal girth, chest depth and bi-acromial distance compared with the JNR group (Table 1). The
EMG group also had significantly greater % muscle mass and chest depth and bi-acromial
distance compared with the JNR group.
Table 2: Group Mean data(s) and Pearson’s product moment correlation coefficients (r) between
selected kinematic, temporal and kinetic parameters and ball release speed.
JNR
EMG NAT
Mean + s r Mean + s r Mean + s r
Kinematics (o)
BFC Shoulder alignment -36.8 + 15.7 -0.20* -32.2 + 11.7 -0.37* -33.4 + 10.7 0.32*
Pelvis alignment -55.3 + 11.5 0.40* -54.8 + 10.3 0.10 -55.8 + 6.1 0.40*
Trunk flexion angle -1.6 + 8.5 -0.25* -5.9 + 9.9 -0.37* -5.7 + 9.9 -0.09
Hip shoulder sep 12.6 + 18.7 0.17* 19.9 + 16.4 -0.18* 9.8 + 19.7 -0.14*
Back knee flexion 44.0 + 12.2 0.40* 38.6 + 11.9 -0.14* 40.8 + 14.3 0.30*
FFC Trunk lateral flexion -16.5 + 5.8 -0.16* -15.3 + 8.4 0.40* -14.7 + 8.2 0.06
Front knee flexion 9.8 + 9.4 -0.12* 5.4 + 7.1 0.11 11.0 + 8.2 -0.42*
BR Trunk lateral flex -26.6 + 6.6 0.43* -26.0 + 7.0 0.03 -31.6 + 4.3 0.33*
Shoulder CR 40.9 + 13.2 -0.30* 40.2 + 11.6 0.15* 39.6 + 11.8 0.30*
Max Front Knee flexion 39.4 + 20.2 -0.16* 22.3 + 17.8 -0.13* 38.6 + 12.1 -0.50*
Back Knee flexion 69.7 + 7.7 0.28* 66.3 + 9.5 0.11 74.4 + 11.9 0.47*
RoM Trunk lat flex 22.3 + 29.0 0.23* 31.7 + 32.4 -0.21* 22.2 + 38.3 -0.33*
(BFC–BR) Trunk flexion 50.8 + 7.6 0.55* 52.4 + 10.1 0.17* 45.5 + 9.0 -0.20*
Hip-shoulder sep 71.3 + 19.4 0.29* 76.9 + 15.3 -0.16 74.7 + 11.9 0.30*
Max Vel Non B Arm (o/s) 742.7 + 107.9 0.08 787.7 + 185.6 0.27* 779.24 + 159.9 0.45*
Run-up Speed Ave (m/s) 5.3 + 0.4 0.42* 5.4 + 0.4 -0.07 5.3 + 0.5 0.34*
Ave last 5m 5.8 + 0.4 0.50* 5.8 + 0.4 -0.17* 5.7 + 0.4 0.52*
Max 6.6 + 0.4 0.38* 6.5 + 0.4 -0.24 6.5 + 0.5 0.54*
Time duration BFC- BR 0.28 + 0.03 -0.18* 0.31 + 0.03 0.19* 0.31 + 0.05 0.04
Kinetics (body weight)
FFC Max Vertical Force 6.1 + 1.6 0.30* 6.3 + 1.3 0.04 7.5 + 1.3 0.34*
Max Braking Force 4.0 + 1.1 0.34* 4.1 + 0.9 0.43* 4.5 + 0.9 0.75*
BFC Max Vertical Force 2.2 + 0.5 0.25* 2.9 + 0.9 0.09 2.7 + 0.6 0.47
Max Braking Force 1.2 + 0.6 0.01 1.7 + 0.7 0.01 1.4 + 0.4 0.20*
(global pelvis orientation, 0o = front on, -90o = side on; knee angle, 0o = full extension, 90o = flexion)
*Significant correlation (p<0.05)
The NAT group produced significantly higher ball speeds than the JNR group (125.6 km/h + 6.7 v
120.0 km/h + 3.9, p = 0.03). These data were similar to the EMG group (123.1 + 2.5). Several large
correlations were found between anthropometric variables and ball speed in the JNR and NAT
groups. Faster ball speed was associated with greater humerus breadth, and chest depth in the

119
JNR group. Conversely, in the NAT group faster ball speed was related to less fat mass (as a
percentage of body mass). No statistically significant correlations between anthropometrics and
ball speed were found in the EMG group.
Technique Differences and Correlations to Ball Speed: In the between-bowler group analysis
several moderate and large correlations were found between technique variables and ball speed
within all groups. In the JNR group increased ball speed was associated with a faster run up,
especially in the last 5 m and a more side-on action at BFC with their knee more flexed. From this
position, faster JNR bowlers increased the level of trunk flexion up to BR and had their trunk more
laterally bent at BR. Faster ball speed in EMG bowlers was correlated with greater lateral trunk
flexion at FFC and higher front foot braking forces. In the NAT group greater ball speed was
associated with being more side on at FFC, more flexion in the back leg, faster non-bowling arm
pull down and higher front foot braking forces. Faster run-ups and less knee collapse at FFC were
also related to faster deliveries.
Within-bowler analysis, where each bowler’s movement solutions are studied separately across all
30 deliveries bowled, showed that run-up speed and knee flexion angle remained related to
bowling faster. Across most variables individual analysis supported group correlations, although it
was able to identify outliers within groups. Inconsistencies in technique factors related to increased
ball speed across bowlers can be explained by difference in technique. For example, bowlers’ front
knee flexion – extension could be separated into those that extended and those that flexed the
knee between FFC and Ball release within groups (Figure 1.), likely having different effects on ball
speed.
-40
-20
0
20
40
60
80
100
120
0 10203040 506070 8090100
% BFC to BR
Angle (degrees)
Figure 1: NAT (blue) and JNR (red) Mean(s) front knee flexion-extension angle
separated into FFC flexion and extensions groups. (0O = full knee extension)
Conclusions: Factors related to JNR and EMG bowlers creating ball speed were not the same as
those for NAT bowlers. To produce greater ball speed National team bowlers maintained a strong
front leg at FFC and hand faster non-bowling arm pull down, while there was only a small
relationship between the same variables in the JNR and EMG groups. Across all groups, greater
ball speed was associated with higher FFC max braking force, the strength of this correlation
increased will skill level. Bowling squads varied in anthropometrics, however generally, technique
factors were more strongly related to ball speed than physical characteristics. Within-bowler
analyses revealed unique movement solutions for generating ball speed in some individuals adding
to the value of using within-group methodologies in biomechanics and motor control research.

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Acknowledgements: The authors would like to acknowledge Cricket Australia funding and Wayne
Spratford, David Pyne, Carl Petersen and AIS Biomechanics scholars for their assistance with data
collection.
References
Hopkins, W. G. (2000). A new view of statistics. Internet Society for Sport Science:
http://www.sportsci.org/resource/stats/.
Lloyd, D. G., Alderson, J., & Elliott, B. (2000). An upper limb kinematic model for the examination
of cricket bowling: A case study of Mutiah Muralitharan. Journal of Sports Sciences, 18, 975-982.
Portus, M., Mason, B., Elliott, B., Pfitzner, M., & Done, R. (2004). Technique factors related to ball
release speed and trunk injuries in high performance Cricket fast bowlers. Sports Biomechanics,
3(2), 263-284.
Pyne, D. B., Duthie, G., Saunders, P. U., Petersen, C. A., & Portus, M. R. (2006). Anthropometric
and strength correlates of fast bowling speed in junior and senior cricketers. Journal of Strength &
Conditioning Research, 20(3), 620-626.
Salter, C. W., Sinclair, P. J., & Portus, M. R. (2007). The associations between fast bowling
technique and ball release speed: A pilot study of the within-bowler and between-bowler
approaches. Journal of Sports Sciences, 25(11), 1279 – 1285.

121
Quantifying Variability within Technique and Performance in Elite Fast Bowlers:
Is Technical Variability Dysfunctional or Functional?
Elissa Phillips1,2,3, Marc Portus1, Keith Davids2, Nick Brown3 and Ian Renshaw2
1 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
2 School of Human Movement Studies, Queensland University of Technology, Brisbane
3 Biomechanics and Performance Analysis, Australian Institute of Sport, Canberra
Correspondence: elissa.phillips@ausport.gov.au
In fast bowling, cricketers are expected to produce a range of delivery lines and lengths while
maximising ball speed. From a coaching perspective, technique consistency has been typically
associated with superior performance in these areas. However, although bowlers are required to
bowl consistently, at the elite level they must also be able to vary line, length and speed to adapt to
opposition batters’ strengths and weaknesses. The relationship between technique and
performance variability (and consistency) has not been investigated in previous fast bowling
research. Consequently, the aim of this study was to quantify both technique (bowling action and
coordination) and performance variability in elite fast bowlers from Australian Junior and National
Pace Squads. Technique variability was analysed to investigate whether it could be classified as
functional or dysfunctional in relation to speed and accuracy.
Methods: Twenty Australian nationally-contracted (NAT) and junior pace squad (JNR) fast bowlers
completed an adapted version of the Cricket Australia bowling skills test (Spratford et al., 2007), in
which they were required to perform 30 trials of good, short and full length deliveries at match
intensity to a right handed batsman (rear projected) on a skills test screen. This test quantifies
accuracy by measuring the bowlers’ ability to hit a target (Figure 1) and ball speed via a radar gun.
Bowling action and coordination were measured from three-dimensional full body movement data
captured using a 22-camera VICON motion analysis system (Oxford Metrics Ltd., Oxford, UK)
sampling at 250 Hz. The University of Western Australia cluster-based model was used to
calculate three- dimensional joint kinematics (Lloyd et al., 2000). Ground reaction force data (GRF)
for back foot contact (BFC) and front foot contact (FFC) were collected at 1000 Hz (Kistler,
Amherst, USA).
Group means of each bowler’s standard deviation (SD) were used to estimate variability of
performance (Fleisig et al., 2009). Paired t-tests were used to compare this variability measure for
eight performance, eighteen kinematic, three temporal and four kinetic variables. Pearson’s
product moment correlation coefficients (r) were calculated to establish the relationship between
performance means and selected technique parameters variability within groups.
Results: The NAT group were significantly better than the JNR group in overall accuracy scores
(48.5% + 8.2 v 37.7% + 11.2, p = 0.03), short accuracy (62.5% + 8.0 v 35.9% + 12.3, p = 0.00) and
significantly higher in all ball speeds measures (overall; 125.6 km/h + 6.7 v 120.0 km/h + 3.9, p =
0.03), (Table 1). The NAT group bowled with more consistent ball speed than JNR (p = 0.04). No
significant differences were noted in how consistently NAT and JNR hit the required target, but this
may be due to the lack of sensitivity in the scoring system (i.e. 0, 25, 50, 75, 90, 100 grid reference
score).
Discussion: Bowling technique was equally consistent in both NAT and JNR groups except for a
difference at back foot contact. The JNR group had significantly greater variability in BFC braking
forces compared to the NAT group (p = 0.02). Although there were similarities in the levels of
variability in technique in juniors and NAT bowlers, differences in performance outcomes were
noted. Higher accuracy scores in the NAT group demonstrated that the JNR group were not able to
complete the same range of tasks with the same level of accuracy or level of control as the elite

122
group. This may have been because they were still developing, exploring and learning the skill as
their technique variability (or consistency) was similar without the same level of accuracy.
Figure 2: Accuracy Scoring System for Short, Good Length and Full deliveries respectively.
Table 1: Performance measure outcomes and variability (mean + s) by group.
Performance Outcomes Performance Variability
JNR Mean NAT Mean JNR SD NAT SD
Overall Ball Speed
(km/h) 120.0 + 3.9 125.6 + 6.7 * 2.8 + 1.0 2.0 + 0.4 *
Full (km/h) 120.1 + 4.2 125.8 + 6.8 * 2.7 + 1.7 2.1 + 0.6
Good (km/h) 120.1 + 4.1 126.0 + 6.7 * 2.3 + 0.7 1.8 + 0.6
Short (km/h) 119.8 + 3.6 124.9 + 6.8 * 2.7 + 1.3 1.7 + 0.6
Overall Accuracy (%) 37.7 + 11.3 48.5 + 8.2 * 38.9 + 3.1 38.3 + 2.1
Full (%) 25.5 + 18.4 31.1 + 9.6 33.2 + 17.4 41.3 + 5.4
Good (%) 51.5 + 19.7 51.8 + 13.4 34.8 + 5.2 33.7 + 5.0
Short (%) 35.9 + 12.3 62.5 + 8.0 * 37.0 + 6.6 33.7 + 3.5
*Significant difference (p < 0.05)
Similarities in technique variability between groups support previous research findings in baseball
pitching which showed similar kinematic consistency in high school and major league pitchers.
However, that study found greater variability in youth pitchers when examining fastball pitchers in
isolation (Fleisig et al., 2009). However, Fleisig and colleagues (2009) did not assess the
relationship between technique variability and accuracy outcomes across these skill levels.
Several correlations were found between technique variability and performance measures (ball
speed and accuracy) within groups. Faster ball speed was associated with greater variability in
trunk lateral flexion at ball release (BR) in the JNR group, and greater variability in maximum front
knee flexion in the NAT group. Greater accuracy was related to lower variability in the average run-

123
up speed in the last 5m in the JNR group and less variability in trunk flexion RoM, and less
variability in maximum angular speed of the non bowling arm in the NAT group.
Table 2: Technique variability parameters by group (mean + s) and their Pearson’s correlation (r)
to overall performance outcomes (ball speed and accuracy scores).
Technique Variability (SD) r Ball Vel r Accuracy
JNR NAT JNR NAT JNR NAT
Kinematics (o)
BFC Shoulder alignment 3.2 + 1.4 2.5 + 0.6 0.35 -0.48 0.07 -0.06
Pelvis alignment 3.0 + 0.8 2.5 + 0.7 0.10 -0.14 0.07 0.01
Hip shoulder sep 2.5 + 0.8 2.2 + 0.6 0.20 0.44 -0.31 -0.51
Back knee flexion 5.0 + 1.3 4.4 + 1.6 -0.10 0.54 -0.05 -0.25
FFC Trunk lateral flexion 2.8 + 1.0 3.2 + 1.6 0.17 -0.21 0.27 0.11
Front knee flexion 2.8 + 0.9 2.7 + 1.4 0.06 0.05 -0.23 0.17
BR Trunk lateral flexion 2.3 + 0.6 2.4 + 1.2 0.75* 0.13 -0.11 -0.29
Shoulder counter rotation 3.1 + 0.9 2.7 + 0.8 0.39 0.03 0.07 -0.18
Max Trunk lateral flexion 1.6 + 0.6 3.0 + 2.7 0.53 -0.61 0.20 -0.22
Front Knee flexion 5.0 + 1.9 4.4 + 2.3 0.37 0.71* 0.17 -0.16
Back Knee flexion 2.8 + 0.8 2.4 + 0.6 0.30 -0.43 -0.34 0.62
RoM (BFC–BR) Trunk lat flex 2.3 + 0.6 2.7 + 1.2 0.27 -0.25 -0.37 -0.61
Trunk flexion 3.2 + 1.0 3.0 + 1.0 0.29 0.26 0.20 -0.90*
Hip-shoulder sep 3.6 + 1.0 3.4 + 1.5 0.43 -0.11 -0.27 -0.70
Max Vel Non B Arm (o/s) 54.6 + 29.0 69.5 + 72.8 0.53 0.43 0.08 -0.73*
Run-up Speed Ave (m/s) 0.15 + 0.03 0.15 + 0.05 0.02 0.06 -0.20 -0.35
Average last 5m (m/s) 0.14 + 0.04 0.14 + 0.05 0.31 0.51 -0.64* -0.33
Max (m/s) 0.14 + 0.03 0.14 + 0.15 0.08 0.28 -0.14 -0.01
Temporal (s)
Time duration BFC - BR 0.01 + 0.003 0.01 + 0.003 -0.13 0.22 0.23 -0.45
Kinetics (body weight)
FFC Max Vertical Force 0.93 + 0.55 0.76 + 0.25 -0.07 0.47 -0.05 -0.24
Max Braking Force 0.70 + 0.35 0.50 + 0.23 -0.04 -0.52 -0.23 -0.08
BFC Max Vertical Force 0.30 + 0.20 0.27 + 0.12 0.34 0.65 -0.19 -0.31
Max Braking Force 0.25 + 0.11 0.13 + 0.05** 0.37 0.31 -0.01 -0.12
** Significant differences (p < 0.05) among the competition levels.
* Significant correlation at p < 0.05
The sensitivity of the accuracy performance measure (and scores) will be addressed in future
research. In this planned work more detailed quantification of accuracy is proposed: the distance
(cm) between where the ball hits the target and the desired target (100% score) will be quantified
for each delivery. The increased precision of this measure is expected to produce more meaningful
correlations between accuracy and technique variability.

124
Conclusions: Similarities in technique variability, but significantly higher accuracy scores in the
NAT group, revealed that elite fast bowlers may have a wider repertoire of movement solutions to
achieve desired performance outcomes. The same small technique adjustments were not
harnessed as successfully in the JNR group, specifically within the short delivery type. The
variability of several technique parameters (unique to each group) were found to be associated to
greater ball speed, conversely consistency in a few technique variables was found to be correlated
to accuracy in these same groups. Future research will examine more sensitive measures of
accuracy and individual analysis of specific delivery types is advocated to better understand unique
differences in movement coordination in elite and developing elite fast bowlers.
Acknowledgements: The authors would like to acknowledge Cricket Australia funding for this
project and Wayne Spratford and the AIS Biomechanics and Performance Analysis scholars for
their assistance with data collection.
References
Fleisig, G., Chu, Y., Weber, A., & Andrews, J. (2009). Variability in baseball pitching biomechanics
among various levels of competition. Sport Biomechanics, 8(1), 10-21.
Lloyd, D. G., Alderson, J., & Elliott, B. (2000). An upper limb kinematic model for the examination
of cricket bowling: A case study of Mutiah Muralitharan. Journal of Sports Sciences, 18, 975-982.
Spratford, W., Thomlinson, N., Denver, E., & Portus, M. (2007). An overview of pace bowling skills
test results of elite male cricketers during 2006. In M. Portus (ed.) Cricket Australia Sport Science
Sport Medicine Conference Proceedings 2007, Cricket Australia Centre of Excellence, Brisbane.

125
A Batting Skills Test to Assist the Development of Elite Cricketers
Marc Portus1, Stephen Timms1, Wayne Spratford1,2, Nadine Morrison1,2, Rian Crowther1,2
1 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
2 Biomechanics & Performance Analysis, Australian Institute of Sport, Canberra
Correspondence: mportus@coe.cricket.com.au
The skill of batting in cricket is an intricate combination of mental, physical, perceptual, technical
and tactical prowess (Weissensteiner et al., 2008). One important aspect of cricket batting,
particularly in the shorter forms of the game, is the ability to play strokes into gaps in the field,
despite the type of delivery bowled. A batting skills test has been developed at the Cricket Australia
Centre of Excellence (COE) to quantify the physical execution aspect of batting skill. The test has
been implemented to: 1. Establish performance benchmarks for batters in the high performance
pathway, and 2. Provide coaches and batters a profile of their strengths and weaknesses so an
individualised skill development program can be developed. The purpose of this study was to
assess if the Junior, Emerging and Elite groups of players performed significantly differently on the
test, and if so, what aspects of the test could discriminate their expertise level.
Methods: The batting skills test was undertaken by 124 players, providing a total of 343 test
results. Testing took place at the COE and used a synthetic centre wicket and cricket field. The aim
of the batting skills test was to measure the physical skill of hitting the ball to a predetermined order
of targets (gaps) in the field when facing balls of various lengths and speeds. The test was
administered within an implicit paradigm, in that players received no explicit instructions on what
technique or tactics to employ. Rather, they were encouraged to develop their own movement
solutions to hit the ball into each target area. The test attempted to replicate the three lengths the
batter would typically experience during a match (full, good and short), at two speeds: 110km/h
and 130km/h. Targets were positioned in key areas replicating common scoring areas or “gaps” in
the field in a real match context (figure 1).
The test started with full length deliveries at 110km/h and batters were asked to hit seven
deliveries from a ball machine to the seven targets sequentially, commencing at Mid Off and
moving clockwise to finish at Wide Mid On (figure 1). The test then continued to the good length
delivery at 110km/h, short delivery at 110 km/h, full delivery at 130 km/h, good length delivery at
130 km/h and concluded with short length deliveries at 130km/h. On most testing occasions time
permitted batters to attempt 2 rounds of each condition (i.e. twice around the ring at Full 110 km/h,
then twice around the ring at Length 110 km/h, and so on). Batters faced six practice trials each
time the test moved to a new length condition.
Each target had 4 cones. A score of 4 was awarded when the ball travelled through the middle 2
cones of the nominated target and a score of 1 if passing through the outer 2 cones (figure 2). A
0.5 competency score was awarded if the batter executed a well crafted shot and the ball travelled
near the targets, but not through them. This was a subjective scorer assessment and all scorers
were trained in the assessment of a 0.5 score to ensure consistency. This measure was used to
provide more sensitivity in the scoring system.
All participating players were from the CA high performance pathway which allowed them to be
classified into 3 groups: Elite (CA contracted players; n = 27; mean age 31 ± 3 years), Emerging
(AIS and COE scholarship holders; n = 216; mean age 22 ± 3 yrs), and Junior (National U18
Talent Camp and U19 Australian Representatives; n = 100; mean age 18 ± 1 yrs). To assess if
these groups performed significantly differently a one-way analysis of variance (ANOVA) with a
Scheffe post hoc test was used (SPSS v18.0).

126
Figure 1: Overhead view of the batting skill test target layout for a right handed batter.
Figure 2: Picture of the cone layout at each target zone. The stump in the middle
served as a visual reference for the centre of the target zone.
Variables assessed ranged from a single target zone (e.g. third man 110km/h), to combined scores
for the off and leg side, scores for each length, scores for each speed, the total number of 4’s hit,
plus the overall skills test score. When all the various combinations of conditions were derived a
total of 113 variables were assessed. Homogeneity of variance was assessed using the Levene
statistic, an alpha level of ≤0.05 meant the homogeneity assumption was considered violated (i.e.
group variances were significantly different) and the Brown-Forsythe F and Tamhane T2 post hoc
test were used. Due to the large number of multiple comparisons, the relatively large sample size,
and hence increased statistical power and chances of a type II error, a more conservative p ≤
0.01significance level was set for the ANOVA phase of the analysis.
4 metres 1 metre 1 metre
1 run 1 run 4 runs 4 runs
Stump
(target)
0.5 runs 0.5 runs
Backward
Square Leg
Deep Mid
Wicket
Wide
Mid On
Mid
Off
Cover
Backward
Point
Batter (RH)
Ball Machine, DV Camera
& Radar
Net/Goal
Scorer
Third Man

127
Variables that were significantly different in the ANOVA were used in a Discriminant analysis to
ascertain the ability of the test, or components of it, to classify each player’s group status (Elite,
Emerging or Junior). Discriminant analysis was a useful statistical procedure as it identified the
least number of batting skills test variables providing the most predictive power of a batters group
status. This was considered important as: 1. It provided insight into whether a certain sub-
component of the test was more important to identify skilled or lesser skilled players (e.g. their
grouping on the CA pathway) and, 2. It allowed the research team to identify if a reduced version of
the skills test would have a similar diagnostic assessment of batting skill as the complete test. The
Mahalanobonis D2 method was used as it is the preferred measure for deriving a stepwise solution
for groups with unequal variances (Hair et al., 2006). The leave-one-out cross-validated
classification results from the Discriminant analyses are presented as a simple % of the players
that were correctly classified into their respective group (SPSS v18.0).
Results: Thirteen variables were significantly different between groups, with Total 110 km/h, Total
4’s and Total Score being significantly different between all 3 groups (table 1). The Elite and
Emerging groups both performed significantly better than the Junior group on Total 130km/h and
Total Legside and the Elite group scored significantly higher than the other groups on Total Length
110 km/h, Total Short 130km/h, Total Offside, Total Short Length, Total Mid-Off, Total Backward
Point and Total Wide Mid-On (table 1). These variables were entered into the blocked enter
method and forward stepwise Discriminant analyses. The blocked method produced an 11 variable
model to yield a 59% cross-validated classification rate (table 1). The forward step-wise
Discriminant analysis used only 2 variables - Total Score and Total 4’s Hit – to produce a 62%
cross-validated classification rate (table 1).
Discussion: The overall results indicate that the Elite group performed best on the batting skills
test, which indicates that the test does measure a component of batting skill. The Emerging groups
results often fluctuated between either end of the representative spectrum, suggesting they
displayed traits of elite players in some aspects of the tests (e.g. Total Full Length) and of junior
developing players in others (e.g. Total Short 130km/h). Theoretically this makes sense as talent
development is a non-linear process with unique sets of intrinsic dynamics contributing to individual
pathways to sporting expertise (Phillips et al., 2010). It would appear players at the Emerging level
were still in the process of developing aspects of their batting skills in their journey to becoming an
Elite player. The Junior group was the least successful at developing solutions for the demands of
the test. When considering traditional cricket training programs this seems logical, as many of
these junior players would not have had the exposure to attempt these shorter game format skills,
for example, hitting a 130 km/h bouncer length ball back through mid-off. The 2 more senior groups
were more likely to have been exposed to such challenges in their careers, plus may have more
refined motor skills allowing them to adapt to new challenges and develop solutions more
efficiently.
As with most skills tests, there are some limitations to be considered. Cricket batting is known to be
an intricate combination of mental, perceptual, and technical skill. Perceptual anticipatory skills
have been shown to be significantly different between expertise levels of cricket batters (e.g. Muller
et al., 2006; Weissensteiner et al., 2008). Similarly this study demonstrates that there are physical-
technical execution skills significantly different between elite and developing batters. Both research
domains have decoupled these perceptual and technical skills to produce these findings. As such
the research team recognizes that this test only evaluates a limited dimension of cricket batting. It
would be an interesting next step to conduct a more multidisciplinary and multivariate analysis of
the relative contributions of these different aspects of cricket batting and how they discriminate
between skilled and lesser skilled players.

Table 4: Statistical comparisons and discriminant analysis results for batting skills test results by pathway group
a Data for these variables violated homogeneity of variance assumption so Borwn-Forsythe F and Tamhane T2 post hoc are presented.
Variable Group 1
Elite
Group 2
Emerging
Group 3
Junior F P
Scheffe
post hoc
Blocked
Discriminant
Analysis
Variables
Cross Validated
Classification
Rate
Stepwise
Discriminant
Analysis Variables
Cross Validated
Classification
Rate
Total 110 km/ha 21.78±7 17.04±7.87 14.89±5.85 11.343 <.01
1v2 (.01)
1v3 (<.01)
2v3 (.02)
1
Total Good Length 110km/h 7.04±3.57 4.56±3.55 3.88±3.24 8.814 <.01
1v2 (<.01)
1v3 (<.01) 2
Total Short130km/h 7.61±3.64 5.42±3.85 4.72±3.37 6.652 <.01
1v2 (.01)
1v3 (<.01) 3
Total 130km/h 15.8±6.35 12.93±6.64 10.6±5.91 8.448 <.01
1v3 (<.01)
2v3 (.01) 4
Total Legside 14.13±6.98 11.34±6.06 8.98±5.01 10.07 <.01
1v3 (<.01)
2v3 (<.01)
Total Offside 23.44±7.03 18.63±8.56 16.51±6.99 8.199 <.01
1v2 (.01)
1v3 (<.01) 5
Total Full Length 10.04±4.46 9.43±5.6 7.54±4.48 5.209 <.01 2v3 (.01) 6
Total Short Length 16.96±6.63 12.54±6.46 11.3±5.52 8.84 <.01
1v2 (<.01)
1v3 (<.01) 7 59.2%
Total Mid Off 6.82±3.77 4.85±3.65 4.58±3.26 4.345 .01 1v2 (.03)
1v3 (.02) 8
Total Backward Point 6.5±2.88 4.73±3.48 4.22±3.4 4.748 .01 1v2 (.04)
1v3 (.01) 9
Total Wide Mid-On 7.15±4.09 5.07±3.68 4.31±3.63 6.350 <.01 1v2 (.02)
1v3 (<.01) 10
Total 4’s Hit a 6.85±2.4 5.3±2.75 4.51±2.12 10.932 <.01
1v2 (.01)
1v3 (<.01)
2v3 (.02)
11 1
Total Score a 37.57±10.28 29.97±12.01 25.49±9.11 16.219 <.01
1v2 (<.01)
1v3 (<.01)
2v3 (<.01)
2
62.1%

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Conclusion: Results suggest that the entire test was a useful exercise to measure a players’
batting skill. The stepwise Discriminant analysis used only Total Score and Total 4’s Hit to
successfully classify 62% of the players, whereas the 11 variable blocked enter method was only
59% successful. It appears the test as a whole was the best format to discriminate between
players’ skill development (group), which indicates the test was a valid measure of batting skill.
From this preliminary statistical analysis it would seem there are no sub-components of the test
that could be used in a smaller version of the test with a similar diagnostic insight into a players
batting skill level as the test in its entirety. However we are conducting other statistical analyses
such as Factor Analysis to further investigate possible reductionist outcomes of the test, which
could be useful in talent search exercises.
Acknowledgements: The authors would like to thank the following people who contributed to the
development of the batting skills test and/or assisted with data collection: Eliot Denver, Elissa
Phillips, Carl Petersen, Damian Farrow, Brian McFadyen, Jamie Siddons, Tin Nielsen, Dene Hills,
QUT School of Human Movement Studies work experience students.
References
Hair, J., Black, W., Babin, B., Anderson, R. and Tatham, R. (2006). Multivariate Data Analysis. 6th
ed. Upper Saddle River, New Jersey: Pearson Education.
Muller, S., Abernethy, B., and Farrow, D. (2006). How do world-class cricket batsmen anticipate a
bowler’s intention? The Quarterly Journal of Experimental Psychology, 59 (12), 2162-2186
Phillips, E., Davids, K., Renshaw, I. and Portus, M. (2010). Expert Performance in Sport and the
Dynamics of Talent Development. Sports Medicine, 40 (4), 271-283.
Weissensteiner, J., Abernethy, B., Farrow, D. and Muller, S. (2008). The development of
anticipation: A cross sectional examination of the practice experiences contributing to skill in cricket
batting. Journal of Sport and Exercise Psychology, 30, 1-23.

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The Utility of a Bowling Skills Test to Assist Developing Fast Bowlers
Marc Portus1, Stephen Timms1, Wayne Spratford1,2, Nadine Morrison1,2, Rian Crowther1,2
1 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
2 Biomechanics & Performance Analysis, Australian Institute of Sport, Canberra
Correspondence: mportus@coe.cricket.com.au
Skilled bowlers are defined by their ability to execute their skills in a variety of environments and
conditions. For pace bowlers the ability to bowl bouncer/short length, good length and yorker
length deliveries is considered a fundamental and key aspect of the skill. This is particularly
pertinent to the shorter forms of the game where the potential margin of error decreases. The
Cricket Australia Centre of Excellence has designed and implemented a bowling skills test to
measure the ability to execute the three lengths (yorker, good length and bouncer/short), on two
lines (left and right handed batter), replicating common tasks bowler’s need to accomplish in match
contexts. The test has been implemented to: 1. Establish performance benchmarks for young fast
bowlers in the high performance pathway; and 2. Provide coaches and young fast bowlers a profile
of their bowling performance strengths and weaknesses so an individualised skill development
program can be developed.
Methods: Testing was conducted over a four year period at the Cricket Australia (CA) Centre of
Excellence (COE). Seventy five (75) male players who were part of the CA high performance
pathway completed the test; these consisted of 14 Elite (CA contracted players; mean age 27 ± 4
years), 43 Emerging (AIS and COE scholarship holders; 23 ± 4 years) and 18 Junior (Australian
U19 squad and National U18 Talent Camp; 17 ± 1 years) bowlers. Players were briefed on the
purpose and protocol of the test and asked to warm up as per their usual routine so they could
bowl at match intensity. The test required bowlers to bowl a match simulated 4-over spell, often
they did this in pairs alternating overs. The bowler was instructed to hit one of six tester nominated
grids on a target positioned on the batters crease (figure 1). The test was a 4-over spell (24 balls)
which resulted in the bowler having 4 attempts at each of the 6 target grids. The order of target
grids was standardised for all bowlers; each over required the bowler to aim at a different target
grid for each delivery. A scaled scoring system was used where 100 points were awarded for a
direct hit of the nominated grid with reduced points awarded (90, 75, 50, 25 or 0) when the ball hit
a grid hit further away from the target grid (for an illustration of this scoring system see Phillips et
al., 2010, in these proceedings). The reduction in score was greater on the leg side of the target to
replicate the lower margin of error a leg side delivery would have when bowling to a high quality
batter in a match. Ball release speed was measured using a calibrated radar gun (Stalker ProTM,
USA) aiming down a line of best fit of the ball’s flight. Each bowler had their mean and maximum
ball speeds analysed. Testing was conducted outdoors on a Turf centre wicket at the COE.
Each bowler received an overall accuracy score for the 4-over spell, and then 3 sub-scores for the
bouncer/short length, good length and yorker length; a sub score for each of the 2 lines to a right
and left handed batter, and scores for a combination of these variables. This produced 12 accuracy
variables for each bowler. These were presented as percentage scores (raw score divided by the
total possible score multiplied by 100). A one way analysis of variance (ANOVA) with a Scheffe
post hoc test was used to assess which of these variables where significantly different between the
Elite, Emerging and Junior groups (p < 0.05). Variables that were significantly different in the
ANOVA were used in two discriminant analysis procedures (blocked enter and forward stepwise)
to ascertain the test results ability to classify what part of the high performance pathway the player
belonged to (Elite, Emerging or Junior). Discriminant analysis was a useful statistical procedure in
this study for 2 reasons. Firstly, it provided insight into whether the test, or a certain sub-
component of it, was characteristic of a certain skill level (e.g. the Elite, Emerging or Junior Group).
Secondly, it allowed the research team to determine if a reduced version of the skills test (fewer

131
variables) could be used in the future and still provide a useful insight into a bowler’s skill level.
Cross validated classification results from the Discriminant analysis are presented (SPSS, v18.0).
Figure 3: Image of the testing setup (Left) and the vertical grid accuracy target (Right).
Results: All groups found the bowling skills test difficult with no group averaging more than 50% in
total accuracy (Table 1). The best 10 all time performers scored between 62 - 75%, indicating that
high scores were possible, but they were not common.
Table 1: Bowling skills test results (% mean ± standard deviation) for the three groups
Variable Group 1
Elite
Group 2
Emerging
Group 3
Junior F P
Scheffe
post hoc
Right Yorker Accuracy (%) 47 ± 17 42 ± 30 21 ± 14 5.737 .01 1v3 (.016)
2v3 (.014)
Left Yorker Accuracy (%) 40 ± 28 38 ± 24 1 ± 2 7.578 <.01 1v3 (.003)
2v3 (.002)
Overall Yorker Accuracy (%) 44 ± 16 39 ± 22 17 ± 15 9.554 <.01 1v3 (.003)
2v3 (.001)
Right Short Accuracy (%) 41 ± 20 48 ± 20 35 ± 16 3.318 .04 2v3 (.050)
Total Right Accuracy (%) 46 ± 11 47 ± 14 34 ± 10 6.765 <.01 1v3 (.036)
2v3 (.003)
Total Left Accuracy (%) 47 ± 13 45 ± 15 23 ± 16 7.755 <.01 1v3 (.003)
2v3 (.002)
Overall Accuracy (%) 46 ± 10 46 ± 13 33 ± 11 8.683 <.01 1v3 (.008)
2v3 (.001)
Ball speeds were not statistically different between the groups (Mean ± SD: Elite 128.3 ± 4.3 km/h,
Emerging 126.5 ± 3.3 km/h, Junior 126 ± 4.5 km/h). ANOVA revealed seven accuracy variables
that were significantly different across the skill groups (Table 1). The Elite and Emerging groups
scored significantly higher than the Junior group in all these variables except for the Right Short
Accuracy sub-total where only the Emerging group scored higher than the Junior group.
Four of these seven variables were used in the blocked discriminant analysis (Right Yorker
Accuracy, Right Bouncer/Short Accuracy, Total Right Accuracy, Overall Accuracy) to yield a 65.6%
cross-validated classification rate. In other words, using the results for these 4 variables, the
LY / RY – Left and Right Yorker
LG / RG – Left and Right Good length
LB / RB – Left and Right Bouncer/short

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Discriminant analysis could successfully predict which group the bowlers belonged to in 66% of
cases. The forward step-wise method produced 1 variable classification model using the Overall
Yorker Accuracy (%) variable to yield a 56% cross-validated classification rate (table 2).
Table 2: Results from the blocked and stepwise Discriminant analyses.
Variable
Blocked
Discriminant
Analysis
Variables
Cross
Validated
Classification
Rate
Stepwise
Discriminant
Analysis
Variables
Cross
Validated
Classification
Rate
Right Yorker Accuracy (%) 1
Left Yorker Accuracy (%)
Overall Yorker Accuracy (%) 1 56%
Right Short Accuracy (%) 2 65.6%
Total Right Accuracy (%) 3
Total Left Accuracy (%)
Overall Accuracy (%) 4
Discussion: This analysis was carried out on four years of bowling skills test data collected at the
COE. Of all the speed and accuracy scores recorded during the test, no component of the test was
significantly different between each and every group within the CA high performance pathway
(Junior v Emerging v Elite). The common pattern was that the Elite and Emerging groups both
scored significantly higher than the Junior group, with the majority of these differences attributable
to better execution of the Yorker length deliveries. These results suggest that the ability to bowl
yorkers was an area where the Junior bowler’s skill set were underdeveloped relative to their
senior fast bowling counterparts. The Yorker is probably not critical to success in junior cricket but
these skills test results and a simple evaluation of any high quality fast bowler suggests it is an
important part of the fast bowler’s armory in the more elite levels of cricket. It should be noted that
while the Elite group did perform best on the yorker length, as a group they still only recorded a
mean ± SD of 44% ± 16%, so it would seem there was still plenty of improvement possible for the
elite bowlers.
This study suggests that the overall bowling skills test does provide useful information to coaches
about a young fast bowler’s skill set and their ability to execute the 3 lengths. The test serves to
highlight strengths and weaknesses in bowlers, which in turn provides coaches with an evidenced
based tool to help guide their coaching programs. The ability to classify bowlers correctly 66% of
the time, based on 4 accuracy variables, including the overall accuracy score, highlights the
importance of control and accuracy. A 66% cross validated classification rate is a moderate, rather
than a strong, result. The fact that the Overall Yorker score was the sole variable used by the
stepwise discriminant analysis illustrates that the Yorker length was the sub-component of the test
that best differentiated fast bowling skill expertise levels. If conducting a reduced version of the test
with significant time constraints – e.g. for mass talent identification purposes - the Yorker length
would be the part of this test to use.
The test does have its limitations, which should be noted. The context of the bowling skills test is
not entirely task representative, in that there is no batter, wicketkeeper, slips cordon, crowd and
match context. This may serve to remove some important cues, motivational and arousal factors
for bowlers. For example one elite bowler stated that he focussed on the batters feet in a match to
execute the yorker length, a cue which had been removed in the test. The ecology of the test does
not represent a true match environment as the contest and atmosphere of international cricket is
not replicated, despite the testers’ best efforts to encourage bowlers. This undoubtedly has an

133
effect on motivation, particularly with the Elite bowlers. This may help explain this group not
bowling significantly faster than the other groups. Further work is ongoing at the COE to improve
the task representation of the bowling skills test (e.g. Phillips et al., 2010).
In summary, the ability to accurately and consistently bowl a specified delivery on command in the
bowling skills test was a difficult task, even for elite bowlers. The bowling skills test resulted in
significantly different scores between the Junior group and the Elite and Emerging groups, mostly
due to the Junior group’s lesser ability to execute the Yorker length delivery. Despite the limitations
of the test, results indicate that the bowling skills test is a useful tool to measure, analyse and help
develop the skill sets of young fast bowlers.
Acknowledgements: The authors would like to thank the following people who contributed to the
development of the bowling skills test and/or assisted with data collection: Eliot Denver, Elissa
Phillips, Carl Petersen, Damian Farrow, Brian McFadyen, Troy Cooley, QUT School of Human
Movement Studies work experience students.
References
Phillips, E., Portus, M., Davids, K., Brown, N., Renshaw, I. (2010). Quantifying variability within
technique and performance in elite fast bowlers: Is technical variability dysfunctional or functional?
In M. Portus (Ed.) Conference proceedings from Conference of Science, Medicine & Coaching in
Cricket, Cricket Australia, Brisbane.

134
The Use of the Total Quality Recovery Model in Determining Optimal
Training Loads and Recovery Periods
Gregory Reddan
School of Physiotherapy and Exercise Science, Griffith University, Gold Coast
Correspondence: g.reddan@griffith.edu.au
As the pressure to win increases in sporting competitions, athletes frequently increase training
workloads and experience higher levels of stress, possibly leading to performance deterioration
when recovery is insufficient. One of the problems confronting coaches is that one athlete’s
overtraining might be another athlete’s optimal training load. A crucial relationship exists between
training and recovery. Coaches need to understand the importance of this relationship and develop
a co-operative approach to achieve optimal results. Each athlete needs to be viewed as a
psychosociophysiological system (Kentta and Hassmen, 1998). Therefore, coaches need to
monitor the psychosocial influences as well as the physiological. The super-compensation principle
suggests that training followed by sufficient recovery will produce an improvement in performance
capacity (Budgett, 1990). However, methods to monitor recovery are still scarce (Kellman, 2002).
Coaches need a system that will organize training and recovery in a structured and efficient
manner.
The Total Quality Recovery (TQR) Model was developed by Kentta and Hassmen (1998) and
focuses on the individual’s subjective perception of recovery. The model should be used mainly to
detect intra-individual changes, rather than comparing the recovery of different individuals, thus
individualising recovery management. The TQR Model is based on recovery points accumulated
over 24 hours. These are allocated accordingly:
Nutrition & hydration 10 points
Sleep and rest 4 points
Relaxation & emotional support 3 points
Stretching & warm-down 3 points
There are numerous benefits to be gained through the use of TQR. Primarily, there is a reduced
risk of overuse injuries and infections, thereby reducing the loss of training days and associated
training effects. Secondly, TQR accelerates recovery by active measures taken by the athlete.
Thirdly, the model indicates a state of under-recovery (very common in high performance athletes)
and is a marker for early staleness. A total of 20 recovery points is the maximum score possible
each day. Previous use indicates that recovery becomes inadequate around 13 points or less.
Swimmers and kayakers have been well-recovered and ready to perform at 17 points.
Table 1 indicates the scoring of recovery points. The athlete is required to make a subjective
decision on the number of points scored in relation to each aspect. Half points can be used.
Athletes need to be educated about appropriate choices for meals, snacks and levels of hydration
before they can evaluate their eating and drinking behaviours.
Six members (three males and three females) of Southport Surf Lifesaving Club (Queensland)
aged 15-27 participated in this study. Two were Exercise science students at Griffith University.
The subjects completed a logbook, which included the Total Quality Recovery Model, for a two
month high volume training period. All subjects showed prior awareness of the nutrition and
hydration aspects of recovery and sleep. However, only 17% demonstrated awareness of the
importance of a power nap in recovery. 67% were aware of the need for mental and muscular
relaxation after training, whilst only 33% showed an understanding of psychosocial recovery and
relaxation after training. All athletes were aware of the importance of proper warm-downs and

135
stretching of exercise muscle groups before the study commenced. After two months, the subjects’
responses were sought as to the effects of their use of the recovery model on a range of features
demonstrated in table 2.
Table 1: Scoring Recovery points
Nutrition and hydration Recovery Points Score
Excellent breakfast 1RP
Excellent lunch 2RP
Excellent dinner 2RP
Excellent between meal snacks 1RP
Fast carbohydrate refuelling 2RP
Adequate water to maintain fluid balance 2RP
Sleep and rest
High quality sleep 3RP
Power nap 1RP
Relaxation & emotional support
Mental/muscular relaxation after training 2RP
Psychosocial recovery/ mentally relaxed 1RP
Stretching and warm-down
Proper warm-downs 2RP
Stretching all exercised muscle groups 1RP
Total 20RP
Table 2: Perceived effects of using TQR
On a scale of 1-10 (1 being very poor and 10 being outstanding), how has the
recovery model:
Mean Range
Improved your performance? 6.0 5-8
Improved your recovery from training? 7.8 6-9
Improved your energy levels? 7.0 5-9
Reduced your amount of sickness? 5.8 5-8
Helped determine your optimal training load? 6.0 5-9
The subjects were also asked to record perceived strengths and weaknesses of the model. (The
number of similar responses is included in brackets). The most common perceived strength was
that it “reminds you each day of all aspects of recovery” (5). Other popular comments were “shows
areas of recovery you need to work on” (3), “easy to complete” (3); and “strong emphasis on food/
nutrition” (2). On the other hand, the perceived weaknesses were seen as “needs to be completed
daily, even on rest days” (2); “not enough emphasis on warm-down and stretching”(1); and “need
to hand in weekly to receive feedback from the coach”(1). The subjects were also questioned as to
which aspects of recovery they had tried to improve after using the model. 67% suggested that
they had sought to improve their nutrition and hydration; 83% tried to achieve high quality sleep;
50% indicated they included a power nap as part of their training; 83% placed greater emphasis on
mental/ muscular relaxation and psychosocial recovery after training; and 67% stated that made a
greater effort to improve warm-downs and stretching of exercise muscle groups after training.
When asked whether they would use the model on a daily basis in the future, 83% of the subjects
provided a positive response with reasons such as “gets you into the habit of recording your
recovery”; “made me aware of the steps for optimal recovery”; and “gives you a good honest

136
assessment of how you recover”. The athletes suggested that “you can look back to see what
worked well and what didn’t” and “you can spot trends and then be more aware of your body”. The
subjects also indicated changes they have made to their training patterns or workload since using
the model. The most common responses were that they were “more conscious of recovery”; “tried
to improve the quality of their diet”; “organized a fast intake of food at the completion of training”;
“wanted to include power naps”; “increased stretching”; and “reduced stress through the day so
that I sleep better”.
Table 3 indicates the scores obtained on the TQR scale in weeks 1 and 8. The changes from week
1 to week 8 were significant (t=-4.521, p=0.006) with an average improvement of 1.3 Recovery
Points or 8.9%. As the study was conducted over such a short time-frame, these improvements
suggest that the use of the TQR scale is worthy of consideration for coaches and athletes.
Table 3: Scores obtained using the TQR scale
Week 1 Week 8 Improvement
Mean 14.6 15.9 1.3
Range 8-19 9-19 0.1-2.5
Common problems experienced in the study were a failure to record resting heart rate, body weight
and general dietary information on a daily basis. The small sample size was obviously a limitation.
It would be useful to examine the effects of use of the scale over a longer period of time (6-12
months). The scale would probably be more effective with high performing athletes focused on
improving their performance.
Although this study involved surf lifesavers, the use of the TQR model is relevant to all sports. Surf
lifesavers mainly compete in aerobic events, whilst cricket involves repetitive anaerobic activities.
However, cricket games continue over several hours or days, thereby increasing the aerobic
aspect. Both sports require careful attention to recovery so that athletes receive maximum benefits
from their training and perform at their optimum in competition on a regular basis. The main
advantage of this model is that it requires the athletes to self-monitor training and recovery
behaviour and thus play a more active role in determining their optimal individual training load. The
model requires minimal expense, is easy to administer and effective. Another important benefit is
the enjoyment the athletes gain from becoming more aware of features of their training and
recovery on a daily basis. Some sports use logbooks as an essential part of the training process.
As recovery is such an important part of the adaptation, the inclusion of the TQR scale as part of a
cricketer’s logbook or diary would provide both the athlete and the coach valuable information to
assist in the long-term development and performance of players.
References
Budgett, R. (1990). Overtraining syndrome. British Journal of Sports Medicine, 24, 231-236.
Kellman, M.(Ed.) (2002). Enhancing Recovery: Preventing underperformance in athletes.
Champaign, Illinois: Human Kinetics.
Kentta, G. & Hassmen, P. (1998). Overtraining and recovery: A conceptual model. Sports
Medicine, 26, 1-16.

137
Links between Physiotherapy Measures and Physical Performance Measures in
Australian First Class Cricketers
Kevin Sims and Aaron Kellett
Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: ksims@coe.cricket.com.au
Field based physiotherapy screening measures in Australian cricketers have been used over a
number of seasons. To date cricket fast bowlers have demonstrated links between lower quarter
injury and reduced front foot ankle dorsiflexion and increased back foot hip internal rotation
(Dennis, Finch, McIntosh, & Elliott, 2008). There is evidence from other sports that screening tests
may be associated with injury. For example in soccer reduced range of hip rotation or extension
have both been linked to injury (Bradley & Portas, 2007; Ibrahim, Murrell, & Knapman, 2007) while
reduced range of ankle dorsiflexion has been linked to lower extremity injury in AFL footballers
(Gabbe, Finch, Wajswelner, & Bennell, 2004).
Measurement of strength is also included in many physiotherapy screening examinations.
Isokinetic examination of soccer players has identified strength imbalances (reduced
hamstring/quadriceps ratio) associated with subsequent hamstring injury (Croisier, Ganteaume,
Binet, Genty, & Ferret, 2008). A deficit in iso-kinetic hip extensor strength (as well as eccentric
hamstring strength) has also been identified as a predisposing factor to hamstring injuries in elite
sprinters (Sugiura, Saito, Sakuraba, Sakuma, & Suzuki, 2008). Other authors have examined
strength using a hand held dynamometer to identify a loss of hip adductor strength preceding the
onset of pain in a group of elite young AFL footballers (Crow, et al., 2010).
The relationship between screening variables of strength and range of joint motion with physical
performance has received less attention. Sprint performance has previously been linked to the
ability to quickly produce force (measured as the time taken to reach 60% MVC) and various jump
variables (force applied 100 ms from start of loaded jump) (Bissas & Havenetidis, 2008; Young,
McLean, & Ardagna, 1995). Another study examined isometric strength of the hip extensors/flexors
and knee extensors but these were found to be poor predictors of sprint performance (Kukolj,
Ropret, Ugarkovic, & Jaric, 1999). A link between improved hip flexion range of motion and running
mechanics has also been suggested (Caplan, Rogers, Parr, & Hayes, 2009). The aim of this study
was to investigate a possible link between physiotherapy screening data and fitness testing results
in Australian first class cricketers.
Method: Sixty-three Australian first class cricketers were tested using the Cricket Australia national
standards protocol for physiotherapy and physical fitness. Briefly these protocols examine joint
range of motion, strength and functional abilities across a range of joints and tasks. The data were
collected in different states of Australia by the respective state physical performance teams
(physiotherapist/strength and conditioner) as part of the pre-season testing. The data were then
collated and analysed using SPSS v12.0. Correlations of the physiotherapy and fitness testing
variables were performed and a series of regression analyses were then done to determine the
relative contribution of any of the physiotherapy data to the physical performance tests.
All data were analyzed using Pearson’s correlation and regression analysis. Two of the
physiotherapy screening variables were related to 5 m sprint time. Players with greater range of hip
external rotation were slower (L&R: r=0.416, p <0.000) and those with stronger hip abductors were
faster (L: r=-0.318, p=0.012; R: r=-0.274, p=0.031). When these two variables were used in
regression analysis it was possible to explain 28% of the variance in the 5 m sprint time. When the
yo-yo test (which assesses players’ ability to recover from repeated high intensity running efforts)
was considered those players with stronger hip abductors (L: r=0.376, p=0.012; R: r=0.360,

138
p=0.016) and better hip extension range (L: r=0.392, p=0.008; R: r=0.388, p=0.009) covered
greater distances. Using these two variables in a regression analysis it was possible to explain
25% of the variance of the distance covered in the yo-yo test.
Although it is recognized that the relationship between functional tasks and physical parameters
from the physiotherapy screening is small (explaining at most 25% of the variance) it is still a useful
finding. Whereas it is common to link factors such as joint range of motion and strength with
potential injury it is equally important to consider their influence on performance. The analysis is
not able to explain how the physical parameters may exert their effect although it is well accepted
that hip abductors are crucial to controlling movement in the frontal plane (Sims & Brauer, 2000).
The players with better strength in the hip abductors may therefore be more effective in directing
their efforts in a straight line rather than inefficient lateral movements. Interestingly the link between
hip abductor strength and performance was evident in both the sprint and the progressive
intermittent running ability. This would appear to indicate that training hip abductor control should
remain an important aspect of athletic training in cricketers.
It is uncertain why a greater range of hip external rotation was linked to a slower 5 m run speed.
However several studies have linked greater ranges of hip external rotation compared to medial
rotation with low back pain (Cibulka, Sinacore, Cromer, & Delitto, 1998; Ellison, Rose, & Sahrman,
1990). It is proposed that a loss of hip rotation may reduce the ability of the pelvis to rotate forward
on the femur during gait, potentially altering the loads on the lumbar spine. In relating this to
performance during a 5 m sprint task it is possible that subjects with greater range of hip external
rotation may not be able to rotate their pelvis forward on the contralateral side as efficiently during
the stance phase of running. In addition a less efficient use of hip internal rotation during stance
phase may lead to the hip abductors/external rotators operating at an inefficient length tension
relationship. This may be important considering the link between hip abductor strength and sprint
speed already discussed.
In summary it would seem reasonable that training which involves hip abductor strengthening and
perhaps hip internal rotation stretching may be beneficial not only for injury prevention but also for
athletic performance.
Acknowledgement: The authors would like to acknowledge the assistance of the state
physiotherapy and strength and conditioning staff. The authors also acknowledge the contribution
of Dr Ian Heazlewood for his expert statistical advice.
References
Bissas, A., & Havenetidis, K. (2008). The use of various strength-power tests as predictors of sprint
running performance. J Sports Med Phys Fitness, 48(1), 49-54.
Bradley, P., & Portas, M. (2007). The relationship between preseason range of motion and muscle
strain injury in elite soccer players. J Strength Cond Res, 21(4), 1155-1159.
Caplan, N., Rogers, R., Parr, M., & Hayes, P. (2009). The effect of proprioceptive neuromuscular
facilitation and static stretch training on running mechanics. J Strength Cond Res, 23(4), 1175-
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Measurement of Ball Flight Characteristics in Finger-Spin Bowling
Wayne Spratford1,2 and John Davison3
1 Biomechanics and Performance Analysis, Australian Institute of Sport, Canberra
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
3 Player Development Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: wayne.spratford@ausport.gov.au
Spin bowling can be broadly classified into two categories; finger and wrist spin and while the
mechanics are different they both have the same aim, to deceive the batsmen through a
combination of spin, flight, drift and speed variations. While they are all important the coaching
literature tends to focus on the ability to place revolutions on the ball in order to turn it off the pitch.
This is best summed up by the late Bob Woolmer;
“Spin the ball, turn it, masses of it, this must be their aim above all. They must want to turn it
square, make it kick to the leg or to the off side at 90°, they must dream of bowling at batsmen
around their legs or of getting the ball to skip into middle-stump from two feet outside off-stump,
before he or she evens begins to experiment with flight, drift and quicker flatter deliveries”
(Woolmer et al., 2008, p.288)
The ability to control these revolutions imparted on the ball may be of more importance as this has
the potential to influence the ball’s flight variation and ability to turn off the pitch and as such should
be a focus of all coaching staff. A spinning object has an axis of rotation, around which the object
spins. A spin bowler should aim to rotate the ball with the seam perpendicular to this axis of
rotation. If this occurs the seam appears to rotate in a stable manner, it is not “scrambled”. The
ability to do this heightens the chance of the seam hitting the ground and enabling it to grip and
turn, which in the general Australian conditions is critical. As yet no research has linked ball
dynamics (revolutions and seam stability) to the Magnus effect, which is the aerodynamic principle
responsible for “drift and dip” in a spin bowler. In practical terms when a right arm finger spin
bowler bowls a stock delivery to a right handed batsman the natural drift will be towards the off-
side or away from the batsmen (Woolmer et al., 2008).
Although the general aerodynamic properties of a cricket ball are complex due to the presence of a
series of seams and the constantly changing contrasting surfaces (smooth and rough), research
does exist highlighting the importance of seam position, axis of rotation and rotation rate in swing
bowling (Mehta & Pallis, 2001). Very little research specifically focussing on spin bowling exists
due mainly to the complexities in enabling a ball to rotate at high frequencies within a wind tunnel
environment. Sayers and Hill (1999) successfully did this for a top spinning delivery with a ball that
had a roughened side (they were limited to top spin due to their mechanism that held the ball).
They reported that the force created was dependent on the free stream velocity (air moving around
the boundary layer of the ball, influenced by the roughened surface of the ball) and the speed of
rotation. It must be noted that ball revolutions reached only 16 per second, which is much lower
than a high level spin bowler. Mehta and Pallis (2001) also theorised that seam roughness in
baseball became less important at higher rotational rates, however it is not well understood
whether this occurs in cricket balls.
The aim of this research was to compare ball revolutions and spin axis directions for two finger-
spin bowlers identified through high speed video as having vastly different spin axis stabilities, one
that appeared stable (subject 1) and one that appeared unstable or scrambled (subject 2). It is
hoped the current modelling approach can discriminate between these two delivery types, allowing
a ball stability measure to be developed to compliment that of ball revolutions in further analysis of
spin bowling.

141
Method: Two finger-spin bowlers participated in this study with a mean age, height and mass of
21.5 years, 184 cm and 85 kg respectively. Both were of 1st class level with subject 1 being left
handed and having played at Australia A level and subject 2 being a batsman, who only bowled at
a part-time level and was right handed. They were both selected due to their visually different
seam stability characteristics during the flight phase (as qualitatively assessed by high-speed
video), with subject 1 appearing completely stable and subject 2 with a scrambled seam.
To track the movement of the ball three retro-reflective markers were placed in positions that did
not impede the natural action of the delivery (figure 1).
Figure 1: Marker positions on the ball (the third marker is obscured)
Each subject warmed up as per their normal bowling routine before bowling 18 stock off-spinning
deliveries, nine to a right handed batsman and nine to a left handed batsman in a custom built
indoor cricketing facility at the Australian Institute of Sport Biomechanics laboratory. Marker
trajectory data from six deliveries were collected for each bowler, six over the wicket to a left
handed batsman for subject 1 and six over the wicket to a right handed batsman for subject 2. This
allowed a mirror image of each data set to allow for handedness differences. Trajectory data were
collected using a 22 camera Vicon Motion Analysis System (Oxford Metrics, Oxford, UK) operating
at 250 Hz. Data were processed, filtered (Woltring filter at a mean square error of 15) and
modelled creating a coordinate system for the ball based on the calculations of Jinji and Sakurai
(2007) and Sakurai et al. (2007) in Nexus software (Oxford Metrics, Oxford, UK).
Ball velocity was calculated as the absolute velocity of the ball origin and expressed as km/h, ball
revolutions as the absolute value of the angular velocity vector and expressed as rev/s. Spin axis
elevation angle was measured relative to the horizontal plane and the alpha angle as the angle
between the velocity vector of the ball and its axis of rotation.
Results: Results for ball revolutions, velocity, elevation angle and alpha angle for subjects 1 and 2
are provided in Table 1. No statistical analysis was undertaken due to the case study approach
(individual differences are assumed).
Table 1: Mean (± SD) of ball kinematics.
Variable Subject 1 Subject 2
Ball revolutions (rev/s) 27.5 (1.2) 29.3 (1.5)
Ball velocity (km/h) 68.2 (0.8) 69.1 (1.0)
Spin axis elevation angle (°) 20.0 (2.0) 17.0 (2.0)
Spin axis alpha angle (°) 31.0 (2.0) 36.0 (2.0)
Discussion: Results show that differences do exist in the spin axis with subject 2 (part-time bowler)
showing lower elevation angles and higher alpha angles for the six deliveries. Ball revolutions were

142
similar. As mentioned in the introduction very little is understood about the aerodynamics of a
cricket ball delivered by a spin bowler and if revolutions were our sole discriminator, we would have
to assume subject 2 (part-time) was of a higher skill level than our national representatives (as ball
revolutions were slightly higher), for this reason other ball measures need to be considered.
Chin et al., (2009) linked lower elevation angles at ball release in finger-spin bowlers to increased
levels of top-spin when delivering their stock delivery. This is a potentially important finding as top-
spin influences “dip” and “bounce” adding to the variation of the stock delivery but until there is
more comprehensive wind tunnel testing on spinning cricket balls, quantifying this remains difficult.
Research into a combination of spinning balls (smooth and baseballs) has indicated that the
Magnus effect has it greatest influence when the direction of travel and the spin axis are at 90° to
each other (Jinji and Sakurai, 2007). While this level is impractical for spin bowling due to the seam
orientation, results from this study show alpha angles of 31° (subject 1) and 36° (subject 2). In
theory the 36° delivery should drift more than 31° delivery, but from qualitative assessment the
seam position for subject 2 was not rotating around the axis of rotation. The influence this has on
the Magnus effect is also not well understood but it is assumed that it does reduce the chances of
repeatedly landing the seam on the ground at initial impact.
The axis of rotation also has the potential to give a seam stability measure, but only if the seam
position is known. Currently we can only give a comparison between alpha angles, as its exact
location relative to the balls coordinate system is unknown. In theory if we assume from the
qualitative analysis that subject 1 has the seam of the ball rotating perfectly around the axis of
rotation inferring that subject 2’s axis of rotation has an off-set of 5° (36-31) from the preferred
position or from subject 1. This makes the assumption that subject 1 is a gold standard measure
and that both players deliver the ball from a similar angle in relation to the batsmen. For these
reasons a more robust method of measurement is needed to eliminate the need to make these
assumptions. It is proposed that future testing will include a static ball measure that will allow the
seam of the ball to be modelled either by markers placed around the seam (Figure 2) or by using a
pointer method. The markers that define the seam will be removed for the dynamic trials and their
coordinates held within the technical coordinate system of the ball. This would allow an axis of
rotation measure in relation to the seam to be calculated accurately and on an individual basis.
Figure 2: Proposed static marker positions on the ball (two dynamic markers are obscured)
Assessing skill level in an artificial environment is always a difficult and complex undertaking and
there is always the argument of ecological validity and that the only true reflection in skill can be
found in a players batting or bowling averages. There may be some merit in this argument but if we
are to develop our pathway and developing players it is important that we are able to firstly quantify
what is happening and secondly understand what is happening with our elite players and monitor
this through our development pathways to better educate coaches and improve best practice.
Further research needs to be conducted into the aerodynamic properties of spin bowling to better
understand the outcomes of differing ball dynamics.

143
Acknowledgement: The authors would like to thank the University of Western Australia for the use
of their kinematic model.
References
Chin, A., Elliot, B., Alderson, J., Lloyd, D., & Foster, D. (2009). The off-break and "doosra".
Kinematic variations of elite and sub-elite bowlers in creating ball spin in cricket bowling. Sports
Biomechanics, 8(3), 187-198.
Jinji, T., & Sakurai, S. (2007). Direction of Spin Axis and Spin Rate of the Pitched Baseball. Sports
Biomechanics, 5(2), 197-214.
Mehta, R. D., & Pallis, J. M. (2001). Sports Ball Aerodynamics: Effects of Velocity, Spin and
Surface Roughness. Paper presented at the Materials and Science in Sports, Coronado,
California.
Sakurai, S., Jinji, T., Reid, M., Cuitenho, C., & Elliot, B. (2007). Direction of Spin Axis and Spin
Rate of the Ball in Tennis Service, International Society of Biomechanics. Taiwan: Journal of
Biomechanics.
Woolmer, B., Noakes, T., & Moffett, H. (2008). Bob Woolmer's Art and Science of Cricket (First
ed.). Sydney: New Holland Publishers Ltd.

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The Influence of Batting Handedness on Rates of Shoulder Counter-Rotation
in Cricket Fast Bowlers
Wayne Spratford, Chris McCosker, Nadine Morrison and Rian Crowther
Biomechanics and Performance Analysis, Australian Institute of Sport, Canberra
Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: wayne.spratford@ausport.gov.au
Introduction: Retrospective injury surveillance studies have shown a disproportionate level of
missed game time due to injury for fast bowlers in comparison with other playing groups (Orchard
et al., 2006). Clinical based technique studies have linked increased levels of shoulder counter-
rotation and pelvis to shoulder separation angle to lumbar spine stress fractures and soft tissue
injuries (Burnett et al., 1996; Portus et al., 2004). While prospective longitudinal studies have linked
increased bowling loads and reduced recovery time to missed game time (Dennis et al., 2003).
Results of these types of studies have seen thresholds or guidelines set for specific techniques as
well as upper and lower bowling limits and more stringent monitoring of recovery time.
What is currently not known about technique is, if specific technique parameters which have been
established as injury mechanisms alter when bowlers are bowling to either a left handed batsmen
(LHB) or right handed batsmen (RHB). This maybe of more relevance as it is common in sports
that feature interactive contests for left-handers to have a distinct advantage and cricket is no
exception. Brooks et al. (2003) found that at the 2003 World cricket Cup, the most successful
teams consisted of approximately 50% left handed batsmen. The current Australian domestic
cricket competition at Sheffield Shield and Pura Cup levels sees 42% of the top seven batsmen as
LHB. This compared with the futures league which only contained 23% of the top seven batsmen
as LHB. While this is only a snap shot, it does identify that left handedness compared with
population statistics, is over represented in the batting population. This trend increases as the level
of competition increases in comparison with the general population, where 10% are left hand
dominant (Raymond et al., 1996).
The aim of this study is to compare the transverse plane differences for shoulder counter-rotation,
pelvis with shoulder separation angle at back foot contact (BFC), pelvis angle at BFC and thorax
angle at BFC when bowling to left and right handed batsmen.
Method: A total of 24 right handed fast bowlers participated in this study with mean age, height and
mass of participants being 18.33 years (±2.97), 1.89 m (±0.06) and 83.26 kg (±7.81). Competition
levels ranged from International, 1st Class to National youth sides with all players being selected by
the National pace bowling coach. To track segmental motion an upper-body static and dynamic
marker set comprising of 54 retro-reflective markers were placed on the upper limbs, torso, head
and pelvis of the subject adapted from the version first used by Lloyd, Alderson & Elliot (2000).
Marker trajectory data were collected using an 18 camera Vicon Motion Analysis System (Oxford
Metrics, Oxford, UK) operating at 250 Hz during the delivery phase, which comprised of BFC, FFC
and ball release (BR). BFC and FFC were defined as the image when the greatest proportion of
foot was in contact with the ground (during BFC it was common that the foot never completely
became flat)
Each subject warmed up as per their normal bowling routine before bowling 24 randomised
deliveries over the wicket comprising of eight yorker length (four to a RHB and four to a LHB), eight
good length (four to a RHB and four to a LHB) and eight short length (four to a RHB and four to a
LHB) to an accuracy target placed where the batsmen would normally stand comprising of series
of 20 cm x 20 cm squares. This allowed the outcome of each delivery to be measured enabling 12
deliveries that reflected this to be selected for analysis comprising of four full length (two to a RHB

145
and two to a LHB), four good length (two to a RHB and two to a LHB) and four short length (two to
a RHB and two to a LHB) for all subjects.
Three-dimensional trajectory data was reconstructed, processed, filtered (Woltring, mean square
error of 15) and modelled in Vicon Nexus software (Oxford Metrics, Oxford, UK). Location of the
shoulder joint centre was determined by using a regression method developed by Campbell et al.
(2009) using five independent variables, subject height, subject mass and a series of three-
dimensional measures between markers placed on the superior aspect of the manubrium (CLAV),
7th cervical vertebrae (C7) and the 10th thoracic vertebrae (T10). The thorax and pelvis segment
reference frames were represented each by four markers.
All variables were measured in the transverse plane. Shoulder counter-rotation was determined by
measuring the shoulder alignment angle at BFC and the minimum angle (most side on position)
prior to FFC contact and subtracting the minimum from the BFC measure. Pelvis to shoulder
separation angle was measured as the relative angle between the alignments of the pelvis and
shoulder segments at BFC. Shoulder alignment and pelvis angles at BFC were also measured.
Traditionally a bowler has been deemed to have a mixed action if either shoulder counter-rotation
or pelvis to shoulder separation angles were ≥ 30° (Burnet et al., 1995; Portus et al., 2004).
Paired t-tests were used to determine if differences existed when comparing shoulder counter-
rotation, pelvis with shoulder separation angle at BFC, pelvis angle at BFC and thorax angle at
BFC when bowling to left and right handed batsmen. Statistical significance was set at p < 0.05.
Results: No statistical significance was found when comparing results between deliveries to LHB
and RHB (pelvis p = 0.71; shoulder p = 0.72; SCR p = 0.84; shoulder separation p = 0.90). Table 1
depicts the mean, standard deviation and ranges for all collected variables.
Table 1: Kinematic variables during the delivery stride between LHB and RHB
Pelvis angle
BFC
Shoulder alignment
angle
BFC
Shoulder counter-
rotation angle
Pelvis to shoulder
separation angle
BFC
LHB RHB LHB RHB LHB RHB LHB RHB
Mean
(S.D)
33.8
(16.1)
32.0
(16.3)
48.2
(18.6)
46.2
(19.1)
35.4
(13.8)
34.6
(13.9)
15.8 (10.7) 15.4
(10.5)
Range 10.5-61.5
8.7- 60.5 32.7-81.6 19.8-79.6 11.8-56.8 7.2-51.7 1.2-36.7 1.3-27.2
Discussion: The prevalence of left handed batsman increases with the level of competition in
cricket. However there is a lack of research examining technique differences associated with
bowling to LHB. It was hypothesised that when bowling to a LHB, bowlers may adopt a more side
on technique to account for the slight change in angle needed to deliver the ball to the opposite
side of the pitch (to the off stump of a LHB). If this correction occurred prior to BFC we would
expect to see lower amounts of shoulder counter-rotation due to the more side on alignment of the
shoulders, while after BFC one could see the opposite happening with larger amounts of shoulder
counter-rotation present. Alternatively if the extra rotation needed occurred only in the upper body,
increases in pelvis to shoulder separation angles may present. However no significant differences
were observed. Brief investigations into within subject variances revealed the same findings. One
limitation of this study was the focus on the transverse plane measures of the shoulder alignment
and pelvis angles and that if any kinematic alterations did occur it may have been beyond these
parameters (i.e. shoulder abduction angles).
Conclusion: No significant differences were found when comparing known injury mechanisms for
fast bowlers bowling to either a left or right handed batsmen. The results did highlight that the
mean shoulder counter-rotation value was 35° with 17 of the 24 bowlers being above the 30° injury

146
threshold, and as such would be categorised as having a mixed action. For this reason it is
important to continue to monitor anything that may affect technique, as levels of shoulder counter-
rotation on average already sit above the known injury level. Further research needs to focus on
variables such as individual shoulder contributions to the shoulder alignment angle, knee angles,
thorax to pelvis lateral flexion which has recently been linked to lower back pain in females
(Stuelcken et al, 2010) and to also analyse technique differences when bowling various length
deliveries.
Acknowledgement: Contributions made by the University of Western Australia and Cricket
Australia need to be acknowledged.
References
Brooks, R., Bussiere, L., Jennions, M., & Hunt, J. (2003). Sinister strategies succeed at the cricket
World Cup. Proc. R. Soc. Lond, 271, 64-66.
Burnett, A., Elliott, B. & Marshall, R. (1995). The effect of a 12 over spell on fast bowling technique
in cricket. Journal of Sports Science, 13-329-341.
Burnett, A., Khangure, M., Elliot, B., Foster, D., Marshall, R. & Hardcastle, P. (1996).
Thoracolumbar disc degeneration in young fast bowlers in cricket: a follow up study. Clinical
Biomechanics, 11, 305-310.
Campbell, A., Lloyd, D., Alderson, J. & Elliott, B. (2009). MRI development and validation of two
new predictive methods of glenohumeral joint centre location identification and comparison with
established techniques, Journal of Biomechanics, 42 (10), 1527-1532.
Dennis, R., Farhart, R., Goumas, C. & Orchard, J. (2003). Bowling workload and the risk of injury
in elite cricket fast bowlers. Journal of Science and Medicine in Sport, 6(3): 359-367.
Lloyd, D., Alderson, J. & Elliott, B. (2000). An upper limb kinematic model for the examination of
cricket bowling: A case study of Mutiah Muralitharan. Journal of Sports Sciences, 18, 975-982.
Orchard, J., James, T. & Portus, M. (2006). Injuries to elite male cricketers in Australia over a 10
year period, Journal of Science and Medicine in Sport, 9, 459-467.
Portus, M., Mason, B., Elliott, B., Pftizner, M. & Done, R. (2004). Technique Factors Related to Ball
Release Speed and Trunk Injuries in High Performance Cricket Fast Bowlers, Sports
Biomechanics, 3:2, 263-284.
Raymond, M Pontier, D Dufour, A.B and Moller, A.P. (1996). Frequency-dependent maintenance
of left handedness in humans. Proc. R. Soc. Lond. 26, 1623-1633

147
Biomechanical Spin Bowling Research: A Prospective Overview
Wayne Spratford1,2,3 and Jacqueline Alderson3
1 Biomechanics and Performance Analysis, Australian Institute of Sport, Canberra
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
2 School of Sport Science, Exercise and Health, The University of Western Australia, Perth
Correspondence: wayne.spratford@ausport.gov.au
Introduction: Spin bowling plays an integral role within the game of cricket. This point is reinforced
by international bowling statistics which identifies the top three bowlers in test match cricket, the
top bowler in one-day cricket and second, third and fourth in Twenty20 cricket all being spin
bowlers. However, the success of spin bowlers is not reflected in the scientific literature, with very
few peer reviewed journal articles examining on any aspect of spin bowling. This inequity may be
fuelled by cricket allied staff focussed predominantly on the higher levels and extensive injuries
suffered by fast bowlers While there are many facets that contribute to a successful spin bowler,
imparting a high level of revolutions on the ball during the delivery phase is seen as critical and the
main causation responsible for turning or spinning the ball off the pitch (Woolmer et al., 2008).
The aim of this proposed research is to quantify the contributions of all upper limb segments on the
production of ball revolutions and seam stability. These variables will be examined throughout the
development pathway (juniors to 1st class and above) for both finger and wrist spin bowlers.
Method: This proposed study will include three separate groups, elite (1st class and above),
pathway players (national U 17 and 19 players as well as Futures league players) and junior (12-
14 year olds). Elite and pathway groups will be selected by the national spin bowling coach with
the junior group being recruited locally. All bowlers will attend the Australian Institute of Sport (AIS)
Biomechanics laboratory where they will undergo a full anthropometric profile including; selected
joint range of motion (ROM) and isometric strength of the shoulder (flexion, extension, abduction,
adduction and internal and external rotation), forearm (pronation and supination) and wrist (flexion,
extension and ulna and radial deviation). The test battery will be adapted from a number of sources
including but not limited to, Cricket Australia Physiotherapy protocols (2010), Marfell-Jones et al.
(2007), Gerhardt et al. (2002). ROM measures will be taken by inclinometers and goniometers and
strength measures by a Cybex machine (Humac Norm).
Bowlers will deliver 36 deliveries (their stock plus two variations) over force plates (back foot and
front foot contacts) while wearing a customised retro-reflective marker set developed by the
University of Western Australia. Trajectory and force data will be collected by a 22 camera Vicon
Motion Analysis System (Oxford Metrics, Oxford, UK) operating at 250 and 1000 Hz allowing full
body kinematics and kinetics to be calculated. A custom made finger kinematic model will also be
developed allowing joint angles to be measured between the hand and proximal phalanges (MCP)
and proximal to middle phalanges (PIP) of the second finger (this will be used for finger spin
bowlers only), as seen in figure 1. Kinematic data will also be processed in the “OpenSim”
environment allowing muscle driven models to be created that estimate muscle tendon forces and
joint moments. The initial emphasis will be on the upper body, with a model created which
represents the shoulder, elbow, forearm, wrist and finger joints and containing the 50 muscle
compartments that cross these (Holzbaur et al., 2005).
Four static retro-reflective markers placed around the seam and three dynamic markers will be
attached to the ball to track ball dynamics. A static ball trial will be captured allowing the seam
position to be determined from the static markers and held in the coordinate system created from
the dynamic markers (static markers will be removed prior to bowling). Trajectory data collected

148
from these will allow axis of rotation measures relative to the seam to be calculated based on the
methods seen in Jinji and Sakurai (2007) and Sakurai et al. (2007).
Figure 1: Marker placements for the kinematic finger model, with and without static markers.
Proposed outcomes: Outcomes will be divided into the following phases;
Phase one: Development and validation of a kinematic finger model and updated ball dynamics
model that incorporates the known position of the seam.
Phase two: Results of the study will allow descriptive analysis across the three groups tested,
measures will include:
• Ball dynamics (revolutions, seam stability, axis of rotation measures)
• Full body kinematics, focussing on upper limb
• Full body joint kinetics, focussing on upper limb (upper body will use inverse dynamics
starting from the ball and working up the upper limb to the shoulder, lower body will start at
the force plate and work up the lower limb to the hip joint).
• Full body muscle tendon forces, focussing on the upper limb
• Anthropometry, ROM (upper limb) and isometric strength measures (upper limb).
Phase three: To examine the relationships and contributions of kinematics, kinetics, muscle tendon
forces, isometric strength and ROM of the upper limb to the ball dynamics (revolutions and seam
stability). Statistics used will include correlations and multiple stepwise regression analysis. It is
proposed that on going work will examine the relationships of a full body model to that of ball
dynamics.
Phase four: To investigate how age and physical maturity affect the development of the spin
bowler by examining differences in collected data between the three groups.
Phase five: To adapt the forward kinematic model currently being developed between the
University of Western Australia and Griffith University to include kinematic and ball dynamic data
from finger and wrist spin bowlers. This will allow coaches, support staff and scientists to quantify
how changing technique will influence the outcome measures of ball dynamics.
Conclusion: It is hoped that this proposed research adds to the scientific literature, helps coaching
staff gain a better understanding of the mechanics of spin bowling and aids in the better
development of our spin bowlers of the future.
References
Gerhardt, J., Cocchiarella, L., & Lea, R. (2002). The Practical Guide to Range of Motion
Assessment (First ed.): American Medical Association.

149
Holzbaur K, Murray W, & Delp S. (2005). A model of the upper extremity for simulating
musculoskeletal surgery and analyzing neuromuscular control. Ann Biomed Eng, 6, 829-840.
Jinji, T., & Sakurai, S. (2007). Direction of Spin Axis and Spin Rate of the Pitched Baseball. Sports
Biomechanics, 5(2), 197-214.
Marfell-Jones, M., Olds, T., Stewart, A., & Lindsay Carter, J. (2007). International Standards for
Anthropmetric Assesment: International Society for the Advancenet of Kinanthropometry.
Sims, K. (2010). Cricket Australia Sport Science Sport Medicine Unit, National Physiotherapy
Screening Protocol: Brisbane: Cricket Australia.
Sakurai, S., Jinji, T., Reid, M., Cuitenho, C., & Elliot, B. (2007). Direction of Spin Axis and Spin
Rate of the Ball in Tennis Service, International Society of Biomechanics. Taiwan: Journal of
Biomechanics.
Woolmer, B., Noakes, T., & Moffett, H. (2008). Bob Woolmer's Art and Science of Cricket (First
ed.). Sydney: New Holland Publishers Ltd.

150
Examining How Psychological Factors Contribute to Team Performance in an
Australian National Cricket Competition
Rosanna Stanimirovic1 and Michael Lloyd1,2
1 Psychology Department, Australian Institute of Sport, Canberra
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: mlloyd@coe.cricket.com.au
The aim of the current study was to investigate how psychological factors contributed to team
performance in a sample of male cricket players competing in the Cricket Australia Under 19 Male
National Championships over 3 consecutive years (2007-2009). Research to determine the
predictive validity of the psychological measures that relate specifically to successful performance
in elite level competition is limited (Humara, 2000). To provide a framework for the choice of
measures in a sporting context, performance in competition was considered a job performance
outcome (Stanimirovic & Hanrahan, 2010). Schmidt and Hunter’s (1998) meta-analysis of the
psychological factors that predict job performance outcomes in non-sport contexts recommended
that a measure of general mental ability (GMA) and a supplementary trait measure should be
administered.
Adams and Kuzmits (2008) and Lyons, Hoffman, and Mischel (2009) determined the predictive
efficiency of GMA in the National Football League (NFL) using the Wonderlic Personnel Test
(WPT). The results of both studies demonstrated that GMA was unrelated to NFL performance.
However, despite the conclusive nature of these findings it is suggested that consideration of an
alternative to the WPT is warranted. In the current study, the measure of GMA was the Raven’s
Standard Progressive Matrices (RSPM; Raven, Court, & Raven, 2004). Carpenter, Just, and Shell
(1990) suggested that analytic intelligence requires the individual to reason and solve problems
involving new information, without relying extensively on an explicit base of declarative knowledge
derived from schooling or previous experience in a specific context. Similarly, it was demonstrated
that analytic intelligence measured by the RSPM is central to intelligence and shares considerable
variance with other tests of crystallised or fluid intelligence. Carpenter et al. described the RSPM as
a classic test of analytic intelligence.
The supplementary measure chosen was the the Bar-On Emotional Quotient Inventory (EQ-i)
which is a measure of trait emotional intelligence (EI). The EQ-i measures how an individual copes
with the demands and pressures of an environment which relates well to the competitive
experience. There is evidence for the existence of an EI – performance relationship in professional
sports including baseball (Zizzi, Deaner, & Hirschhorn, 2003), cricket (Crombie, Lombard, &
Noakes, 2009), and ice-hockey (Perlini & Halverson, 2006). Therefore providing further evidence
for the EI – performance relationship in cricket can provide valuable information for the
development of psychological interventions specific to performance enhancement in cricket.
The aims of the current study were to examine whether GMA and trait EI contributed to overall
team performance at the Cricket Australia Under 19 Male National Championships. To facilitate
this investigation, 290 male athletes completed the RSPM and the EQ-I across three consecutive
championships (2007-2009). Data from three participants were excluded due to the entire team not
completing the protocol. In total, data from 22 teams were included in the analyses. The total points
scored by those teams at the end of the championships were used as the performance measure.
For all analyses, the data were screened and outliers removed so the final sample was N = 287.
Normality was then checked for all variables. Regression analyses were conducted using the raw
scores for the RSPM and standardised scores for the EQ-i subscales. GMA was entered as the first
step and the EQ-i subscales were entered as the second step in a stepwise manner.

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Regression analyses indicated that GMA was not a significant contributor to total number of points
scored by a team. Happiness was a significant contributor (2%) to total number of points scored by
a team. Bar-On (1997) broadly defines the EQ-i subscale of happiness as self-motivation and
more specifically as individuals who feel satisfied with their lives. The EQ-i happiness subscale is
negatively correlated with depression scales and therefore happiness can be described as “not
getting down on yourself” in situations. The relevant statistics for the regression analyses are
included in Table 1.
Table 1: Regression Analyses for Total Points Scored by a Team at the Cricket Australia Under 19
Male National Championships in 2007, 2008, and 2009
B SEB β R
2 ad
j
∆R2
Step 1
GMA
-0.05
0.08
-.04
-0.003 0.000
Step 2
happiness
0.09
0.04
.13* 0.02* 0.02
* p < .05
New South Wales (NSW) and Victoria (VIC) have been the more successful teams over the 2007-
2009 periods and were hence examined more closely. There was no significant contribution of
GMA for either NSW or VIC. The relevant statistics for the regression analyses are included in
Table 2. For NSW over the 3 year period, teams with higher scores on impulse control scored
more total points. Bar-On (1997) defines impulse control as the ability to be composed by
effectively and constructively controlling aggression, hostility, and irresponsible behaviours.
Generally, “keeping a cool head” in all situations. Individuals with low scores may be against
personal change and are rigid in their thinking. For VIC over the 3 year period, teams with higher
scores on happiness scored more total points.
Table 2: Regression Analyses for Total Points Scored by NSW and VIC at the Cricket Australia
Under 19 Male National Championships in 2007, 2008, and 2009
B SEB β R
2 ad
j
∆R2
NSW
impulse control
0.32
0.14
0.42*
0.13*
0.16
VIC
happiness
0.25
0.11
0.38*
0.14*
0.14
* p < .05
The results of the current study demonstrated how psychological factors contributed to team
performance at the Cricket Australia Under 19 Male National Championships. GMA provided no
contribution, whereas The EQ-i subscale of happiness significantly contributed to total team points
scored. For the more successful states (i.e., NSW and VIC), the EQ-i subscales of impulse control
and happiness significantly contributed to more points scored by the team over a 3 year period.
Overall the results suggested that teams with athletes who “don’t get down on themself” and “keep
a cool head in all situations” will perform better. The results are preliminary and more data
confirming the findings is necessary.
The important outcome of the study from an applied perspective is providing further evidence to
athletes and coaches about which psychological competencies lead to successful performance by
cricket teams in competition. The information can also be presented to professional sporting
organisations and used to propose how psychological services can be integrated in a structured
manner.
Acknowledgement: The authors thank the championship organisers, the U19 cricketers, their state
associations and staff for their willingness to participate, Marc Portus who facilitated some of the

152
data collection and analysis and Dr Ian Heazlewood who provided some statistical support.
References
Adams, A. J., & Kuzmits, F. E. (2007). Testing the relationship between a cognitive ability test and
player success: The National Football League case. Athletic insight, 10 (1). Retrieved July 4, 2009,
from www.athleticinsight.com/Vol10Iss1/Testing Success.htm
Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical
account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97 (3),
404-431.
Crombie, D., Lombard, C., & Noakes, T. (2009). Emotional intelligence scores predict team
sports performance in a National cricket competition. International Journal of Sports Science
& Coaching, 4, 209-224.
Humara, M. (2000). Personnel selection in athletic programs. Athletic Insight, 2 (2). Retrieved July
4, 2009, from www.athleticinsight.com/Vol2Iss2/Personnel.htm
Lyons, B. D., Hoffman, B. J., & Mischel, J. W. (2009). Not much more than g? An examination of
the impact of intelligence on NFL performance. Human Performance, 22, 225–245.
Perlini, A. H., & Halverson, T. R. (2006). Emotional intelligence in the National Hockey League.
Canadian Journal of Behavioural Science, 38 (2), 109-119.
Raven, J. C., Court, J. H., & Raven, J. (2004). Raven manual: Section 3. The Standard Progressive
Matrices. Oxford, England: Oxford Psychologists Press.
Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel
psychology: Practical and theoretical implications of 85 years of research findings. Psychological
Bulletin, 124, 262–274.
Stanimirovic, R., & Hanrahan, S. J. (2010). Psychological predictors of job performance and career
success in professional sport. Manuscript submitted for publication.
Zizzi, S. J., Deaner, H. R., & Hirschhorn, D. K. (2003). The relationship between emotional
intelligence and performance among college baseball players. Journal of Applied Sport
Psychology, 15, 262-269.

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The 3D Kinematics of the Single Leg Flat and Decline Squat
Stephen Timms1,2, Tony Shield1, Marc Portus2, Kevin Sims2, Patrick Farhart
1 School of Human Movements, Queensland University of Technology, Brisbane
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: stimms@coe.cricket.com.au
Introduction: The single leg squat (SLS) replicates an athletic position commonly assumed in sport
such as changing direction, jumping and balancing which all require multi-plane control of the trunk
and pelvis on the weight bearing femur (Kibler et al., 2006; Willson et al., 2006; Zeller et al., 2003).
Consequently, the SLS is used clinically as a functional measure of lumbo-pelvic control (Kibler et
al., 2006; Willson et al., 2006; Zeller et al., 2003) as it has a greater ability to highlight those with
poor lumbo-pelvic stability (Mitchel et al., 2003) than the standard two legged squat. With the
addition of a decline board, the single leg decline squat (SLDS), is mostly used as a targeted
rehabilitation intervention for patellar tendinopathy (Cannel et al., 2001; Purdam et al., 2004;
Richards et al., 2008; Young et al., 2005; Zwerver et al., 2007). The SLDS has been employed in
the Cricket Australia (CA) physiotherapy screening protocols as a measure of lumbopelvic control
in the place of the more traditional single leg flat squat (SLFS). Previous research has investigated
the kinematic differences between the SLDS and SLFS both in 2D (Kongsgaard et al., 2006) and
3D (Frohm et al., 2007) focussing mainly on the differences in knee kinematics. These researchers
were unable to demonstrate clear differences between the two conditions particularly relating to the
torso and weight bearing hip. A better understanding of the differences between the two conditions
around the weight bearing hip is an important aspect of interpreting the SLDS in the CA
physiotherapy protocol.
Methods: 19 participants (age 23.8 ± 3.7 years) who played at least 1st grade club cricket or at a
higher level in the previous season were recruited. Testing was conducted at the biomechanics lab
at the Queensland University of Technology (QUT). Approval from the QUT Human Research
Ethics Committee was granted prior to testing.
Testing Procedure: The single leg squatting manoeuvres were explained and demonstrated to
each of the subjects in both the flat and decline positions. The decline explanation was consistent
with that of the CA physiotherapy protocol. A decline board of 20° and the floor of the testing
laboratory were used to satisfy the decline and flat scenarios of the testing procedures
respectively. Subjects were given two practice squats on each leg for both types of squats to
familiarize themselves with the activity. During the decline squatting procedure the subject’s weight
bearing (WB) foot was placed squarely on the decline board. The trunk remained in an upright
position with arms folded across the stomach as to not obscure the line of sight between the
reflective markers and the cameras. Subjects were then asked to complete five continuous and
controlled squats with a self selected depth whilst attempting to maintain the original upright trunk
position. A metronome was used to ensure a standardized rate of movement through the
descending and ascending phases of the squat. If the subject failed to ground the non-weight
bearing limb then that was considered a successful trial. The subject repeated this process for the
contralateral leg and then again on each leg for the flat squat. The initial squat condition was
randomized.
Single Leg Squatting 3-Dimensional Motion Capture: Kinematic measurements of the pelvis, knee
and ankle during the performance of the SLDS and SLFS protocols was assessed with an 8
camera 3-Dimensional Vicon® motion analysis system at a sampling rate of 120 Hz. Reflective
markers were placed on the lower limbs as per Besier and colleagues (2003). In addition to these
markers, additional markers were also secured on the C7 and T10 spinous processes, the sternal
notch, the xiphoid process of the sternum, the anterior superior iliac spines and the posterior

154
superior iliac spines. A custom written kinematic model developed at the University of Western
Australia; using Bodybuilder for Biomechanics software (Oxford Metrics Group, UK), was be used
for kinematic measures.
Data Analysis: The mean (mean angle throughout the squatting protocol) and the mean end of
range (EOR - mean angles for all kinematic variables when the knee was at maximal flexion)
angles were calculated for the weight bearing knee over 4 trials. The EOR the position each
kinematic variable was in at the deepest part of the squat. Both of these measures were deduced
from the last four squats from each set of five. The first squat was excluded from analysis as it was
decided that it may have characteristics of bipedal stance and thus not a true reflection of a single
leg squat. Thirty three (33) Paired samples correlations and paired samples t-tests were performed
between conditions on both weight bearing legs to assess the differences between conditions. A
significance level of <.05 was set.
Results: The results illustrate a number of significant differences between the squatting conditions
for both the mean angles and EOR which can be seen in full in tables 1 and 2 respectively.
Significant differences were observed at most segments of the body, especially the weight bearing
hip which are summarized in table 3. Mean angle and EOR results illustrate differences between
conditions particularly around the weight bearing hip and relative angles between the hip and
thorax.
Table 1: Summary of the functional differences of the SLS conditions
Joint/Segment SLFS relative to SLDS
Thorax/Torso ↓ Flexion
↓ Lateral flexion
Pelvis ↓ Axial rotation away from WB limb
Pelvis relative to Lumbar ↓ Lateral flexion
Pelvis relative to Thorax ↓ Flexion
↑ Axial rotation away from WB limb
WB Hip
↓Flexion (Mean)
↑Adduction
↑ Internal femoral rotation
NWB Hip ↑Flexion
↑Adduction
Knee
↓Flexion
= Valgus/Varus
↓ Tibial internal rotation (at EOR)
Ankle ↓Flexion
Discussion: As mentioned, the SLS is commonly used by clinicians as a functional measure of
dynamic lumbo-pelvic control (Kibler et al., 2006; Mitchel et al., 2003; Willson et al., 2006; Zeller et
al., 2003). Non optimal lumbo-pelvic control has been characterised by the presence of excessive
femoral internal rotation, femoral adduction, knee valgus, tibial internal rotation and foot pronation
of the weight-bearing limb with resultant excursion of the contralateral non weight bearing Ilium and
excessive lateral flexion of the trunk (Cannel et al., 2001; Kibler et al., 2006; Mitchel et al., 2003;
Willson et al., 2006; Zeller et al., 2003). All of these aspects were altered between the two
conditions however not consistently.
The mean angles outlined in table 1 illustrate a number of relationships between different kinematic
variables and the different squatting conditions. Subjects maintained greater flexion angles for the
thorax, WB hip, knee and ankle in the SLDS indicating subjects stayed in a deeper squat for
longer. Greater mean flexion angles at the WB hip were coupled with increased femoral adduction
and internal rotation angles. However, there was no statistical difference in coronal plane

155
movements at the knee but rather greater axial rotation of the pelvis away from the WB limb. When
these kinematic differences are pooled it illustrates that the introduction of the decline board
directly affects not only the ankle but also the kinematics about the hip joint in all three planes.
The non-weight bearing (NWB) hip flexion and hip adduction angles were larger in the SLFS. This
coupling may be a result of the hip being closer to the ground during the SLFS consequently
drawing the NWB hip towards the midline of the body as to not allow ground contact with the
contralateral foot. Constraints of the calf and ankle during the SLFS may also cause greater levels
of instability which may manifest itself in the use of the non WB hip as a counter balance to any
lateral flexion of the trunk.
The disparity between the mean angle and the EOR analyses were minimal but there were two
noteworthy variations in the EOR analysis. The first variation is the lack of any significant
difference in the WB hip flexion angle. This may indicate that whilst subjects stayed more flexed
during the entire task, the EOR hip flexion angles were not significantly different. This result was
surprising given that it was originally hypothesized that this was one area that subjects could
manufacture depth during the SLFS in the absence of greater knee and ankle flexion. The second
variation was the internal rotation of the tibia relative to the femur being significantly smaller in the
SLFS. This is most likely due to the formation of the anatomical structures of the ankle in particular
the talocrural joint which limits rotation of the tibia when fully dorsiflexed.
In summary, when the results from the trunk, hip, knee and ankle are pooled they clearly illustrate
the differences between the two conditions. The decline squat allowed for maintaining a greater
squat depth as characterized by greater mean flexion angles in the torso, WB hip and knee. These
increases were not coupled with dorsiflexion of the ankle attributable to the 20° slope of the to the
decline board. The flat squat has a similar WB hip EOR flexion angle but hip adduction and internal
rotation angles are significantly different in the SLDS. It may therefore be easier to clinically
recognise poor movement patterns in the decline squat due to the altered kinematics of the SLS. It
is worth noting however that the significant differences only ranged between 2° and 5° of actual
movement. It is unclear if these significant kinematic differences translate into noticeable variation
at the clinical level. Nonetheless, the findings of this study support the use of the SLDS as a
screening tool in physiotherapy protocols.
References
Besier, T. F., Sturnieks, D. L., Alderson, J. A., & Lloyd, D. G. (2003). Repeatability of gait using a
functional hip joint centre and a mean helical knee axis. Journal of Biomechanics, 36, 1159-1168.
Cannel, L. J., Taunton, J. E., Clement, D. B., Smith, C., & Khan, K. M. (2001). A randomized
clinical trial of the efficacy of drop squats or leg extension/leg curl exercises to treat clinically
diagnosed jumper's knee in athletes. British Journal of Sports Medicine, 35(1), 60-64.
Frohm, A., Halvorsen, K., & Thorstensson, A. (2007). Patellar tendon load in different types of
eccentric squats. Clinical Biomechanics, 22, 704-711.
Kibler, W. B., Press, J., & Sciascia, A. (2006). The role of core stability in the athletic function
Journal of Sports Medicine, 36(3), 189-198.
Kongsgaard, M., Aagaard, P., & Roikjaer, S. (2006). Decline eccentric squats increases patellar
tendon loading compared to standard eccentric squats Clinical Biomechanics, 21, 748-754.
Mitchel, B., Colson, E., & Chandramohan, T. (2003). Lumbopelvic Mechanics. British Journal of
Sports Medicine, 37(3), 279-280.

156
Purdam, C. R., Johnsson, P., Alfredson, H., Lorentzon, R., Cook, J. L., & Khan, K. M. (2004). a
pilot study of the eccentric decline squat in the management of painful chronic patellar
tendinopathy. British Journal of Sports Medicine, 38, 395-397.
Richards, J., Thewlis, D., Selfe, J., Cunningham, A., & Hayes, C. (2008). A biomechanical
investigation of a single-limb squat: implications for lower extremity rehabilitation exercise. Journal
of Athletic Training, 43(5), 477-482.
Willson, J. D., Ireland, M. L., & Davis, I. (2006). Core strength and lower extremity alignment during
single leg squats. Medicine & Science in Sports & Exercise, 38(5), 945-952.
Young, M. A., Cook, J. L., Purdam, C. R., Kiss, Z. S., & Alfredson, H. (2005). Eccentric decline
squat protocol offers superior results at 12 months compared with traditional eccentric protocol for
patellar tendinopathy in volleyball players. British Journal of Sports Medicine, 39, 102-105.
Zeller, B. L., McCrory, J. L., Kibler, W. B., & Uhl, T. L. (2003). Differences in kinematics and
electromyographic activity between men and women during the single-legged squat. . American
Journal of Sports Medicine, 31(3), 449-456.
Zwerver, J., Bredeweg, S. W., & Holf, A. L. (2007). Biomechanical analysis of the single-leg decline
squat. The British Journal of Sports Medicine, 41, 264-268.

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Day 3 Thursday 3 June

158
THURSDAY 3 JUNE
Parallel Seminars
8.30 am Spine in Sport 1
Invited Speaker: Dr.
Matthew Scott-Young (45
mins)
Invited Speaker: Prof.
Peter O’Sullivan (45 mins)
Moderator: Alex Kountouris
Grand Ballroom 4
Technology in Sport
Invited Speaker: Dr. Daniel James:
Technology in Sport: Past, Present, Future
(25 mins)
Invited Speaker: Dr. Andrew Wixted:
Sensors to measure illegal bowling actions
– The ICC project (25 mins)
Invited Speaker: Prof. Franz Fuss: Smart
Balls in Sport (25 mins)
Richard McInnes: Using Technical
Information in a Practical Environment (15
mins)
Moderator: Richard McInnes
Grand Ballroom 1
Coach Longevity
Invited Speaker: Kevin
Sheedy (30 mins)
Invited Speaker: John
Wright (30 mins)
Q&A from the floor (30 mins)
Moderator: Greg Chappell
Grand Ballroom 2
10.00am Morning Tea
10.30 am KEYNOTE ADDRESS 9: Greg Chappell
Developing and managing talent in the changing cricket landscape Grand Ballroom 2
11.30 am Seminar: Bridging the gap between domestic and international cricket Grand Ballroom 2
- Invited Speaker: Ewen McKenzie: Bridging the gap: provincial & international rugby (25 mins)
- Panel: Ewen McKenzie, Tim Nielsen, Alex Kountouris, Ryan Harris, Ross Chapman, Stuart Karppinen
(35 mins)
- Moderator: Ian Healy
12.30 pm Lunch
Parallel Seminars
1.00 pm Spine in Sport 2:
Assessment &
management
Prof. Peter O’Sullivan
By registration only
Grand Ballroom 4
The Smart & Resilient Cricketer
Dr. Michael Lloyd – EQI Profiling in
Cricket: a three year project (20 mins)
Invited Speaker: Dr. Luana Main – A new
model to measure athlete stress (20 mins)
Invited Speaker: Dr. Daniel Gucciardi –
Mental Toughness in Cricket (20 mins)
Panel: Ross Chapman, Luana Main, Scott
Cresswell, Daniel Gucciardi (30 mins)
Moderator: Dr. Michael Lloyd
Grand Ballroom 2
The Battle Zone: Game
Based Training in Cricket
Greg Chappell
Ian Renshaw
Darren Holder
David Fitzgerald
John Davison
Starts in Grand Ballroom 1 for
intro presentation (15 mins)
and then moves to Tennis
Court (60 mins)
2.30 pm Seminar: Workload restrictions for young fast bowlers: are they doing more harm than good?
- Panel: John Orchard, Patrick Farhart, Aaron Kellett, Alex Kountouris, Bruce Elliott, Damian Farrow,
Peter O’Sullivan, Geoff Lawson, Craig McDermott, Troy Cooley
- Moderators: Marc Portus and Stuart Karppinen Grand Ballroom 2
3.15 pm - 3.30 pm CONFERENCE CLOSING Grand Ballroom 2

159
The Spine in Cricket
Peter O’Sullivan
School of Physiotherapy, Curtin University of Technology, Perth
Correspondence: p.osullivan@curtin.edu.au
LBP is a common health problem that affects up to 80 % of people across their life time. A small
group of these disorders is associated with patho-anatomical findings such as fractures,
spondylolisthesis and degenerative disc with modic changes. For the majority no clear patho-
anatomical basis is found. The prevalence of LBP increases rapidly across adolescence to reach
adult rates in the late teens. Risk factors for the development of LBP are multidimensional – with
physical, lifestyle, neuro-physiological and psychosocial factors known to increase risk (O’Sullivan
2005).
It is well known that sports that involve cyclical spinal loading coupled with rotation and / or side
bending are associated with a higher prevalence of patho-anatomical changes to spine’s structures
and low back pain (LBP). Fast bowlers in cricket have a high prevalence of stress reactions /
fractures of the pars interarticularis and multi-level degenerative disc changes (Elliott et al 1986,
Ranson et al 2005). Stress fractures and reactions of the pars interarticularis occur on the side
contra-lateral to the bowling arm (Ranson et al 2007).
Recent research has revealed that fast bowlers couple contra-lateral side flexion, rotation and
extension beyond their normal physiological range close to front foot contact - highlighting the huge
forces generated on these spinal structures. The amount of side bending imposed on the spine
with other movement coupling, during fast bowling, has been proposed as a potential risk factor for
stress fractures (Ranson et al 2007).
Risk for injury will be potentially influenced by a number of factors such as:
• individual motor control issues involving shoulder, thorax and pelvic / hip region
• spinal structure resilience
• bowling action (side vs front on) (Elliott and Foster 1984)
• bowling volumes
In a series of studies, neither spinal kinematics, bowling action nor muscle asymmetry predicted
low back pain or stress fracture in a cohort of fast bowlers (Ranson et al 2007, Ranson et al 2008).
Recent research also highlights that the risk for back pain and the presence of chronic back pain is
often associated with increased spinal stability generated by increased activity of spinal stabilising
muscles and their inability to relax (Dankaerts et al 2009), questioning current beliefs and practice.
Based on these findings, it is likely that multiple rather than single factors, are related to increase
risk of injury, and these factors may be different for each player. This reinforces the importance of
individual specific investigation of risk and tailored management approaches rather than generic
approaches to prevention and intervention. This approach pays attention to movement efficiency
and control through the whole kinetic chain with the aim to minimise stress on spinal structures,
and is integrated into sports specific conditioning programs. Addressing bowling action and
managing training volume is also considered.
This concept is supported by recent findings of different subgroups based on body postures and
movement patterns relating to different back pain presentations (Dankaerts et al 2009, O’Sullivan
2005, Smith et al 2008). Furthermore tailored interventions that identify and target the risk factors

160
identified for each person have the capacity to prevent and manage LBP with greater effect than
generic approaches to management (Perich et al 2010, Fersum et al 2010).
Management approaches that are specific, functionally targeted and multidimensional hold hope
for the management and prevention of LBP.
References
Elliott BC, and Foster D. (1984) A biomechanical analysis of the front-on and side-on fast bowling
techniques. Journal of Human Movement Studies. 10:83-94, 1984.
Elliott BC, Foster D, and Gray S. (1986) Biomechanics and physical factors affecting fast bowling.
Australian Journal of Science and Medicine in Sport. 18:16-21, 1986.
Dankaerts, W., O’Sullivan, P.B., Burnett, A.F. and Straker, L.M. (2009) Discriminating healthy
controls and two clinical sub-groups of non-specific chronic low back pain patients using trunk
muscle activation and lumbo-sacral kinematics of postures and movements - A statistical
classification model, Spine, 34(15):1610-8.
Fersum K, O’Sullivan P, Skouen J, Smith A, Kvåle A, (2010) Efficacy of classification based
‘cognitive functional therapy’ in patients with Non-Specific Chronic Low Back Pain – A randomized
controlled trial, Proceedings of Norwegian Spine Research Association - Multidisciplinary
Conference, April, Oslo, Norway.
O’Sullivan, P.B. (2005) Diagnosis and classification of chronic low back pain disorders-
Maladaptive movement and motor control impairments as underlying mechanism. Manual
Therapy 10, 242-255.
Perich D, Burnett A, O’Sullivan P, Perkin C., (2010) Low back pain in adolescent female rowers: a
multi-dimensional intervention study, Knee Surgery, Sports Traumatology, Arthroscopy, in press
Ranson C, Kerslake R, Burnett A, Batt M and Abdi, S. (2005) Magnetic resonance imaging of the
lumbar spine of asymptomatic professional fast bowlers in cricket. Journal of Bone and Joint
Surgery. 87-B:1111-1116.
Ranson, C., Burnett, A., King, M., Patel, N. and O’Sullivan, P. (2007). The relationship between
bowling action classification and three-dimensional lumbar spine motion in fast bowlers in cricket.
Journal of Sports Sciences, 26(3):267-76.
Ranson CA, Burnett AF, O’Sullivan PB, Batt ME and Kerslake, RW. (2008) The lumbar paraspinal
muscle morphometry of fast bowlers in cricket. Clinical Journal of Sport Medicine, 18:31–37.
Smith A, O’Sullivan P, Straker L (2008) Classification of sagittal thoraco-lumbo-pelvic alignment of
the adolescent spine in standing and its relationship to low back pain, Spine, 33 (19) 2101-7.

161
Technology in Sports Assessment: Past, Present and Future
Daniel James1,2, Andrew Wixted1
1 Centre for Wireless Monitoring & Applications, Griffith University, Brisbane
2 Centre of Excellence For Applied Sport Science Research, Queensland Academy of Sport,
Brisbane
Correspondence: d.james@griffith.edu.au
Technology continues to transform many aspects of our lives. The adoption of technology into
sport is no exception where advances in materials, equipment design, clothing and more recently
micro sensors have all had an impact on the practice and performance of sport. Today, micro
sensors have been adopted by companies such as Nike, Apple, Nintendo and Polar as consumer
technologies for diverse markets such as gaming, telecommunications and sports. The range of
sports where micro sensors have been used is extensive, accelerometers have been used in half-
pipe snowboarding to detect air time and activity, in rowing to monitor athlete biomechanics and
boat movement through the water, in running to generate force-plate simulations, contact time,
step rate and other biomechanical information, in football to estimate energy expenditure, in
swimming to count laps, monitor lap times and stroke rate. Coupled with high precision GPS these
sensors give researchers the means to monitor the position, orientation, activity velocity and
aspects of biomechanics of runners, skiers, football players and rowing sculls just to name a few.
In many cases this has provided new information or previously difficult to obtain information. The
list is long and growing. For any sport, the question is, what can these sensors do for me? This
might be answered by looking at the limitations of the sensors and the techniques used to extract
information from the sensors. In this paper some examples of miniaturised technology using
primarily inertial sensors are applied to a range of sporting applications yielding field based results
analogous to those achieved in the laboratory but under typical training and sometimes
performance conditions.
Technology: Accelerometers measure changes in motion in three dimensions and are today
millimetres in size. Through numerical integration, velocity and displacement can be calculated. It
is well understood though that the determination of position from acceleration alone is an error-
prone and complex task. Numerous error sources exist and the even the simplest error source
such as white noise, can result in ever increasing errors. This is demonstrated by a random walk
(drunkard’s walk) simulation (Fig.1(a,b)). Other small errors such as small misalignments (Fig.1(c))
or calibration errors (Fig.1(d)) can also affect positional output quite dramatically.
Thus accelerometers are often only used for short-term navigation and the detection of fine
movement signatures, features (such as limb movement) and temporal discrimination of events
(e.g. ground contact). Accelerometers can be used to determine orientation with respect to the
earth’s gravity as components of gravity are aligned orthogonal to the accelerometer axis. In the
dynamic sports’ environment, complex physical parameters can be derived in relation to running
and stride characteristics and in the determination of gait
These methods have been able to offer comparable results to expensive optical based systems
and can be used anywhere. Rate gyroscopes, a close relative of the accelerometer, measure
rotation about a single axis and can also used to determine orientation in an angular co-ordinate
system, although these suffer from not being able to determine angular position in the same way
accelerometers have trouble with absolute position. Additionally many physical movements, such
as lower limb movement in sprinting, exceed the maximum specifications in commercially available
units that are sufficiently small and inexpensive for such applications.

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Figure 1: Examples of Random Walk errors for (a) rate-gyroscope output integrated to degrees,
(b) accelerometer output double integrated to metres. (c) Comparison of Random Walk errors from
(B) and error due to 0.5 degree misalignment of horizontal accelerometer. (d) Bias, non-linearity
and calibration errors.
Challenges: In general sensors that are mounted on the athlete or on something related to the
sport such as a boat, a bicycle, a ball or a bat. In each case the sensor operates within a moving
sensor frame of reference and is subject to vibration or skin artefact. There is little knowledge of an
external frame of reference for inertial sensors. Magnetometers and stationary accelerometers are
an exception to this. 3D-magnetometers can be orientated with respect to the earth's magnetic
field and stationary accelerometers can be oriented with respect to the earth's gravitational field.
Under specific circumstances of repetitive movement as occurs in running or swimming, the signal
related to the movement can be filtered off and the underlying orientation information extracted.
Although not a MEMS sensor, the Global Positioning System devices are now small enough to be
incorporated in athlete mounted sensor systems and can help solve these problems.
Interpretation: Acceleration and rotational velocity are not easy to intuitively understand, nor can
they easily be converted to more conventional measures. The real strength of these sensors is in
recognising repeatable signatures of movement and temporal event markers of athlete
movements. In these cases an accelerometer used to detect rate information, such as stride or
stroke rate, does not require calibration. Similarly, detecting timing between closely timed impact
events requires no calibration as the accuracy of activity detection is governed by the accuracy of
the system oscillators, typically better than 0.01%.
Other activities can also be robustly measured using data fusion (the combining of data from
multiple synchronised sensors). A series of sensors mounted on the foot, shin, thigh and sacrum
can trace the impact transmission through various limb segments and information about arm
movements (throwing or bowling) can be similarly deduced. Rate-gyroscopes mounted on different
limb segments can track when the limb is operating as a single unit and when there is joint rotation.
Applications in Sport: The application to particular sports involves identifying the various signal
signatures available from the sensors and identifying how this signal signature can be exploited.
This is the key to success with these sensors, recognising and applying sport specific expertise to
understand and interpret the data. A traditional use of accelerometers is for estimating energy
expenditure by integrating the acceleration signal over various epochs. For normal daily activities
this results in values that correlate well with energy expenditure. In sporting activities this method
does not correlate well with energy expenditure due to the different mechanisms generating the
signal. In field activities or training activities where running is common, energy expenditure can be
estimated from the rate of activity, in particular the step rate. For sub-maximal running, combined
step-rate and mass correlate highly with energy expenditure (Wixted et. al, 2007).
In Australia this was investigated primarily through an Australian research consortium (CRC for
Microtechnology circa 2000-2006) sports science, engineering and commercialisation teams had

163
early success in a number of sports, national and internationally this research continues. Rowing
was trialled initially because of a number of advantages. Firstly rowing is predominantly a 1-D
activity thus it was hoped that inertial navigation might be possible. Secondly the monitoring
equipment could be mounted to the scull rather than the athlete simplifying packaging and device
size constraints. Thirdly, and importantly, rowing is a technical sport and already uses a number of
technologies in the training environment, thus there was little cultural change required by athletes
and coaches to adopt a newer technology. Data from the accelerometers proved useful in
identifying stroke phase characteristics such as the catch, drive and recovery phases, something,
which is difficult to do even with a video system. From these basic measures such as stroke rates
and counts could easily be extracted and combined together with GPS and other data such as
heart rate to analyse performance and further develop race strategy.
Figure 2: (a) Prototype Accelerometer Platform (b) Acceleration outputs for swimming analysis
The technology was then encased in water proofing material and applied to trunk movement in
swimming. Identification of stroke type, stroke counts, lap times and tumble turns using a model of
body roll dynamics was constructed allowing the development of automated algorithms to extract
performance characteristics and stroke identification that exceeded hand timed and counted data
and was comparable with underwater video and touch pad equipped pools (Davey, Anderson,
James, 2008). Recently the device has been applied to winter sports to detect and quantise aerial
activity time and type in ski and snowboard events and produce automated scores that correlate by
80% with international judges (Harding et al, 2008). Implements used in sports have also been
instrumented showing that key characteristics of swing can be measured and extracted, and that
they and correlate well with athlete skill this has recently been investigated as a biofeedback
training tool.
Applications in Cricket: A variety of training and competition sensor implementations have potential
within cricket, many are currently under development. These have applications in skill development
and in injury prevention.
Skills development:
(a) A spin-tracking ball using accelerometers or gyroscopes for the development of bowler
technique.
(b) An intelligent bat for the development of batting skills.
(c) A combination of arm mounted accelerometers and gyroscopes for the monitoring of the
bowling arm action including elbow extension.
Injury Prevention:
(a) During running some athlete’s exhibit high impact forces transmitted to the spine while
other athletes do not. This monitoring could be implemented with bowlers, detecting the
forces transmitted during the delivery stride and identifying limits that may be related to the
onset of injury. These sensors can be integrated with in sole pressure sensors to track the
force chain from initial contact to post delivery.

164
(b) To minimise bowler back injury, laboratory testing is used to detect upper body rotation
during bowling. This testing could be replaced or augmented using MEMS sensors.
(c) Fitness training monitors can be used to estimate the duration and intensity of training
activities.
Discussion: Looking to the future technology continues to develop at what seems to be an
accelerating pace. Moore’s law, (named after a Caltech professor in the 1950’s) said that
complexity would double every year, the law has proved remarkably accurate to this day. Ever year
the size, computing power, availability and cost of technology changes steadily. This has lead to
advancement and convergence of many technologies of interest to the sporting community such as
the mobile telephone, computer, video and still cameras to name but a few. Communications
infrastructure for such devices has lead to previously unthought-of applications and the athlete
today is no exception. Mobile phones such as the iPhone are popular technologies today, with
plenty of computing power, jammed full of micro sensors and already in the hands of many
sporting professionals and athletes. Can we put them to work for athlete monitoring?
Historically an engineering approach to using technology for sporting applications has yielded
many interesting papers and ideas but little success in the sporting arena. However informed
signal processing of the data through the use of sport specific knowledge and involvement of sport
scientist has allowed the extraction key features in the data which can then be interpreted in a
useful manner. Critical to the success of these endeavours is to ensure that the development of the
technology has been in partnership with key stakeholders including athletes, coaches and sport
scientists and organisations. Keeping the technology development and interpretation firmly
grounded on providing useful outputs that benefit athletes has been critical to the ultimate goal of
enhancing sporting activity.
References
Neil Davey, Megan Anderson, Daniel A. James, Validation trial of an accelerometer-based sensor
platform for swimming, Sports Technology 2008; 1 (4) pp 202-207
Harding JW, Mackintosh CG, Martin DT, Hahn AG, James DA. Automated Scoring for Elite Half-
Pipe Snowboard Competition - Important Sporting Development or Techno Distraction? Sports
Technology 2008; 1 (6): pp277-290
David Rowlands, Daniel A. James and David Thiel, Bowler, Analysis in Cricket using Centre of
Mass Inertial Monitoring, Sports Technology 2009: pp39-42.
Andrew Wixted, David V. Thiel, Allan, Hahn, Chris Gore, D. Pyne, Daniel A. James, "Measurement
of Energy Expenditure in Elite Athletes using MEMS based inertial sensors", IEEE Sensors, Vol 7,
No4, pp481-8, April 2007

165
Wearable Sensors for on Field near Real Time Detection of Illegal Bowling Actions
Andrew Wixted1, Wayne Spratford2,3, Mark Davis2, Marc Portus3, Daniel James1,4
1 Centre for Wireless Monitoring & Applications, Griffith University, Brisbane
2 Biomechanics Department, Australian Institute of Sport, Canberra
3 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
4 Centre of Excellence for Applied Sports Science Research, Queensland Academy of Sport,
Brisbane
Correspondence: a.wixted@griffith.edu.au
Introduction: A well-developed and legal bowling action is of enormous importance to developing
cricket athletes and teams both nationally and internationally. Good bowlers can make the
headlines, but so do the controversial ones. The bowling action can currently only be measured
with accuracy in a few specialist laboratories around the world. Testing requires a break in a
player’s schedule, travel to a distant testing centre and players are then asked to bowl in an
unnatural environment. This testing is therefore somewhat expensive and not routinely accessible,
but this process is the best method currently available.
Recently developed micro technologies have been used in athlete performance monitoring,
biomechanical monitoring and physiological monitoring in other sports such as swimming
(Kavanagh et.al. 2006, James et.al. 2007, Davey et.al. 2008), snowboarding (Harding 2008),
running (Wixted et.al 2007, 2010) and also in cricket (Rowlands et.al. 2009). These technologies
offer the promise of wearable alternatives to laboratory testing that would be inexpensive enough
to be used routinely by elite and developmental players and coaches. In light of the above, real
time, non-laboratory monitoring of the bowling arm-action by miniature sensors is a potential
solution to the disruptive issues caused by the processes currently in-place to assess perceived or
actual throwing. Current and future miniature sensors are achieving, or will soon achieve, the
sensitivity and range of operation necessary to monitor athletes operating at the elite levels of
many sports. In the case of monitoring the arm action of bowlers, a number of checkpoints needed
to be achieved to verify that miniature sensors could supply the required level of monitoring. The
verification process involved testing with actual sensors and the use of “virtual” sensors generated
from the existing library of Cricket Australia motion capture data (Vicon). These combined tests
identified some of the sensor operational ranges and the feasibility of identifying the illegal arm
actions using miniature sensors.
Sensor systems have a very restricted view of the world and this limits their ability to identify
activities that seem obvious to an onlooker. Sport involves movement and the sensors
predominately used for measuring kinematic activities are accelerometers and rate gyroscopes (to
measure rotation). These sensors are often arranged on three orthogonal axes to capture activity
in three dimensions (3D). The accelerometers and rate-gyroscopes are usually Micro-Electro
Mechanical Systems (MEMS) and are manufactured, along with support electronics, directly in
silicon with sizes of 3D accelerometers as small as 5x3x1mm. Gyroscopes have similar sizes. The
combination of 3D accelerometers and gyroscopes and the fast sampling rate of sensor systems
allow the sensor systems to capture information about a small segment of the physical world yet
this small segment is often sufficient to determine the parameters of interest.
To monitor a bowler for illegal bowling it is necessary that the sensor can identify specific points of
the arm action, in particular the start and end of the arm action. The start of the arm action occurs
when the elbow is level with the shoulder, the end of the arm action occurs when the ball is
released. Between these two points the sensor needs to monitor the critical aspect of elbow
extension. Although stationary 3D accelerometers can detect orientation, the complex forces
affecting a moving limb prevent the direct extraction of orientation therefore; a simple orientation

166
based detection of start of arm action is not possible. The ball release point appears to occur
around 120 degrees after the start of arm action. Other than some sort of finger tip pressure sensor
there does not appear to be any obvious sensor measure that can detect ball release. Finally, the
detection of elbow extension: this does appear amenable to detection by miniature sensors. Using
data from multiple accelerometers or multiple gyroscopes, elbow extension should be detectable
as a centrifugal acceleration phase difference or a rotation rate phase difference between upper
arm and forearm mounted sensors.
To ascertain the feasibility of MEMS based sensor monitoring of bowling arm elbow extension, the
dynamics of the bowling arm were investigated using ‘virtual’ sensors. The virtual sensors were
also used to determine the working range required for physical sensors.
Virtual Sensors: 120 Hz Vicon motion capture data from numerous bowlers performing a range of
deliveries was processed to generate the output of virtual sensors. This process involved selecting
specific Vicon markers and converting their position data from an external ‘cricket-pitch’ based
Frame of Reference (FOR) to acceleration data and angular velocity data in a sensor based FOR.
The markers involved were part of a semi rigid technical marker set. The process of creating the
virtual sensors involved double differentiation of the position information to convert it to
acceleration. For each frame of Vicon data the orientation of the technical marker relative to the
external FOR was extracted and the orientation used to transform the external FOR acceleration
into sensor FOR acceleration. As the process of transforming acceleration from one FOR to
another involved generating rotation angles, the sequence of rotation angles was differentiated to
give rotation rates (angular velocity).
The data from the virtual sensors represented the outputs of accelerometers and rate-gyroscopes
had they been fitted in the location of the technical marker when the original motion capture data
was collected. This data was analysed and a very clear ball-release signature was identified
indicating that real sensors should be able to identify the ball release point (Fig.1(a)). Not included
in Fig.1(a) were two illegal bowls where there was a distinctive phase difference between the
upper-arm and forearm peak acceleration.
No specific start of arm-action was initially identified in 120Hz Vicon data but when data from a 250
Hz Vicon system was processed many deliveries showed a spike of very high angular velocity on
or about the start of the bowling action (Fig.1(b)). Video replay indicated that this was associated
with the elbow extension and shoulder rotation that brings the ball into the upward facing direction
(a supination action) just prior to the start of the arm action. This shoulder rotation is necessary to
prepare the shoulder joint for the delivery action.
Figure 1: (a) Centrifugal Acceleration for right arm bowlers at ball release, (b) Example of two axes
of angular velocity with a spike of angular velocity at the start of the arm action.

167
Despite the apparent confirmation of the existence of potential arm-action start and end signatures,
the Vicon data is subject to its own processing errors and these signatures need to be confirmed
with actual sensors. The Vicon data does give indicative acceleration and angular velocity:
• Forearm Centrifugal Acceleration: > 70g ( ~ 700ms-2)
• Forearm Angular Velocity: > 3500 deg/s
Both these values are achievable by current technology.
Physical Sensor: The positions of the Vicon technical markers were not the optimal positions for
actual sensors attempting to detect elbow extension. In a separate experiment rate-gyroscopes
were used to synchronously collect data from either side of the elbow joint. Elbow extension and
flexion was performed through a bowling action. From the gyroscopes, an arm starting in the
already flexed position and extending through the bowl showed peak rotation rates for the forearm
occurring prior to the upper-arm peak rotation (figure 2). An arm staying straight through the
bowling action and then flexing after release showed the gyroscopes for the upper arm and
forearm tracking together until the flexing started (figure 3). The gyroscope output for an arm
starting straight but flexing through the action showed the sensors not even tracking together with
the forearm’s sensors peaking sometime after the upper arm sensor (figure 4).
Figure 2: Exaggerated stick figure representation and gyroscope output for an arm starting the
bowling action flexed and extending through to release.
Figure 3: Exaggerated stick figure representation and gyroscope output for an arm starting the
bowling action straight and beginning to flex at release.
Figure 4: Exaggerated stick figure representation and gyroscope output for an arm starting the
bowling action straight and flexing through the bowl.

168
Where the arm remains straight through the bowling action it appeared that the outputs of the
sensors tracked together. If a single flexion or extension occurred the phase relationship between
the forearm and upper arm sensors was distinctly different. At this time the effect of a complete
flexion-extension action during the bowling has not been analysed although study of the historical
data indicates that this type of action occurs for some bowlers.
Similar data has been collected from accelerometers and although the accelerometers used for
these experiments were overloaded by the bowling action, the phase shifting between upper-arm
sensors and forearm sensors for flexion-extension was identified in the output.
Summary: Analysis from virtual sensors indicated that arm action start and end points have
identifiable signatures that can be utilised by miniature sensors. The virtual sensors also indicated
the signal magnitudes. For some types of illegal bowling action, the virtual accelerometers
identified a phase shift between forearm and upper-arm acceleration. Analysis of the output of
physical sensors indicated that changing elbow extension during the bowling arm action is
identifiable in the sensor output. These factors have become the input to the development of
sensors specifically designed to capture the bowling action and extract elbow extension. The next
phase of this development is the confirmation of sensor operation using simultaneously collection
of data from both the sensors and the motion capture system. This step will also begin the
development of the mapping from sensor phase shift to elbow angle
Acknowledgements: The authors acknowledge the funding for this project from the ICC and the
Marylebone Cricket Club (MCC) as well as the support and historical data from CA and the AIS.
References
Kavanagh JJ, Morrison S, James DA, Barrett R, Reliability of segmental accelerations measured
using a new wireless gait analysis system, Journal of Biomechanics, Vol 39, pp2863-72, 2006
James DA, Davey N, Hayes J, From conception to reality: A wearable device for automated
swimmer performance analysis, Japan Society for Sciences in Swimming and Water Exercise 11th
Annual Conference proceedings, pp101-109. 2007
Davey N, Anderson M, James DA, Validation trial of an accelerometer-based sensor platform for
swimming, Sports Technology, 2008; 1 (4) pp 202-207
Harding JW, Macintosh CG, Martin DT, James DA, Classification of aerial acrobatics in elite half-
pipe snowboarding using body mounted inertial sensors, The Engineering of Sport, 2008, Estivalet,
Brisson Ed. pp447-456 Paris: Springer
Wixted AJ, Thiel DV, Hahn A, Gore C, Pyne D, James DA, Measurement of Energy Expenditure in
Elite Athletes using MEMS based inertial sensors, IEEE Sensors Journal, Vol 7, No4, pp481-8,
April 2007
Wixted AJ, Billing DC, James DA, Validation of trunk mounted inertial sensors for analysing
running biomechanics under field conditions, using synchronously collected foot contact data.
Sports Engineering, 2010, In Press.
Rowlands D, James DA, Thiel DV, Bowler Analysis in Cricket using Centre of Mass Inertial
Monitoring, Sports Technology, 2009, (21-2), pp39-42.

169
Smart Balls: Design and Application
Franz Konstantin Fuss
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University,
Melbourne
Correspondence: franz.fuss@rmit.edu.au
Instrumentation in sports extends to three areas: sports facilities, athletes and equipment.
Instrumentation of facilities ranges from simple cameras to more sophisticated systems such as
the Hawk Eye system. Instrumentation of athletes is often compromised by attaching sensors to
the skin and body segments, which can affect the performance, whereas instrumentation of
equipment hinges on the rules of sport.
Smart, or instrumented, sports equipment serves three purposes (Fuss 2008):
• Sports applications: optimisation of training and quantification of performance, as well as
player selection and prevention of injuries;
• Engineering applications: testing and design optimisation;
• Business and media applications: mobile digital devices for live broadcasts, distribution of
data and their statistics (TV and stadium screens, mobile phones).
Simple smart balls were introduced to find their location and measure their speed. RadarGolf
produced an instrumented golf ball where a handheld emits a specific radio frequency signal
returned by the ball in order to locate hidden and lost balls. Commercially available speed sensor
balls like Platypus (cricket ball) and Markwort (baseball) are used for measuring the bowling or
pitching speed, however, they crack when struck by the bat and cannot be reused.
The development of more sophisticated devices involve the following steps (Fuss 2008):
• appropriate design of the equipment including selection of suitable sensors and
transducers,
• means for data transfer (preferable wireless) and signal processing,
• identification of signal parameters which represent, i.e. highly correlate with the
performance,
• providing easily comprehensive information to athletes, coaches, and spectators, e.g. by
graphical representation of performance parameters,
• incorporation of optional biofeedback systems for improvement and optimisation of training,
and
• commercial product design aiming at live broadcast of selected data during sports events.
Balls instrumented with sensors range from golf balls and bowling balls over cricket balls and
baseballs to footballs and even pucks.

170
Accurate Technologies, Inc. has embedded a data transmission system and several sensors
(accelerometer, infrared, vibration, GPS) into golf balls (Erario and Erario 2006).
A prototype of a smart cricket ball was developed by Iksens (2003) instrumented with capacitative
touch sensors for determining the exact time of ball release by the bowler. Harland (2008)
instrumented a cricket ball with a gyrometer for quantifying the angular velocity (spin rate) of a
cricket ball. The gyro, however, was not selected appropriately and its detectable range was
beyond the spin rate. Further disadvantages were the mass and size of the power source and
distance of wireless transmission. Koyanagi and Oghi (2008) inserted a commercially available
wireless accelerometer into a baseball and analysed the spin rate in a pitching experiment.
Nowak et al. (2004) embedded a triaxial accelerometer into an American football to measure spin
and wobbles along a football’s trajectory. Cairos Technology, Adidas-Salomon and the Fraunhofer
Institute applied magnetic-field detection to a soccer ball. Once a ball crosses the goal line, a
signal is sent to the referee’s watch. Carnegie Mellon University produced a smart NFL ball
(American football), instrumented with a 2.4GHz ZigBee radio with an optimized 8051
microprocessor, an Analog Devices ADXL330 triple-axis accelerometer and a 500mAh lithium
polymer battery (Goldhammer et al. 2009). The instrumentation served to classify and evaluate
various football actions and to discriminate between throwing and other actions.
The FoxTrax system applied an IR sensor incorporated in a hockey puck. The puck's path was
highlighted on TV broadcasts to improve the spectators’ ability to follow the course of a hockey
game (Cavallero 1997).
Fuss (2009) instrumented a tenpin bowling ball with three 6-DOF force and moment transducers
(Figure 1) and measured the forces and moments applied to the finger and thumb holes, replaced
by tubes connected to the transducers. The forces and moments were displayed as 4D vector
diagrams (with the time colour-coded). The performance parameters (magnitude of pinch grip force
and moments) correlated well with the average score of the athletes.
Figure 1: instrumentation of a tenpin bowling ball; force vector and moment vector diagrams.
The purpose of instrumenting balls is either:
• event-based (ball release time, scored goal), or
• to improve the quality of live broadcasts (highlighting a ball on the screen), or
• to enable tracking of the ball (flight path detection), or
• performance-based (quantification of spin, wobbling, forces or moments).

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The development is a relatively young area of sports engineering and thus still in the experimental
stage. More powerful wireless data transfer, smaller MEMS sensors and longer lasting miniature
batteries will allow measuring a wide range of performance data in the future and will result in more
sophisticated products for optimisation of training and quantification of performance.
References
Cavallaro R. 1997 The FoxTrax hockey puck tracking system. IEEE Computer Graphics and
Applications, 17(2):6-12.
Erario J., Erario R. 2006 System and method for tracking identity movement and location of sports
objects. US Patent US7095312B2.
Fuss F.K. 2008 Instrumentation of athletes and equipment during competitions. Sports
Technology, 1(6): 235–236, DOI: 10.1002/jst.75.
Fuss F.K. 2009 Design of an instrumented bowling ball and its application to performance analysis
in tenpin bowling. Sports Technology, 2(3-4): 24-38, DOI: 10.1002/jst.104.
Goldhammer A.P. et al. 2009 Myron: smart footballs for automated coaching. In: Alam F, Smith LV,
Subic A, Fuss FK, Ujihashi S (Eds), The Impact of Technology on Sport III, RMIT Press,
Melbourne pp 593-597.
Harland A. 2008 Wireless measurements in ball sports. 7th Conference of the International Sports
Engineering Association ISEA, June 2008, Biarritz, France (read by title, no abstract available).
Iksens J. 2003 Smart cricket ball. BEng project thesis, Division of Electrical Engineering, University
of Queensland.
Koyanagi R., Oghi Y. 2008 Measurement of the forces on ball in flight using built-in accelerometer.
The Engineering of Sport, 7, Vol. 2, pp 1-7, Springer, Paris.
Nowak C., Krovi V., Rae W. 2004 Flight Data Recorder for an American Football. In: The
Engineering of Sport 5, Eds: Hubbard M, Mehta RD, Pallis JM, International Sports Engineering
Association, Sheffield, UK.

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Examining How Psychological Factors Contribute to Team Performance in an
Australian National Cricket Competition
Rosanna Stanimirovic1 and Michael Lloyd1,2
1 Psychology Department, Australian Institute of Sport, Canberra
2 Sport Science Sport Medicine Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: mlloyd@coe.cricket.com.au
The aim of the current study was to investigate how psychological factors contributed to team
performance in a sample of male cricket players competing in the Cricket Australia Under 19 Male
National Championships over 3 consecutive years (2007-2009). Research to determine the
predictive validity of the psychological measures that relate specifically to successful performance
in elite level competition is limited (Humara, 2000). To provide a framework for the choice of
measures in a sporting context, performance in competition was considered a job performance
outcome (Stanimirovic & Hanrahan, 2010). Schmidt and Hunter’s (1998) meta-analysis of the
psychological factors that predict job performance outcomes in non-sport contexts recommended
that a measure of general mental ability (GMA) and a supplementary trait measure should be
administered.
Adams and Kuzmits (2008) and Lyons, Hoffman, and Mischel (2009) determined the predictive
efficiency of GMA in the National Football League (NFL) using the Wonderlic Personnel Test
(WPT). The results of both studies demonstrated that GMA was unrelated to NFL performance.
However, despite the conclusive nature of these findings it is suggested that consideration of an
alternative to the WPT is warranted. In the current study, the measure of GMA was the Raven’s
Standard Progressive Matrices (RSPM; Raven, Court, & Raven, 2004). Carpenter, Just, and Shell
(1990) suggested that analytic intelligence requires the individual to reason and solve problems
involving new information, without relying extensively on an explicit base of declarative knowledge
derived from schooling or previous experience in a specific context. Similarly, it was demonstrated
that analytic intelligence measured by the RSPM is central to intelligence and shares considerable
variance with other tests of crystallised or fluid intelligence. Carpenter et al. described the RSPM as
a classic test of analytic intelligence.
The supplementary measure chosen was the the Bar-On Emotional Quotient Inventory (EQ-i)
which is a measure of trait emotional intelligence (EI). The EQ-i measures how an individual copes
with the demands and pressures of an environment which relates well to the competitive
experience. There is evidence for the existence of an EI – performance relationship in professional
sports including baseball (Zizzi, Deaner, & Hirschhorn, 2003), cricket (Crombie, Lombard, &
Noakes, 2009), and ice-hockey (Perlini & Halverson, 2006). Therefore providing further evidence
for the EI – performance relationship in cricket can provide valuable information for the
development of psychological interventions specific to performance enhancement in cricket.
The aims of the current study were to examine whether GMA and trait EI contributed to overall
team performance at the Cricket Australia Under 19 Male National Championships. To facilitate
this investigation, 290 male athletes completed the RSPM and the EQ-I across three consecutive
championships (2007-2009). Data from three participants were excluded due to the entire team not
completing the protocol. In total, data from 22 teams were included in the analyses. The total points
scored by those teams at the end of the championships were used as the performance measure.
For all analyses, the data were screened and outliers removed so the final sample was N = 287.
Normality was then checked for all variables. Regression analyses were conducted using the raw
scores for the RSPM and standardised scores for the EQ-i subscales. GMA was entered as the
first step and the EQ-i subscales were entered as the second step in a stepwise manner.

173
Regression analyses indicated that GMA was not a significant contributor to total number of points
scored by a team. Happiness was a significant contributor (2%) to total number of points scored by
a team. Bar-On (1997) broadly defines the EQ-i subscale of happiness as self-motivation and
more specifically as individuals who feel satisfied with their lives. The EQ-i happiness subscale is
negatively correlated with depression scales and therefore happiness can be described as “not
getting down on yourself” in situations. The relevant statistics for the regression analyses are
included in Table 1.
Table 1: Regression Analyses for Total Points Scored by a Team at the Cricket Australia Under 19
Male National Championships in 2007, 2008, and 2009
B SEB β R
2 ad
j
∆R2
Step 1
GMA
-0.05
0.08
-.04
-0.003 0.000
Step 2
happiness
0.09
0.04
.13* 0.02* 0.02
* p < .05
New South Wales (NSW) and Victoria (VIC) have been the more successful teams over the 2007-
2009 periods and were hence examined more closely. There was no significant contribution of
GMA for either NSW or VIC. The relevant statistics for the regression analyses are included in
Table 2. For NSW over the 3 year period, teams with higher scores on impulse control scored
more total points. Bar-On (1997) defines impulse control as the ability to be composed by
effectively and constructively controlling aggression, hostility, and irresponsible behaviours.
Generally, “keeping a cool head” in all situations. Individuals with low scores may be against
personal change and are rigid in their thinking. For VIC over the 3 year period, teams with higher
scores on happiness scored more total points.
Table 2: Regression Analyses for Total Points Scored by NSW and VIC at the Cricket Australia
Under 19 Male National Championships in 2007, 2008, and 2009
B SEB β R
2 ad
j
∆R2
NSW
impulse control
0.32
0.14
0.42*
0.13*
0.16
VIC
happiness
0.25
0.11
0.38*
0.14*
0.14
* p < .05
The results of the current study demonstrated how psychological factors contributed to team
performance at the Cricket Australia Under 19 Male National Championships. GMA provided no
contribution, whereas The EQ-i subscale of happiness significantly contributed to total team points
scored. For the more successful states (i.e., NSW and VIC), the EQ-i subscales of impulse control
and happiness significantly contributed to more points scored by the team over a 3 year period.
Overall the results suggested that teams with athletes who “don’t get down on themself” and “keep
a cool head in all situations” will perform better. The results are preliminary and more data
confirming the findings is necessary.
The important outcome of the study from an applied perspective is providing further evidence to
athletes and coaches about which psychological competencies lead to successful performance by
cricket teams in competition. The information can also be presented to professional sporting
organisations and used to propose how psychological services can be integrated in a structured
manner.
Acknowledgement: The authors thank the championship organisers, the U19 cricketers, their state
associations and staff for their willingness to participate, Marc Portus who facilitated some of the

174
data collection and analysis and Dr Ian Heazlewood who provided some statistical support.
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Stanimirovic, R., & Hanrahan, S. J. (2010). Psychological predictors of job performance and career
success in professional sport. Manuscript submitted for publication.
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intelligence and performance among college baseball players. Journal of Applied Sport
Psychology, 15, 262-269.

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The Multi-Component Training Distress Scale for Monitoring
Athlete’s Health and Wellbeing
Luana Main
School of Sport Science, Exercise and Health, The University of Western Australia, Perth
Correspondence: lmain@iinet.net.au
It is well supported that overtraining (OT) occurs when there is an imbalance between the stressors
imposed upon an athlete, and the athlete’s ability to adapt to or cope with these stressors.
However, too often athletes are subjected to stressors outside of their training and competitive
environment which can affect their ability to positively adapt to normal training stimuli. Yet
everyone is different, often what is perceived as a threat and evokes a physiological stress
response in one person may not be stressful (perceptually or physically) to another, which is why it
is so important to monitor athletes on an individual basis and adapt training loads wherever
possible.
Overtraining may occur along a continuum from deliberate overload training or overreaching
(functional) to non-functional overreaching and finally overtraining syndrome (OTS) (Meeusen et al.
2006). For example, functional overreaching occurs when periods of intensified training, such as
during a training camp, are performed to induce a super-compensation effect. In comparison, non-
functional overreaching represents the point where the first signs of prolonged training overload
and hormonal disturbances occur. At this point differentiation between non-functional overreaching
and OTS is very difficult and it is suggested that diagnosis of OTS can only be made
retrospectively when the time for recovery is known. With non-functional overreaching,
homeostasis generally can be restored with reductions in training load (for a few weeks); where-as
symptoms of OTS will persist for a far greater period of time (weeks to months), despite training
load reductions. Therefore, overtraining syndrome represent the “prolonged maladaptation” of
several biological, metabolic, neurochemical, and hormonal regulation mechanisms.
Numerous researchers have noted that the borderline between optimal performance and
performance impairment due to OT is subtle. Consequently a number of risk factors which may
contribute to, or prolong the experience of OT have been explored. Factors which have been
identified may be loosely grouped into one of three possible categories: 1) training issues; 2)
situational and environmental stressors; and 3) athlete issues. Training issues are probably the
most commonly cited risk factors in terms of the development of OT. Of particular note is high
volume or high intensity training, which has received a great deal of attention. Other possible
training risk factors that have been proposed include training monotony, lack of periodisation,
and/or a failure to include adequate recovery into the training program.
Thus it is clear that the type of training greatly influences the performance outcome. At the same
time, training factors alone are not the only contributors to the development of OT. Situational and
environmental stressors may also have a significant impact on an athlete’s ability to respond to
training stimuli. For example, poor nutrition, travel (especially across time zones) and jet lag can
have a significant impact on athletic performance, particularly combined with changes in training
environment, altitude, temperature and or humidity. Conflicts with coaches, team-mates, friends or
parents within an athlete’s sporting involvement may also contribute to the development of a
training stress state.
In addition to these stressors, athletes may also be subjected to stressors external to their sport
environment. For example, work, study or relationship stressors may also affect an athlete’s ability
to train and respond to the training stressors. Finally, it has been suggested that premature return
from injury; poor or inadequate sleep and poor or inadequate nutrition (e.g. caloric restriction,

176
insufficient carbohydrate intake, iron deficiency); may all impact on an athlete’s ability to cope with
and positively adapt to imposed training loads. One significant risk factor is a lack of monitoring for
signs and symptoms of OT (Hooper & Mackinnon, 1995). If athletes and coaches are aware of the
early warning signs, then preventative measures can be taken and any increased risk of OT may
be reduced.
While numerous physiological and biochemical symptoms have been proposed as potential
indicators of overtraining, stronger and more consistent relationships have been observed with
self-report measures. Furthermore, these same measures appear to be sensitive to the symptoms
of both short-term and long-term training overload across a range of different sports. Existing
approaches to the monitoring of training state via self-report can be placed into various categories
based on the primary psychological parameter examined. Typically these have included: mood
disturbance, perceived stress scales or symptom checklists. Although some investigators have
simultaneously assessed a combination of these parameters, to date there is no measurement tool
that combines them in an efficient and athlete-friendly way. The general purpose of our research
(Main & Grove, 2009) was to take steps towards developing a brief instrument that overcomes this
limitation.
When developing the Multi-component Training Distress Scale three instruments provided the
initial item pool for responses by the athletes. These instruments included the 10-item version of
the Perceived Stress Scale (PSS; Cohen, Kamarck, & Mermelstein, 1983), the 24-item Brunel
Mood State Scale (BRUMS; Terry, Lane, & Fogarty, 2003), and the 19-item Training Stress Scale
(TSS; Grove & Shepherdson, 2005). The 10-item PSS assesses the extent to which general life
situations are considered stressful. The PSS was originally developed as a tool for examining the
role of non-specific appraised stress in the aetiology of disease and behavioural disorders.
Although the PSS has not been widely used in sport settings, there is evidence that healthy control
athletes and overtrained athletes differ significantly on this measure (Hynynen, Uusitalo, Knottinen,
& Rusko, 2006). The measure also possesses sound internal consistency, with alpha coefficients
exceeding 0.80.
The 24-item BRUMS, developed by Terry and colleagues (2003), was used to measure mood
disturbance. This instrument was chosen because: 1) it is relatively brief; 2) it possesses sound
psychometric properties within adult populations; and 3) empirical findings also suggest that the
BRUMS is sensitive to mood changes that occur in response to high training loads (Anglem,
Lucas, Rose, & Cotter, 2008; Terry et al., 2007). Finally, the TSS (Grove & Shepherdson, 2005)
was used to measure behavioural symptoms of training distress. This scale is based on the
symptom clusters identified by Fry and colleagues (1994) in their thorough study of overtrained
SAS military personnel. Research suggests that the TSS possesses good internal consistency, is
sensitive to changes in the training load, and correlates positively with other indicators of
overtraining (Main, Dawson, Grove, & Landers, 2007).
With a sample of 492 athletes from a range of sporting backgrounds, the factor structure of the
training distress item pool was assessed using a series of principal axis exploratory factor
analyses. Subscale internal consistencies were evaluated using Cronbach alphas, with item total
correlations also examined to identify poorly fitting items. Pearson product moment correlations
were used to investigate relationships among the factors. These procedures identified six
conceptually distinct factors with eigenvalues greater than 1.0. Factors one, two, and five were
representative of measures associated with psychological overload. Factor one consisted primarily
of items related to depression. Factor two was representative of the POMS vigour subscale
(reversed scores), while factor five may be considered a measure of perceived stress. Factors
three, four, and six reflected physical and behavioural complaints associated with training overload.
More specifically, these three factors consisted of items related to bodily symptoms, sleep
disturbances, and fatigue.

177
Thus the resultant 22-item scale has face validity as a framework for assessing training distress
symptoms. From a psychometric standpoint, it also exhibits a clean factor structure, good internal
consistency within the subscales, and theoretically relevant relationships with a similar but
qualitatively distinct construct (i.e. burnout risk). Because it includes six distinct symptom clusters
covering three broad response domains (mood, stress, behavioural physical symptoms), it should
be less susceptible to measurement error than approaches focusing on only one of those domains.
Although simultaneous assessment of multiple self-report domains has certainly been undertaken
before, we believe the framework outlined here is unique from a cost benefit perspective. Relevant
information about six symptom clusters is provided by just 22 items, which makes the framework
informative for practitioners as well as user-friendly for athletes. Combining an assessment of
these symptom clusters with carefully selected physical performance tests and/or biochemical
markers is a logical direction for future monitoring of athlete health and wellbeing.
References
Anglem, N., Lucas, S. J., Rose, E. A., & Cotter, J. D. (2008). Mood, illness and injury responses
and recovery with adventure racing. Wilderness and Environmental Medicine, 19, 30-38.Terry et
al., 2007
Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal
of Health and Social Behavior, 24, 385-396.
Fry, R. W., Grove, R. J., Morton, A. R., Zeroni, P. M., Gaudieri, S., & Keast, D. (1994).
Psychological and immunological correlates of acute overtraining. British Journal of Sports
Medicine, 28, 241-246.
Grove, J. R., & Shepherdson, A. J. (2005). Beyond the POMS: A multi-component assessment
model for training distress. In T. Morris, P. Terry, S. Gordon, S. Hanrahan, L. Ievleva, G. Kolt et al.
(Eds.), Promoting health and performance for life: Proceedings of the ISSP 11th World Congress
of Sport Psychology [CD-ROM]. Sydney, NSW: International Society of Sport
Psychology.Hynynen, Uusitalo, Knottinen, & Rusko, 2006
Hooper, S.L., & MacKinnon, L.T. (1995). Monitoring overtraining in athletes. Sports Medicine, 20,
321-327.
Main, L.C., Dawson, B., Grove, J.R., Landers, G., & Goodman, C. (2009). Monitoring training
distress: changes in perceived stress and inflammatory cytokines. Research in Sports Medicine,
17, 112-123.
Main, L.C., & Grove, J.R. (2009). A multi-component assessment model for monitoring training
distress among athletes. European Journal of Sport Science, 9, 191-198.
Meeusen, R., Duclos, M., Gleeson, M., Rietjens, G. J. W. M., Steinacker, J. M., & Urhausen, A.
(2006). Prevention, diagnosis and treatment of the overtraining syndrome. European Journal of
Sport Science, 6, 1-14.
Terry, P. C., Lane, A. M., & Fogarty, G. J. (2003). Construct validity of the Profile of Mood States-
Adolescents for use with adults. Psychology of Sport and Exercise, 4, 125-139.

178
Mental Toughness: Conceptualisation, Measurement, and Development
Daniel Gucciardi
School of Human Movement Studies, The University of Queensland
Correspondence: d.gucciardi@uq.edu.au
The pursuit of excellence in sport encompasses the continual development of four key facets of
performance, namely physical, technical, tactical, and mental skills. However, when physical,
technical and tactical skills are evenly matched, which commonly occurs in elite sport, performers
with greater levels of this thing called mental toughness seem to prevail more consistently than
those performers with lower levels. While athletes, coaches, sport administrators, and their media
widely acknowledge the importance of mental toughness as a key ingredient of optimal
performance, what exactly do these commentators mean when they refer to this thing called
mental toughness? The purpose of this presentation is to review and critically discuss recent
developments in research and theory on mental toughness in sport. Leveraging off recent and
ongoing mental toughness research in the cricket, the presentation will be structured around the
conceptualisation, measurement, and development of this desirable construct.
The turn of the new millennium saw the mental toughness construct emerge as an important
agenda item for several research teams around the world. Prior to this influx of research and
theory development much of what was known about mental toughness was based on anecdotal
reports. The result was a diverse selection of definitions and explanations of mental toughness that
largely included an assortment of positive psychological characteristics (e.g., resilience,
insensitivity to criticism) and mental skills (e.g., arousal regulation, visualisation). The conceptual
confusion created from this lack of consistency and understanding resulted in numerous positive
psychological characteristics being incorrectly labelled as mental toughness, particularly as they
were based on investigators’ opinions rather than empirical research (for reviews, see
Connaughton & Hanton, 2009; Gordon & Gucciardi, forthcoming; Gucciardi et al., 2009).
Although mental toughness research is still in its infancy, the knowledge base contributing to
current conceptualisations of mental toughness in sport now has a scent of scientific rigour owing
to the efforts of several groups of researchers. Initial attempts to empirically examine this construct
focused on qualitative methodologies in which key stakeholders’ (e.g., athletes, coaches, sport
psychologists) perceptions of mental toughness were generated and explored. Collectively, the
findings from this research provide support for a conceptualisation of mental toughness that: is
multifaceted; is made up of key components broadly classified as attitudes, cognitions, emotions,
values, and behaviours; consists of a core constellation of key characteristics that would not vary
significantly by sport (e.g., self-belief/confidence, personal values, attentional control, self-
motivation, positive and tough attitudes, enjoyment and thriving through pressure, resilience, and
sport intelligence); and encompasses dealing with and thriving through both negatively (e.g., injury,
de-selection) and positively construed (e.g., personal success, team winning streak) situations or
critical incidents (Gucciardi et al., 2009). The latter component provides an important piece of
evidence on the distinction between mental toughness and resilience. Whereas both resilience and
mental toughness are pertinent constructs when things are not going well (e.g., injury, performance
slump, de-selection), only mental toughness appears to facilitate optimal functioning when things
are going well (e.g., sustaining a winning streak, maintaining career best form, regular selection for
a national team).
Prior to the aforementioned advancements in mental toughness research and theory, the lack of a
clear conceptualisation and operational definition of mental toughness resulted in limited attempts
to develop questionnaires that profile and assess mental toughness. Despite the initial lack of
research attention to mental toughness measurement, a number of sport-general (i.e., can be used

179
across different sports) and sport-specific questionnaires (i.e., designed specifically for use with a
particular sport) have emerged in recent years. Concerns with the currently available sport-general
measures include a lack of rigorous psychometric testing and grounding in relevant research and
theory. Although sport-specific measures appear to have somewhat addressed these concerns,
they are clearly limited in that they are developed for a particular context and, therefore, should
only be used with this specific sample of athletes. The Cricket Mental Toughness Inventory
(Gucciardi & Gordon, 2009) is briefly discussed as a measure for use in cricket contexts.
The question remains, if mental toughness is so important for optimal performance, can we
develop it? If so, how do we go about doing this? Recent research indicates that there appears to
be an inherited or genetic component to mental toughness (Horsburgh et al., 2009), which likely
represents ‘raw’ or natural abilities that have not undergone any formal training. Nevertheless, just
because an individual may have a genetic predisposition to be mentally tough does not mean that
mental toughness will necessarily evolve. Rather, there appears to be a general consensus that it
is the experiences we are exposed to that make the most significant contribution to the
development of mental toughness. Drawing on recent mental toughness development research,
methods by which key stakeholders can formally nurture its development are discussed.
As architects of the sport context, coaches play an important role in creating environments that
expose athletes to important ‘mental toughness’ developmental experiences. Helping cricketers
develop an awareness of critical incidents (i.e., situations demanding higher degrees of mental
toughness), adopting a strengths-based coaching style, (i.e., encourage athletes to leverage off
their signature strengths in making weaknesses irrelevant), and promoting autonomy-supportive
motivational climates (e.g., personally meaningful rationales for behaviour, minimising external
controls, opportunities for participation and choice) are important processes that coaches can
engage in to facilitate the development of mental toughness.
Sport psychologists, on the other hand, become important mediators between the academic and
sport communities to ensure that adopted strategies and processes are evidenced-based. In
addition to psychological skills training (i.e., techniques and strategies to assess, monitor, and
adjust thoughts and feelings for optimal performance) – which should not be equated with ‘mental
toughness development’ – sport psychologists can educate coaches, players, parents, and cricket
organisations about their roles in developing mental toughness. The importance of interpersonal
relationships, social support, and exposure to psychological principles from an early age are
important considerations.
Recognising that mental toughness research remains a ‘work in progress’, attendees will gain an
understanding of current conceptualisations of mental toughness, its measurement, and
development in cricket. Examples of mental toughness measurement and development within elite
and youth cricket contexts will be provided to facilitate attendees’ application of the information
presented.
Acknowledgements: Gucciardi is supported by a University of Queensland Postdoctoral Research
Fellowship. Special appreciation is extended to Prof. Sandy Gordon for his ongoing mentorship
and intellectual contributions to our mental toughness research program. Our research would not
have been possible without the support of staff at the Centre of Excellence (Dr.’s Marc Portus and
Michael Lloyd), State Cricket Associations, Cricket Coaches Australia and the cricket community.
References
Connaughton, D., & Hanton, S. (2009). Mental toughness in sport: Conceptual and practical
issues. In S. D. Mellalieu & S. Hanton (Eds.), Advances in applied sport psychology: A review (pp.
317-346). London: Routledge.

180
Gordon, S., & Gucciardi, D.F. (forthcoming). Mental toughness. In J. Adams (Ed.), Sport
psychology: Theory and practice. Pearson.
Gucciardi, D.F., & Gordon, S. (2009). Development and preliminary validation of the Cricket Mental
Toughness Inventory. Journal of Sports Sciences, 27, 1293-1310.
Gucciardi, D.F., Gordon, S., & Dimmock, J.A. (2009). Advancing mental toughness research and
theory using personal construct psychology. International Review of Sport & Exercise Psychology,
2, 54-72.
Horsburgh, V.A., Schermer, J.A., Veselka, L., & Vernon, P.A. (2009). A behavioural genetic study
of mental toughness and personality. Personality and Individual Differences, 46, 100-105.

181
The Battle Zone: Constraint-Led Coaching in Action
Ian Renshaw1, Greg Chappell2, David Fitzgerald2, John Davison2 and Brian McFadyen2
1 School of Human Movement Studies, Queensland University of Technology, Brisbane
2 Player Development Unit, Cricket Australia Centre of Excellence, Brisbane
Correspondence: i.renshaw@qut.edu.au
The increased professionalism of cricket means that coaches are constantly looking for ways to
improve their practice. Formal cricket practice has traditionally been centred on the ‘net session’
with a strong focus on technical skill development for both batters and bowlers. In the past, this
sole focus of structured training was not an issue as many expert and non-expert cricketers
developed all other aspects of their games by playing backyard cricket. Backyard games have
been viewed as the true academy of Australian cricket as they allow players to devote hours of
holistic practice where they developed unique skills, mental skills and the physical conditioning that
underpinned their later expertise (Cannane, 2009; Renshaw & Chappell, 2010). Today, there are
less opportunities for young players to practice informally and coaches are concerned that young
players lack ‘game sense’ and there is great interest in finding new ways to ‘coach’ up and coming
young players. One new methodology currently being adopted across many development and high
performance programmes is the constraint-led approach (Renshaw et al., 2010a). In this article we
discuss why this approach has been adopted in the AIS cricket program at the Cricket Australia
Centre of Excellence and give an example of how the Battle Zone concept is based on the
approach.
The constraints-led approach (see Renshaw et al., 2010a; Renshaw & Chappell, 2010 and
Renshaw & Holder, 2010 in these proceedings) to coaching cricket embraces the concepts and
ideas embedded in Teaching Games for Understanding or Game Sense approach to skill learning
(Bunker & Thorpe, 1982). Bunker and Thorpe were teacher educators and developed their ideas in
response to observation of traditional technique based school lessons that were focussed on
developing technique, were boring and typified by the constant request of “when can we play a
game”. The teacher centred, controlling lessons were not exploiting intrinsic motivation and were
failing the less and most able players. As a result players had no understanding of how to play
games and were more about ‘control’ of children rather than learning. Additionally, the approach
led to a failure of skills to transfer to the game and disobeyed some basic skill acquisition
principles.
Many of these criticisms of traditional approaches to teaching games apply equally to cricket that
has relied on net practice to prepare players for matches. Net practice has its main focus on the
development of technique and players lack opportunities to practice situations that are
representative of match scenarios. Players are always encouraged to use their ‘imagination’ and
guess where a ball had been hit or how many runs were scored. However, as any cricketer knows,
in the nets, all bowlers have 15 fielders who stop every shot, while for the batter any good shot
goes to the boundary! Nets are failing cricketers in helping them develop decision-making skills
and it is no wonder that coaches are critical of the ‘game sense’ of young players. Sadly, it seems
that many coaches fail to see any connection between the sole focus on nets and the poor
decision-making ability.
However, as mentioned in the introduction many coaches have explored different ways of
practicing but struggle for new ideas due to lack of facilities. At the Centre of Excellence, access to
facilities is not an issue and we were keen to develop a game centred approach to the programme.
We believe that playing in games is crucial to the holistic development of players because like the
backyard cricket environment it “includes the critical mix of physical and mental development under
simulated match conditions in real-time environments” (Chappell, 2004). An initial candidate for

182
consideration was centre wicket practice. However, previous experiences of coaches suggested
that this approach was limited in what could be achieve as it lacked intensity, with fielders standing
around not engaged in the bat-ball ‘competition’. After some time brainstorming about how we
could manipulate constraints to re-create match intensity we came up with the idea of putting a low
‘net’ on the 30 m circle. We also, wanted the players to be constantly challenged and engaged
emotionally, mentally and physically in the practice tasks. We therefore christened the practice
area the “Battle Zone”. The layout of the Battle Zone can be seen in the figure below. It should be
clear that the Battle Zone is as much a concept as it is a physical space.
Figure 1: The Battle Zone.
We believe that the Battle Zone is and will continue to be a key part of our skill development
programme and that it will accelerate the skills of the AIS cricket scholars. There are a number of
key reasons why this is the case. The primary factor is that it puts the player at the centre of the
learning process and requires each player to take responsibility for their own learning. It provides
realistic practice that enables transfer of skill to the real game creating players who can solve
problems via exploration of their own constraints. This is important because technique and
decision-making cannot be developed separately because correct movements do not emerge if the
perceptual environment is not reflective of those in performance (Renshaw et al., 2007). For
example, to learn when to move down the wicket to hit a spinner on the full, a batter needs to learn
to pick the bowlers deliveries and recognise the appropriate flight that affords the opportunity to get
to the ball on the full. Similarly, for learning to place and pace shots, batters need to be able to see
the consequences of their shots. For bowlers, learning to bowl to set fields in specific game
scenarios enables them to bowl to plans, learn to work out batters strengths and weaknesses and
where precisely to place fielders.
Playing constraint-led games gives the player and coach opportunities to identify current strengths
and weaknesses in terms of technical, tactical mental or physical factors. Coaches can then design
games by manipulating constraints to facilitate the opportunity for the player to explore new ways
of overcoming the weaknesses. For example, if a spin bowler needs to improve his ability to bowl
on turning pitches, coaches can ‘doctor’ the playing surface to create turn and bounce. If the player
is a poor decision maker when fatigued conditions can be created that require the player to
perform in this state (see Renshaw & Chappell, 2010 for an example how we have achieved this in
a recent practice session).

183
The Battle Zone is an extremely flexible resource. By using manikins to represent fielders we give
batters and bowlers the opportunity to practice mutually beneficial game scenarios via games that
can be simply 1 on 1 or up to 8 v 8. When ‘live’ fielders are part of the game, the batter must hit the
ball along the ground and it is still live if it hits the net. A standard requirement from fielders is that
they must always throw to the bowler or keeper and this results in all fielders having to concentrate
and be part of every delivery, even if the ball is not hit to them. Coaches can be creative by
manipulating both task and environmental constraint. For example, more runs can be awarded for
hitting or bowling balls into specific zones, different makes of balls can be used, or balls with
specific characteristics such as different colours or seams. Game scenarios can be developed, or
different equipment used (e.g., bat width manipulations).
One final point is that the Battle Zone is based on adopting all the principles of the constraint-led
approach which form the basis of Non-Linear Pedagogy (Renshaw, et al., 2010b). This approach is
inter-disciplinary and captures the need for all aspects of performance to be considered as
potential factors that can interact to shape behaviours. It is worth noting that the Centre of
Excellence has used this inter-disciplinary approach for a number of years featuring weekly
meetings between coaches, physiotherapists, nutritionists, psychologists, medicos and strength
and conditioning staff to plan, review and monitor the progress of each individual scholar.
Assessment of performance in the Battle Zone provides support staff with highly functional and
relevant performance data on which to plan the whole of the player’s skill development
programme, which will of course contain input as and when required from all of the support staff.
In summary, the Battle Zone shifts control of learning and skill development back to the players,
allowing them autonomy and enhances their feelings of competence as it enables them to gain
vital experiences that are representative of real match conditions. The Battle Zone concept allows
coaches to tailor game design to the needs of individuals and recognises the importance of
creating exciting challenges that players love. It captures cricketer’s love of competition and is
much more fun for both players and coaches. Finally, and most importantly, in our opinion it works.
References
Bunker, D. & Thorpe, R. Bunker, D., & Thorpe, R. (1982). A model for the teaching of games in the
secondary schools. The Bulletin of Physical Education, 5-8.
Chappell, G. (2004). Some thoughts to end a tremendous year of learning. Thanks to all who took
part. In N. <newsletter@chappellway.com.au> (Ed.).
Renshaw, I., Davids, K., & Savelsbergh, G. (Eds.). (2010a). Motor Learning in Practice: A
constraints-led approach. London: Routledge.
Renshaw, I., & Davids, K. (2006). A comparison of locomotor pointing strategies in cricket bowling
and long jumping. International Journal of Sports Psychology, 37(1), 1-20.
Renshaw, I., Oldham, A. R., Golds, T., & Davids, K. (2007). Changing ecological constraints of
practice alters coordination of dynamic interceptive actions. European Journal of Sport Sciences,
7(3), 157-167.
Renshaw, I., Chow, Ji-Yi , Davids, K. & Hammond, J. (2010b) 'A constraints-led perspective to
understanding skill acquisition and game play: a basis for integration of motor learning theory and
physical education praxis?', Physical Education & Sport Pedagogy, 15: 2, 117-137.
Renshaw, I., & Chappell, G. S. (2010). A Constraints-led Approach to Talent Development in
Cricket. In L. Kidman & B. Lombardo (Eds.), Athlete-Centred Coaching: Developing Decision
Makers (2nd ed., pp. 151-173). Worcester: IPC Print Resources.

184
Renshaw, I., & Holder, D. (2010). The Nurdle to leg and other ways of winning cricket matches. In
I. Renshaw, K. Davids & G. Savelsbergh (Eds.), Motor Learning in Practice: A constraints- led
approach. London: Routledge.

Subject Index
12th man, 47
accuracy, 19, 25, 28, 36, 71, 72, 73, 74, 81, 105, 106,
107, 121, 122, 123, 124, 130, 131, 132, 144, 162,
165
administrators, 178
Administrators, 29
aetiology, 7, 176
ankle, 59, 60, 61, 90, 137, 153, 154, 155
anthropometry, 49, 117
anti-doping, 12
athlete development, 87
back injury, 9, 101, 164
back pain, 9, 10, 11, 48, 50, 138, 139, 146, 159
ball speed, 18, 105, 107, 117, 118, 119, 121, 122, 123,
124
batting, 8, 11, 15, 32, 33, 34, 35, 36, 46, 63, 65, 66, 82,
83, 84, 98, 99, 109, 114, 125, 126, 127, 128, 129,
142, 144, 163
Battle Zone, 181, 182, 183
biomechanics, 12, 17, 20, 59, 61, 62, 63, 67, 77, 87,
101, 119, 124, 153, 161, 168
Biomechanics, 9, 10, 11, 17, 20, 59, 61, 62, 63, 70, 88,
104, 114, 117, 120, 121, 124, 125, 130, 140, 141,
143, 144, 146, 147, 149, 154, 155, 165, 168
brain, 99
Burnout, 52, 53, 54, 55
catching, 115
coaches, 1, 6, 7, 9, 14, 15, 16, 19, 21, 23, 30, 32, 34,
35, 36, 41, 42, 53, 54, 56, 57, 70, 76, 77, 86, 94, 95,
97, 100, 104, 109, 112, 113, 114, 115, 116, 125,
130, 132, 134, 136, 142, 148, 151, 163, 164, 165,
169, 173, 175, 176, 178, 179, 181, 182, 183
coaching, 7, 9, 10, 14, 15, 33, 35, 36, 66, 77, 81, 96,
111, 114, 116, 121, 132, 140, 148, 171, 181
Coaching, ii, 35, 37, 43, 88, 89, 133, 152, 174, 181, 183
decision making, 36, 82
ecological validity, 105, 142
elbow, 9, 17, 18, 19, 21, 147, 163, 165, 166, 167, 168
emotional labour, 51
engineering, 162, 164, 171
fast bowling, 9, 10, 47, 59, 69, 70, 86, 87, 88, 99, 105,
106, 107, 108, 114, 115, 116, 117, 120, 121, 132,
146, 159
fielding, 8, 18, 19, 22, 46, 98, 105, 107
fitness assessment, 90
flexibility, 80
foot, 8, 18, 21, 33, 59, 60, 61, 63, 64, 65, 67, 68, 69, 71,
87, 110, 116, 117, 119, 121, 137, 144, 147, 153,
154, 155, 159, 162, 168
footwear, 59, 61
goal setting, 94, 95, 96, 97
GPS, 74, 75, 76, 82, 83, 84, 112, 161, 163, 170
hamstring, 46, 137, 138, 139
hip, 67, 91, 92, 93, 137, 138, 139, 148, 153, 154, 155,
159
hips, 18
hydration, 25, 26, 27, 107, 134, 135
illegal bowling actions, 13, 89
injury rate, 101, 104
injury rates, 22, 46
injury surveillance, 12, 13, 47, 49, 144
Injury surveillance, 22
interviews, 114
Kinanthropometry, 149
kinematic, 8, 9, 34, 60, 61, 62, 63, 67, 68, 69, 88, 99,
117, 118, 120, 121, 122, 124, 143, 145, 146, 147,
148, 153, 154, 155, 165
kinematic model, 63, 120, 124, 143, 146, 147, 148, 154
kinematics, 9, 11, 21, 59, 60, 63, 66, 67, 68, 69, 70,
121, 141, 147, 148, 153, 155, 156, 159
kinetics, 21
Kinetics, 15, 20, 55, 81, 88, 89, 118, 123, 136
knee, 18, 19, 59, 60, 61, 62, 68, 69, 118, 119, 122, 123,
137, 146, 153, 154, 155
knees, 18
learning, 14, 15, 16, 35, 36, 37, 77, 91, 109, 110, 111,
122, 181, 182, 183
lumbar spine injury, 48, 49, 69
management, 31, 47, 51, 52, 54, 57, 102, 134, 156, 159
match context, 125, 132
mechanics, 17, 19, 20, 22, 35, 87, 137, 138, 140, 148
Medicine, ii, 7, 9, 10, 11, 17, 25, 28, 34, 46, 47, 48, 50,
54, 59, 62, 66, 70, 88, 89, 90, 92, 93, 94, 99, 104,
105, 108, 111, 112, 114, 117, 121, 124, 125, 129,
130, 133, 136, 137, 140, 144, 146, 147, 149, 150,
153, 155, 156, 165, 172
mental skills, 7, 35, 36, 52, 80, 178, 181
motor, 9, 15, 25, 32, 35, 37, 71, 77, 78, 79, 80, 90, 91,
99, 109, 110, 111, 119, 127, 159, 183
motor control, 9, 77, 119, 159
movement demands, 74, 76, 83, 84
multidisciplinary, 77, 80, 127
multivariate, 77, 78, 79, 80, 127
national standards, 137
nutrition, 27, 29, 30, 31, 134, 135, 175
overuse, 19, 46, 57, 101, 103, 116, 134
perception, 35, 63, 88, 99, 115, 134
perceptions, 31, 94, 95, 113, 178
periodisation, 56, 175
Periodisation, 42, 56
physiology, 77
physiotherapy, 137, 138, 153, 155
Physiotherapy, 21, 159
Physiotherapy Screening, 149
planning, 14
practice, 8, 10, 11, 14, 15, 16, 21, 33, 35, 36, 37, 52,
101, 109, 110, 111, 114, 125, 129, 142, 153, 159,
161, 181, 182, 183
psychological, 7, 32, 41, 51, 52, 77, 78, 81, 94, 95, 97,
110, 114, 115, 116, 150, 151, 172, 173, 178, 179
psychologists, 94, 178, 179, 183
Psychology, 7, 10, 11, 34, 37, 45, 54, 55, 66, 97, 111,
116, 129, 150, 152, 172, 174, 183
quadratus lumborum, 9, 11, 48, 49
RANGE OF MOTION, 23, 60, 61, 78, 90, 91, 92, 93,
137, 138, 139, 147
reflection, 103, 142, 154
research, 7, 9, 10, 14, 15, 17, 19, 20, 35, 44, 45, 48, 49,
51, 52, 70, 75, 77, 79, 80, 82, 83, 84, 86, 87, 90, 94,
96, 101, 107, 109, 112, 113, 114, 117, 119, 121,
122, 123, 124, 127, 130, 140, 142, 145, 146, 147,
148, 152, 153, 159, 162, 174, 176, 178, 179, 180

Research, 2, 7, 8, 15, 34, 40, 44, 48, 52, 59, 80, 81, 84,
86, 93, 97, 104, 110, 120, 142, 147, 150, 153, 160,
161, 165, 172, 176, 177, 179
resistance training, 80, 86, 87, 90, 91
rule changes, 36, 47
selection, 42, 51, 52, 53, 54, 57, 98, 116, 152, 169,
174, 178
selectors, 79
sensors, 89, 161, 162, 163, 164, 165, 166, 167, 168,
169, 170, 171
shoulder, 9, 18, 19, 20, 21, 67, 68, 69, 71, 72, 73, 104,
118, 123, 144, 145, 147, 148, 159, 165, 166
skill, 8, 11, 14, 15, 22, 25, 27, 33, 34, 35, 36, 56, 57, 71,
77, 84, 94, 97, 107, 109, 110, 111, 112, 115, 116,
117, 119, 122, 125, 126, 127, 129, 130, 131, 132,
133, 142, 163, 181, 182, 183
skill acquisition, 14, 15, 36, 181, 183
skill development, 14, 35, 115, 125, 129, 130, 163, 181,
182, 183
skills, 7, 8, 14, 32, 33, 34, 35, 36, 54, 79, 80, 84, 90, 94,
97, 98, 105, 109, 110, 111, 115, 116, 121, 124, 125,
126, 127, 128, 129, 130, 131, 132, 133, 163, 178,
181, 182
smart balls, 169
spin bowling, 9, 140, 142, 147, 148
spine, 48, 49, 67, 68, 69, 70, 101, 102, 103, 138, 144,
159, 163
Sport Science, 7, 16, 25, 34, 46, 48, 51, 59, 77, 82, 90,
94, 99, 105, 112, 114, 117, 120, 121, 124, 125, 130,
137, 140, 144, 147, 149, 150, 153, 161, 165, 172
sport scientists, 35, 37, 41, 114, 164
Strength & Conditioning, 56, 86, 87, 93, 97, 120
strength and conditioning, 36, 84, 113, 138, 183
Strength Training, 86, 89, 93
stress, 3, 42, 48, 49, 56, 57, 67, 69, 92, 93, 101, 102,
103, 106, 107, 134, 136, 144, 158, 159, 175, 176,
177
Stress, 52, 102, 159, 176
stresses, 9, 21
Supplementation, 29
talent, 10, 44, 45, 47, 54, 80, 109, 110, 114, 115, 116,
127, 129, 132
Talent Development, 37, 114, 129, 183
talent identification, 80, 110, 115, 116, 132
task representation, 133
task representative, 132
Technique variability, 121, 123
Technology, 35, 55, 90, 105, 114, 117, 121, 153, 161,
164, 168, 170, 171, 181
throwing, 17, 18, 19, 20, 69, 71, 72, 73, 87, 99, 162,
165, 170
Throwing, 21
throwing mechanics, 17, 19
training, 7, 20, 25, 26, 27, 28, 29, 30, 32, 34, 41, 42, 43,
52, 54, 56, 57, 74, 75, 77, 82, 84, 86, 87, 88, 90, 91,
92, 93, 94, 95, 96, 97, 99, 102, 103, 104, 105, 107,
110, 112, 113, 115, 116, 127, 134, 135, 136, 138,
159, 161, 162, 163, 164, 169, 171, 181
trunk, 10, 11, 18, 19, 21, 50, 70, 101, 119, 120, 122,
153, 154, 155, 163, 168
VARIABILITY, 23, 26, 36, 42, 77, 121, 122, 123, 124,
133
Vision, 32, 34, 98
WADA, 12, 29
workload, 10, 41, 46, 51, 52, 53, 54, 57, 82, 90, 101,
102, 103, 104, 112, 116, 136, 146
wrist, 140, 147, 148