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To ease interpretation for coaches and athletes, the TSA and each test's z-score, for that matter, can then be ranked, and a "traffic light" system can be used to highlight how each athlete's fitness compares with their teammates. TSA, total score of athleticism.
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OFTENTIMES, THE VARIOUS COACHING STAFF, SPORT SCI- ENCE, AND MEDICAL PRACTI- TIONERS OF A SPORTS CLUB REQUIRE A SINGLE, HOLISTIC INDICATION OF AN ATHLETE’S ATHLETICISM. CURRENTLY, THERE IS NO CONSENSUS ON HOW THIS IS BEST DEFINED, AND THUS, A TOTAL SCORE OF ATHLETICISM (TSA) MAY PROVIDE ONE SUCH METHOD. THE TSA IS DERIVED FROM THE AVERAGE OF Z- SCO...
Contexts in source publication
Context 1
... the TSA is calculated by averaging all z-scores ( Figure 7). For ease of interpretation for coaches and athletes, the TSA and each test's zscore can then be ranked, and a "traffic light" system can be used (Figure 8) to highlight how each athlete's fitness compares with their teammates; an example of how this can be presented (using the "VLOOKUP" function) is shown in Figure 9. ...
Context 2
... the TSA is calculated by averaging all z-scores ( Figure 7). For ease of interpretation for coaches and athletes, the TSA and each test's zscore can then be ranked, and a "traffic light" system can be used (Figure 8) to highlight how each athlete's fitness compares with their teammates; an example of how this can be presented (using the "VLOOKUP" function) is shown in Figure 9. ...
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Objectives
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Citations
... T-scores were calculated to establish normative benchmarks for individual profiling, as described in previous publications, to facilitate practical use and interpretation of S-MAS values. 31,45 Sample sizes of 50 to 85 have been suggested as the minimum required to achieve stable means and standard deviations for establishing normative data. 39 z scores were initially calculated using the following formula: z = (S-MAS value -group mean)/group SD. z scores were converted to T-scores using the formula T = (z x 10) 1 50. ...
Background
Qualitative movement screening tools provide a practical method of assessing mechanical patterns associated with potential injury development. Biomechanics play a role in hamstring strain injury and are recommended as a consideration within injury screening and rehabilitation programs. However, no methods are available for the in-field assessment of sprint running mechanics associated with hamstring strain injuries.
Purpose
To investigate the intra- and interrater reliability of a novel screening tool assessing in-field sprint running mechanics titled the Sprint Mechanics Assessment Score (S-MAS) and present normative S-MAS data to facilitate the interpretation of performance standards for future assessment uses.
Study Design
Cohort study (diagnosis); Level of evidence, 3.
Methods
Maximal sprint running trials (35 m) were recorded from 136 elite soccer players using a slow-motion camera. All videos were scored using the S-MAS by a single assessor. Videos from 36 players (18 men and 18 women) were rated by 2 independent assessors blinded to each other's results to establish interrater reliability. One assessor scored all videos in a randomized order 1 week later to establish intrarater reliability. Intraclass correlation coefficients (ICCs) based on single measures using a 2-way mixed-effects model, with absolute agreement with 95% CI and kappa coefficients with percentage agreements, were used to assess the reliability of the overall score and individual score items, respectively. T-scores were calculated from the means and standard deviations of the male and female groups to present normative data values. The Mann-Whitney U test and the Wilcoxon signed-rank test were used to assess between-sex differences and between-limb differences, respectively.
Results
The S-MAS showed good intrarater (ICC, 0.828 [95% CI, 0.688-0.908]) and interrater (ICC, 0.799 [95% CI, 0.642-0.892]) reliability, with a standard error of measurement of 1 point. Kappa coefficients for individual score items demonstrated moderate to substantial intra- and interrater agreement for most parameters, with percentage agreements ranging from 75% to 88.8% for intrarater and 66.6% to 88.8% for interrater reliability. No significant sex differences were observed for overall scores, with mean values of 4.2 and 3.8 for men and women, respectively ( P = .27).
Conclusion
The S-MAS is a new tool developed for assessing sprint running mechanics associated with lower limb injuries in male and female soccer players. The reliable and easy-to-use nature of the S-MAS means that this method can be integrated into practice, potentially aiding future injury screening and research looking to identify athletes who may demonstrate mechanical patterns potentially associated with hamstring strain injuries.
... Raw scores offer the advantage of immediate feedback and direct comparison with an individual's performance over time. On the other hand, standardized scores express the test results as a standard deviation from the mean, which is valuable to show where the player ranks relative to the group or comparing test performance with different outcome variables (McGuigan et al., 2013;Turner et al., 2019). ...
... A large percentage (68%) of practitioners compared results with normative data or established benchmarks, which can play a key role in setting performance goals and talent identification (McGuigan et al., 2013). Finally, a positionspecific comparison (52%) is performed by practitioners, as different positions have varying physical demands, thus different expected physical profiles (Walker and Hawkins, 2018;Turner et al., 2019). ...
This study provides insight into the current fitness testing practices in elite male soccer. One hundred and two practitioners from professional soccer leagues across 24 countries completed an online survey comprising 29 questions, with five sections: a) background information, b) testing selection, c) testing implementation, d) data analysis, and e) data reporting. Frequency analysis was used to evaluate the responses to fixed response questions and thematic analysis was used for open-ended questions to generate clear and distinct themes. Strength (85%) and aerobic capacity (82%) represent the most frequently assessed physical qualities. Scientific literature (80%) is the most influential factor in testing selection and practitioners conduct fitness testing less frequently than their perceived ideal frequency per season (3.6 ± 2 vs. 4.5 ± 2). Time and competitive schedule were the greatest barriers to fitness testing administration. Practitioners mostly used a ‘hybrid’ approach (45%) to fitness testing, blending ‘traditional’ (i.e., a day dedicated to testing) and ‘integrated’ (i.e., testing within regular training sessions) methods. Microsoft Excel is the most used software for data analysis (95%) and visualization (79%). An equal use of the combination of best and mean scores of multiple trials (44%) and the best score (42%) was reported. Comparing a player’s test performance with previous scores (89%) was the most common method for interpreting test results. However, only 38% considered measurement error. Digital displays and verbal feedback are the most common data reporting methods, with different data reporting processes for coaches and players. Practitioners can use data and findings from this study to inform their current testing practices and researchers to further identify areas for investigation, with the overarching aim of developing the field of fitness testing in elite male soccer.
... This approach has already been adopted in fitness testing, using standardized scores from a series of tests to create a single Total Score of Athleticism (TSA) value for each individual player. 45 By averaging standardized scores (ie, Z scores) and applying the TSA instead of only interlimb symmetry in different tests, this allows clinicians and coaches to examine contextualized data of individual athletes relative to their teammates and thus set benchmarks for RTS readiness that are realistic to the demands that athletes will be exposed to. Oleksy et al 32 showed reduced composite scores (using Functional Movement Screen, Y-Balance Test, and tuck jump assessment) in Polish players who underwent ACL reconstruction in comparison to healthy controls. ...
... Finally, the TSA score was calculated by averaging all Z scores. 45 The TSA is a measure used across sports and performance settings, 36,54 including athletes returning after ACL reconstruction. 32 The use of Z scores allows clinicians to compare data across similar athletes, who share the same training approach, demands, and constraints. ...
... This permits the contextualization of a single player's data in relation to his teammates and can be used to set benchmarks and rehabilitation goals that are realistic during rehabilitation for the restoration of physical performance to a level no less than that of uninjured players and are reflective of RTS demands. 45 Regression analysis showed that the TSA score accounted for 20% of the variability observed in the identification of a player's status. Although the optimal testing procedure to determine sport readiness is currently unclear, 3 our results confirm the utility of an overall measure of contextualized physical preparedness before RTS to differentiate between injured and uninjured players. ...
Background: There is no consensus on the optimal testing procedure to determine return-to-sport (RTS) readiness after anterior cruciate ligament (ACL) reconstruction. Current approaches use limb symmetry across a range of tests, but this does not consider a patient’s level of athleticism or benchmarks relative to his or her noninjured counterparts.
Purpose: To examine the utility of the Total Score of Athleticism (TSA), a composite scale including strength, power, and reactive strength assessments, to aid RTS decision-making.
Study Design: Cross-sectional study; Level of evidence, 3.
Methods: A total of 95 professional soccer players (60 who underwent ACL reconstruction [mean age, 25.1 6 12.6 years] and 35 who were uninjured [mean age, 23.8 6 2.8 years]) completed a battery of tests including isokinetic knee extension and flexion torque, bilateral and unilateral countermovement jump height, relative peak power, and reactive strength index–modified. The TSA score (derived from Z scores) was calculated, and we (1) examined differences between the ACL-reconstructed and uninjured groups at the time of RTS, (2) assessed the predictive ability of the TSA to identify the player’s status (ACL reconstruction vs uninjured control), and (3) included a case series to discuss the characteristics of players who sustained a subsequent injury within 4 months after RTS.
Results: A large difference between the ACL-reconstructed and uninjured groups in the TSA score (d = 0.84; P < .0001) was evident. For every additional increase of 1 unit in the TSA score, the odds of belonging to the ACL-reconstructed group decreased by 74% (95% CI, 0.19-0.56). By visual inspection, the frequency of reinjured players was higher in the low (4/7) TSA tertile compared with the medium (2/7) and high (1/7) TSA tertiles.
Conclusion: Preliminary evidence indicates that the TSA may be a useful RTS readiness tool, as the composite score derived from strength and power measures was different in soccer players at the time of RTS after ACL reconstruction compared with healthy matched controls. There was also a higher frequency of low TSA scores in players who sustained a second injury after RTS. Therefore, it is recommended to routinely administer RTS tests encompassing strength, power, and reactive strength qualities each season across the largest possible number of players (ideally teammates).
... Alternatively, a 0.2 m$ second 21 mean concentric barbell velocity difference in the back squat may sound small, but in reality, it suggests a difference of ;15-20% 1 RM (22). A range of statistical methods are available to help illustrate these differences (e.g., Z-scores and T-scores (46,84); refer to Figures 11 and 12) and demonstrating the magnitude of difference, irrespective of the units of measurement that can effectively illustrate the practical significance of the data being presented. ...
... (A and B) Present athlete testing data through Z-scores and T-scores. In (B) (T-scores), the yellow circle and number represent the athlete's score out of 100, while the green and red values represent the highest and lowest scores from the cohort(84). ...
Understanding the physical qualities of athletes can lead to improved training prescription, monitoring, and ranking. Consequently, testing and profiling athletes is an important aspect of strength and conditioning. However, results can often be difficult to interpret because of the wide range of available tests and outcome variables, the diverse forms of technology used, and the varying levels of standardization implemented. Furthermore, physical qualities can easily be misrepresented without careful consideration if fundamental scientific principles are not followed. This review discusses how to develop impactful testing batteries so that practitioners can maximize their understanding of athletic development while helping to monitor changes in performance to better individualize and support training. It also provides recommendations on the selection of tests and their outcome measures; considerations for the proper interpretation, setup, and standardization of testing protocols; methods to maximize testing information; and techniques to enhance visualization and interpretation.
... The morphological features linked to specific body regions (upper and lower limb) may be preferred over whole-body measures to interpret male players' physical potential in vertical jump and sprint performance (Bongiovanni et al., 2021). The idea to manage the training process by monitoring the multidimensional performance can work to the advantage of practitioners (Turner et al., 2019), especially if information based on an individual's anthropometric measures (e.g., morphological data of upper and lower limbs) can be associated (Rossi et al., 2022). ...
This study aimed to determine whether vertical jump performance differs among youth players with different performance levels. A total of 84 youth male soccer players from the U19 category were recruited. The players were divided into the elite (EG) and sub-elite (SEG) groups based on their performance level. The vertical jump (VJ) performance during countermovement jump with free arms (CMJFA) and drop jump (DJ) were measured using two force platforms Kistler B8611A at a sampling rate of 1000 Hz (KISTLER Instrumente AG, Switzerland). The following outcome variables were calculated in all participants: vertical jump height (VJH), vertical ground reaction force (VGRF), and force impulse (FI) in absolute and relative values. Data analysis showed no significant differences (p>0.05) with trivial to small effects in VJH for both jump types (CMJFA and DJ) between EG and SEG. FI relative provides a good indication of VJH in comparison with the other kinematic variables selected in this study. The leg muscle mass (LMM) ratio normalised to body mass provides a better association with VJH than the other selected morphological variables in this study, although a small effect size was observed. VJH does not appear to be a distinguishing feature among soccer players in the first two performance levels in the U19 category. Therefore, the results may not be sensitive enough to differentiate the performance levels, especially when the difference is only in one performance level. Future research should consider using other parameters, such as the eccentric rate of force development or reactive strength index, as recommended in a previous study.
... Due to the fact that the coaching staff require readable and easy-to-use indications of an athlete's readiness to play [26], the previously reported single-score injury risk index called the Composite Score of Readiness (CSR) may provide such information [27]. Thus, there is a need for unification of test interpretation and determining which tests are the most indicative of deficits after ACL reconstruction. ...
In recent years, many studies on the safe return to sport (RTS) have been published, but there are still no clear and validated guidelines. After ACL reconstruction between limbs, asymmetry of muscle strength affects knee mechanics during walking and running, and asymmetrical joint kinematics and kinetics are considered as a strong risk factor of musculoskeletal injury. Therefore, proper diagnosis of any motor deficits remaining after ACL reconstruction seems particularly important. The aim of this study was to analyze how many tests should be included in the RTS test battery and which of them are most indicative for functional deficits related to anterior cruciate ligament (ACL) reconstruction. Sixty-five male football players (age 18–25 years) were divided into three groups: ACL group—after ACL rupture and reconstruction, mild injury group—post mild lower limb injuries, and the control group—without injuries. They performed five tests: Functional Movement Screen, Tuck Jump Assessment, Y-balance Test, Hop Test for Distance, and Isokinetic Test. The Composite Score of Readiness (CSR) index was calculated and expressed as the sum of z-scores. The multiple regression model for all tests was calculated, and then redundant variables were excluded. We observed that all tests significantly influenced the final CSR index. The Y-balance Test, Tuck Jump Assessment, and Isokinetic Test for knee flexion influenced the final CSR index the most, which means that these tests are greatly indicative of functional deficits related to ACL reconstruction. The strength of the extensor (quadriceps) muscle and the quadriceps/hamstring ratio appeared to be non-sensitive for testing functional deficits related to ACL reconstruction. If the test battery includes 4–5 tests, it better differentiates the athletes following ACL reconstruction from those after mild injuries, even if they all were cleared to play.
... The majority of the players with scores from "high to extreme" (8/11) and from "small to moderate" (8/15) were in the U12 category ( Figure 2). The use and interpretation of the z-score allow identification of the players whose injury prevention strategies should be optimised [38] since, as demonstrated, the maturation phase and the chronological age of the female players can influence the jump landing performance [7]. ...
Neuromuscular and biomechanical imbalances that exist in jumping and landing actions should be examined in order to intervene to decrease the risk of ACL injury. The main aim of this study was to analyse and compare, by chronological age, jumping and landing deficits in young female handball players using the Tuck Jump Assessment (TJA). A secondary aim was to relate the qualitative asymmetry values detected using the TJA to the quantitative asymmetry values detected starting from the single leg countermovement jump (SL-CMJ). Sixty-one young female handball players (age: 14.3 ± 1.5 years) were distributed into three groups: U12, U14 and U16 and performed the TJA test and the single leg countermovement jump (SL-CMJ). The female U12 category players obtained the highest scores in the TJA and there were significant differences between the U12 (12.11 ± 1.97) and U14 (10.89 ± 1.74) categories (p = 0.017; ES = 0.374). In the U12 category, the female players presented larger interlimb asymmetry magnitudes in the SL-CMJ test; they also obtained higher scores in the qualitative criteria of the TJA test that referred to asymmetry (r = 0.43; p = 0.027). The analysis of the jumping and landing pattern using TJA allowed us to identify that the lower extremity valgus at landing, foot contact timing not equal and landing contact noise are the main biomechanical deficits in young female handball players. Furthermore, the asymmetry values assessed qualitatively (TJA) are associated with the asymmetry values assessed quantitatively (difference in jump achieved with each limb in the SL-CMJ test) in younger categories.
... The scores of each athlete in the three measures (VIFT, PSS, and ASR) were standardized using the z-score, considering the mean and standard deviation of the twenty players. The sum of the z-score of each test allowed us to determine the total score of athleticism (TSA) of each player [40]. Based on the z-scores, the players were classified as: (i) lower TSA if below 0.0; and (ii) higher TSA if above 0.0. ...
Abstract Purpose This study aimed to (1) analyze the impact of a small-sided game training program in the locomotor profile of youth male soccer players (while interacting with the baseline level – higher and lower level); and (2) test the relationships between variation in locomotor profile and the accumulated demands in 3v3, 5v5 and match over the period of observation. Methods The cohort lasted 3-weeks. Twenty under-17 male amateur soccer players (16.8 ± 0.41 years; experience: 6.35 ± 0.67 years) were assessed twice for their final velocity at 30−15 intermittent fitness test (VIFT), peak speed at 30-m sprint test (PSS) and anaerobic speed reserve (ASR). The PSS was estimated using a Global Positioning System, while the VIFT was estimated using the maximum level attained by the players during the test. Based on the baseline levels, the scores were standardized using the Z-score. The total score of athleticism (TSA) was calculated per player to organize the players into two groups: lower TSA and higher TSA. Over the three weeks of observation, the small-sided games of 3v3 and 5v5 and match demands were monitored using polar team pro. The heart rate responses (mean and peak), distance covered (overall and split by speed thresholds), and peak speed in these games were obtained and summed over the weeks. The repeated measures ANCOVA tested the variations (time) of the locomotor profile of players while considering the baseline as covariable and the group as a factor. The Pearson-product correlation test analyzed the relationships between variations in locomotor profile (Δ, post-baseline) and the accumulated demands in 3v3, 5v5, and match. Results Between-groups analysis (lower TSA vs. higher TSA) revealed no significant differences on VIFT (p = 0.915), PSS (p = 0.269), ASR (p = 0.258) and TSA score (p = 0.138). Within-group (baseline vs. post-observation) analysis revealed significant difference on VIFT (p
... Z-scores allow practitioners to understand how well an individual athlete performs in relation to the squad mean (i.e., how many standard deviations above or below the squad mean each individual athlete's score is) for any given force plate metric and, therefore, make informed training program decisions based on the identified physical strengths and deficits. T-scores, which are an alternative form of standardized score [19], may be calculated by multiplying Z-scores by 10 and adding 50 to provide a more intuitive value for athletes and sports coaches (scaled from 0 to 100 (or 20-80 when considering a typical three standard deviation limit about the mean), with a score of 50, rather than 0, equaling the mean). This notion is visually depicted in Figure 1, where corresponding Z-scores and T-scores for an assumed normally distributed ('bell curve') data set are shown. ...
... Hedges' g effect sizes were interpreted as trivial (≤0. 19), small (0.20-0.49), moderate (0.50-0.79), or large (≥0.80) [32]. A series of two-way mixed-effects model (absolute agreement and average measures) intraclass correlation coefficients (ICCs), along with the upper and lower CI 95 , were used to determine the relative (i.e., rank-order) between-trial reliability of each variable. ...
The countermovement jump (CMJ) is an important test in rugby league (RL), and the force plate is the recommended assessment device, as it permits the calculation of several variables that explain jump strategy, alongside jump height. The purpose of this study was to produce normative CMJ data and objective benchmarks for professional RL forwards and backs. Normative data for jump height, modified reactive strength index, and jump momentum are provided for 121 professional RL players (66 forwards and 55 backs) who completed CMJ testing on a portable force plate during preseason training. Standardized T-scores (scaled from 0 to 100) were calculated from the respective positional group mean and standard deviation to create CMJ performance bands that were combined with a qualitative description (ranging from extremely poor to excellent) and a traffic light system to facilitate data interpretation and objective benchmark setting by RL practitioners. The jump height and modified reactive strength index benchmarks were larger for the lighter backs, whereas the jump momentum benchmarks were larger for the heavier forwards. The presented novel approach to compiling and presenting normative data and objective benchmarks may also be applied to other data (i.e., from other tests or devices) and populations.
... While talent identification processes have been extensively reported (Falk et al., 2004;Pyne et al., 2005;Pion et al., 2015;Till et al., 2016;Dodd and Newans, 2018;Johnston et al., 2018), standards and benchmarks are typically reported univariately; that is, each attribute is assessed in isolation. For example, players could be standardized within each attribute (i.e., z-scores) to determine where the athlete sits with respect to the rest of the athletic population (Turner et al., 2019). However, this method has a flaw, in that some attributes are negatively correlated, as well as physiological/mechanical characteristics such as maximal sprint speed and endurance capacity (Sánchez-García et al., 2018). ...
Athletes often require a mix of physical, physiological, psychological, and skill-based attributes that can be conflicting when competing at the highest level within their sport. When considering multiple variables in tandem, Pareto frontiers is a technique that can identify the observations that possess an optimal balance of the desired attributes, especially when these attributes are negatively correlated. This study presents Pareto frontiers as a tool to identify athletes who possess an optimal ranking when considering multiple metrics simultaneously. This study explores the trade-off relationship between batting average and strike rate as well as bowling strike rate, economy, and average in Twenty 20 cricket. Eight hundred ninety-one matches of Twenty 20 cricket from the men's (MBBL) and women's (WBBL) Australian Big Bash Leagues were compiled to determine the best batting and bowling performances, both within a single innings and across each player's Big Bash career. Pareto frontiers identified 12 and seven optimal batting innings performances in the MBBL and WBBL respectively, with nine and six optimal batting careers respectively. Pareto frontiers also identified three optimal bowling innings in both the MBBL and WBBL and five and six optimal bowling careers in MBBL and WBBL, respectively. Each frontier identified players that were not the highest ranked athlete in any metric when analyzed univariately. Pareto frontiers can be used when assessing talent across multiple metrics, especially when these metrics may be conflicting or uncorrelated. Using Pareto frontiers can identify athletes that may not have the highest ranking on a given metric but have an optimal balance across multiple metrics that are associated with success in a given sport.
