Annual Age-Grouping and Athlete Development

Abstract

Annual age-grouping is a common organizational strategy in sport. However, such a strategy appears to promote relative age effects (RAEs). RAEs refer both to the immediate participation and long-term attainment constraints in sport, occurring as a result of chronological age and associated physical (e.g. height) differences as well as selection practices in annual age-grouped cohorts. This article represents the first meta-analytical review of RAEs, aimed to collectively determine (i) the overall prevalence and strength of RAEs across and within sports, and (ii) identify moderator variables. A total of 38 studies, spanning 1984–2007, containing 253 independent samples across 14 sports and 16 countries were re-examined and included in a single analysis using odds ratios and random effects procedures for combining study estimates. Overall results identified consistent prevalence of RAEs, but with small effect sizes. Effect size increased linearly with relative age differences. Follow-up analyses identified age category, skill level and sport context as moderators of RAE magnitude. Sports context involving adolescent (aged 15–18 years) males, at the representative (i.e. regional and national) level in highly popular sports appear most at risk to RAE inequalities. Researchers need to understand the mechanisms by which RAEs magnify and subside, as well as confirm whether RAEs exist in female and more culturally diverse contexts. To reduce and eliminate this social inequality from influencing athletes’ experiences, especially within developmental periods, direct policy, organizational and practitioner intervention is required.

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Annual Age-Grouping and Athlete
Development
A Meta-Analytical Review of Relative Age Effects in Sport
Stephen Cobley,
1
Joseph Baker,
2
Nick Wattie
1
and Jim McKenna
1
1 Carnegie Research Institute, Leeds Metropolitan University, Leeds, West Yorkshire, UK
2 School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada
Contents
Abstract................................................................................. 235
1. Background .......................................................................... 236
1.1 Explanations for Relative Age Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
1.2 Rationale for a Meta-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
1.3 Study Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
2. Methods.............................................................................. 238
2.1 Sample of Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
2.2 Study Review Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
2.3 Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
3. Results ............................................................................... 239
3.1 Overall Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
3.2 Subgroup Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
3.2.1 Sex......................................................................... 240
3.2.2 Age Category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
3.2.3 Skill Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
3.2.4 Sport Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
4. Discussion............................................................................. 240
4.1 General Findings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
4.2 Context-Specific Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
4.3 Eliminating Relative Age Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
4.4 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
5. Conclusions........................................................................... 254
Abstract Annual age-grouping is a common organizational strategy in sport.
However, such a strategy appears to promote relative age effects (RAEs).
RAEs refer both to the immediate participation and long-term attainment
constraints in sport, occurring as a result of chronological age and associated
physical (e.g. height) differences as well as selection practices in annual age-
grouped cohorts. This article represents the first meta-analytical review of
RAEs, aimed to collectively determine (i) the overall prevalence and strength
of RAEs across and within sports, and (ii) identify moderator variables.
A total of 38 studies, spanning 1984–2007, containing 253 independent samples
across 14 sports and 16 countries were re-examined and included in a single
analysis using odds ratios and random effects procedures for combining
REVIEW ARTICLE Sports Med 2009; 39 (3): 235-256
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study estimates. Overall results identified consistent prevalence of RAEs, but
with small effect sizes. Effect size increased linearly with relative age differ-
ences. Follow-up analyses identified age category, skill level and sport context
as moderators of RAE magnitude. Sports context involving adolescent (aged
15–18 years) males, at the representative (i.e. regional and national) level in
highly popular sports appear most at risk to RAE inequalities. Researchers
need to understand the mechanisms by which RAEs magnify and subside, as
well as confirm whether RAEs exist in female and more culturally diverse
contexts. To reduce and eliminate this social inequality from influencing
athletes’ experiences, especially within developmental periods, direct policy,
organizational and practitioner intervention is required.
1. Background
Within many sport contexts, the youth stages
of participation are often organized into annual
age-groups using specific cut-off dates (e.g.
1 September in the UK). Whilst with honourable
intention and for the purposes of competition
organization and values of fair play, such a policy
remains insensitive to the subtle chronological
age differences (referred to as ‘relative age’ dif-
ferences) between members within an annual
cohort.
[1]
These differences are associated with im-
mediate and long-term consequences, commonly
known as ‘relative age effects’ (RAEs).
[1-3]
Grondin et al.
[2]
were the first to assess the
consequences of annual age-grouping in sports,
following consistent reports of attainment
differentials according to relative age in educa-
tion.
[4-6]
They examined the birth-date distribu-
tions of Canadian ice-hockey and volleyball
players, participating at recreational, competitive
and senior professional levels for the 1981–2
season. Their results identified significant and
repeated over-representations of ice-hockey
players born in the first quartile (i.e. the 3 months
after age-group cut-off dates) for each age-group
category and level of competition, including profes-
sionals, while in volleyball over-representations
were observed for the elite representative levels.
Barnsley et al.
[1]
also identified birth-date dif-
ferentials amongst ice-hockey players in the
Canadian elite developmental leagues and Na-
tional Hockey League (NHL) for 1983–4, and
later found similar inequalities in the junior
representative leagues (at ages ‡11 years).
[3]
Together, these studies suggested that being re-
latively older within an annual sporting cohort
provided significant attainment advantages when
compared with those who were relatively young-
er. Many studies have identified similar differ-
entials in birth-date patterns across youth age-
groups and levels of competition for the sports of
baseball,
[7-8]
ice hockey,
[9]
soccer
[10-12]
and ten-
nis.
[13,14]
Studies have also identified RAEs in
other sports, but essentially in high performing
samples, including Australian Rules football,
[15]
cricket,
[16,17]
netball
[17]
and both codes of rug-
by.
[15]
It is important to note that RAEs are not
universal. In fact, in several contexts (e.g. golf
[18]
)
RAEs have not been identified or predicted to
occur. These contexts are typically free of annual
age-grouping and other requisite precursor con-
ditions (e.g. selection processes in tiers of youth
competition).
1.1 Explanations for Relative Age Effects
Although previous studies (until recently
[19]
)
did not include physical or maturational indices,
most suggested physical differences (i.e. greater
chronological age and likelihood of more
advanced physical characteristics) as being pri-
marily responsible for RAEs.
[3,20,21]
Attributes of
greater height, mass (to a degree), aerobic power,
muscular strength, endurance and speed do pro-
vide performance advantages in most sports.
[22-23]
Furthermore, during adolescence, a time when
annual age-groupings are employed and where
sport competition can be intensive, a 1-year age
difference, especially during the stages of puberty
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(i.e. 13–15 years of age in boys; 12–14 years in
girls) can heighten physical
[24,25]
and perfor-
mance
[23,26]
differences. Thus, relatively older
athletes may have an increased likelihood of ex-
hibiting advanced physical characteristics and
entering puberty earlier, compared with their re-
latively younger peers. In sports where body size,
strength and power convey advantages, elite ju-
nior athletes have been identified as above aver-
age for height and weight when compared with
age-matched normative data (e.g. soccer
[27,28]
).
Likewise in gymnastics, where height and mass
gain impedes flexibility, rotational speed and the
strength to mass ratio, maturational delay in more
highly skilled gymnasts has been observed.
[14]
In
fact, a greater frequency of relatively younger
gymnasts has been reported in high performance
contexts.
[29]
A complementary and interacting mechanism,
relating to selection and experience, has also been
proposed to account for the long-term propaga-
tion of RAEs. Being relatively older is more likely
to provide a performance and selection ad-
vantage when assessed or evaluated (by coaches)
against annual age-group peers. This selection
advantage increases the likelihood of access to
higher levels of competition, training and coach-
ing.
[30]
It is likely that such access will be accom-
panied by increases in volumes of practice,
training load and competition frequency, thereby
generating an experience advantage over non-
selected and likely relatively younger peers. In
contrast, those not selected are considered less
able to access practice and coaching expertise
facilities, or higher levels of competition, con-
straining their sporting involvement and devel-
opment. Events associated with selection, trials
or talent identification are thus postulated to
differentiate an individual’s ability to invest in
practice and accumulate sport-specific skill and
experience, factors deemed critical for attain-
ment.
[31,32]
Selection and exposure to practice
and match-play may provide significant technical
and game intelligence advantages
[33,34]
to selected
relatively older players, accounting for their over-
representation in senior professional sports.
Other interacting psychological and broader
sociocultural mechanisms have also been pre-
sented to account for RAEs. Linked with
selection and experience differences according to
relative age, psychological disparities have also
been suggested.
[21]
Relatively older players may
be more likely to develop higher perceptions of
competence
[35,36]
and self-efficacy.
[37]
In compar-
ison, relatively younger athletes, faced with con-
sistent sport selection disadvantages, may be
more likely to have negative sport experiences,
develop low competence perceptions, and thus
terminate sport involvement.
[38-40]
Related to
sociocultural influences, two studies have asso-
ciated population and sport participation growth
with heightened competition in youth sport con-
texts, and thereby an inflated likelihood of
RAEs.
[41,42]
Likewise, sport policies that have
attempted to address performance concerns on
the international stage by adopting earlier com-
petition, talent identification and streaming have
also been associated with the first appearances of
RAEs in sport.
[41]
Such sociocultural forces
should be kept in mind with reference to the
rationale and purpose for the present study.
1.2 Rationale for a Meta-Analysis
RAEs appear to be complex phenomena, with
sociocultural antecedents combining with inter-
individual age and physical differences to affect
sport attainment. To date, a variety of sports
contexts differing in age categories, levels of
competition and cultures have been assessed for
RAEs. Since the narrative review of Musch and
Grondin,
[21]
many more samples within studies
have been collected and examined, yet several
questions remain unresolved. For example, how
prevalent and robust are RAEs across and within
sports contexts? What factors modify the risk
of RAEs in a sports context? By employing
meta-analytical methods, these questions can be
addressed as most previous studies provided
consistent sample and sport context informa-
tion, often presenting birth-date frequency data
(i.e. the proxy measure to identify relative age in
an annual age-grouping) in quarterly or half-
yearly distributions. Such information is valuable
if support toward direct intervention is to be
generated. Furthermore, by identifying potential
Relative Age Effects in Sport 237
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factors that moderate risk size, sports contexts
can consider strategies that will help address and
remove the unnecessary RAE inequality.
1.3 Study Purpose
The purpose of this meta-analytical review
was to generate a broad picture of RAE pre-
valence in sport by systematically re-examining
the numerous ‘snap-shots’ taken of sport con-
texts in previous studies. For the first time, meta-
analytical methods were used to ascertain the risk
size and moderating factors of RAEs across and
within sports. We hypothesized that RAEs were
apparent across sexes in highly popular team
sport contexts where (i) annual age-grouping
policies were employed in youth participation
stages, and (ii) youth stages would include in-
tensive competition and a skill level hierarchy,
which involved selection mechanisms regulating
access to higher levels of competition. In an at-
tempt to directly explain and account for the
consistent birth-date discrepancies in senior pro-
fessional sport, we further hypothesized that
RAE risk size increased with skill level and
chronological age.
2. Methods
2.1 Sample of Studies
Published research papers, including those
published in peer-reviewed conference proceed-
ings, were tracked, collected and analysed over a
3-month period, specifically November 2006 to
January 2007. This included searches of PubMed,
PsychINFO and PsychARTICLES databases
using the keywords ‘relative age effect’, ‘birth-
date effect’, ‘season of birth’, ‘age position’ and
their derivations. Additional criteria for inclusion
in the meta-analysis were that papers reported
both sample characteristics and information re-
garding the sport context. Sample characteristics
pertaining to birth date, sex and chronological
age at the time of data collection were extra-
polated along with the type of sport, level of
competition, country and competition year(s) of
data collection. Those studies reporting birth-
date distribution in quartiles (i.e. per 3 months),
halves (i.e. 6 months) or both were included.
After obtaining all listed studies, reference
sections were further examined to locate other
relevant studies. All articles written in French
and English were identified and interpreted. At
the time of writing, we were not aware of any
RAE-related publications in other languages.
Finally, where sample and sport context in-
formation was not presented, authors were con-
tacted for respective information. Five authors
were contacted for further information, with
three able to return required information, which
often related to sample characteristics (e.g. sport
context, skill level). These authors were also
asked if they were aware of any additional studies
not included in our list. No study additions were
made through this procedure.
1
The overall pro-
cess yielded 38 studies, spanning 1984 (i.e. the
first published study of RAEs in sport
[2]
) to Jan-
uary 2007,
2
with 253 independent samples
in 14 sports, across 16 countries. Participants
involved in identified studies were either current or
former sports players who competed at a range of
levels, including recreational, junior and national
senior representatives.
2.2 Study Review Procedure
All articles were read and examined in full by
the first author. Information extracted from each
of the studies was categorized and cross-validated
by an independent reviewer. No categorization
or reporting accuracy errors were evident in the
extraction of variable information. Of the 38
studies, only three (reflecting ten samples) failed
to report complete sample information for either
total sample size or the distribution of birth dates
after contacting the authors. These samples
therefore had to be excluded from particular or
whole aspects of the data analysis.
1The search and data collection procedure missed one study, which came to light following data analysis.
[43]
2Since the time of data collection and analysis several more studies have been published.
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2.3 Data Analysis
Across sport contexts, studies generally stan-
dardized relative age differences by categorizing
birth date into quartiles (i.e. 3-month periods)
following cut-off dates. From these original data,
odds ratios (ORs) and 95%confidence intervals
(CIs) were calculated for both quartile and half-
year distributions.
3
Specifically, for each sample
reported, birth-date distributions (e.g. number of
people in quartile 1) were compared against an
expected frequency, assuming an equal distribu-
tion (e.g. N =100, expected quartile count =
100/4=25). This comparator (or control group)
value was utilized similar to previous studies
(except for Grondin et al.
[2]
). To clarify this as-
sumption, however, national population statis-
tics were checked and findings suggest that
variations in birth-date distribution occur across
a calendar year. For example, for the period
of 1970–2000 in Canada and the UK (countries
where many relative age studies have been
conducted), a consistently higher number of
births occurred in the spring–summer (i.e.
April–August) months.
[45,46]
In comparison, the
months/quartiles coinciding with dates used for
age-grouping in sport for these countries re-
ported a consistently lower number of births. So,
while the assumption of equal distribution was
not completely accurate, birth distributions were
not correlated with age-grouping and participa-
tion trends in sport, thus national census data
were deemed unlikely to bias or influence sample
OR calculations.
When comparing quartiles and half-year dis-
tributions in all OR analyses, quartile 4 (i.e. the
relatively youngest members) and the second
6 months of annual age-groupings were assigned
as referent groups. Overall summary effect sizes
were calculated using DerSimonian and Laird
[47]
methods for combining samples (see Sutton
et al.
[48]
). Since heterogeneity between studies was
expected because of variety in sport contexts and
samples characteristics, a random effects model
was used. The outcomes were weighted by the
inverse variance. Heterogeneity was assessed
using the Cochran Q value.
[49]
When hetero-
geneity was detected, sources of heterogeneity
were explored using sub-stratification analysis.
All analyses were conducted using either Micro-
soft Excel or RevMan 4.2.
[50]
3. Results
3.1 Overall Results
For quartile analyses, the birth dates of
124 524 sport participants (former or present) in
246 samples were compared. Descriptive analyses
identified an uneven distribution of birth dates in
the overall sample (i.e. quartile 1 [Q1] =31.2%;
quartile 2 [Q2] =26.1%; quartile 3 [Q3] =22.3%;
quartile 4 [Q4] =20.6%). For half-year compari-
sons, data from seven additional samples were
included,
[16,51]
raising total participants to
130 108 across 253 independent samples. This
sample equated to 57.26%(born in the first
6 months of an age-grouping year) and 42.74%
(born in the second 6 months of an age-grouping
year).
Based on established criteria for interpreting
effect sizes,
[52]
DerSimonian and Laird
[47]
proce-
dures revealed a significant overall, but small, OR
of 1.65 (95%CI 1.54, 1.77; Z=14.46, p <0.001)
across all samples for the likelihood of sports
participants to be born in Q1 versus Q4 of an
age-grouping year. Heterogeneity was also evi-
dent between samples (Q value =1731.1, degrees
of freedom [df] =245, p <0.0001). A decreasing
linear trend of RAE risk was identified following
comparisons between Q2 and Q4, with an overall
OR of 1.37 (95%CI 1.30, 1.44; Z =11.59,
p<0.001) and Q3 and Q4, OR 1.13 (95%CI 1.10,
1.16; Z =7.88,p <0.001). Heterogeneity was also
apparent for these comparisons (Q2 and Q4:
Q value =957.9, df =245, p <0.0001; Q3 and Q4:
Qvalue=306.2, df =245, p <0.005). Relative age
effect sizes were also small when comparing
3An odds ratio is considered as a comparison between the odds of exposure (i.e. to a sport context) compared to
the odds of exposure (i.e. general population). Confidence intervals quantify the uncertainty in measurement.
It is usually reported as 95%CI, which is the range of values within which we can be 95%sure that the true value
for the whole population lies. See Rudas
[44]
for an introduction.
Relative Age Effects in Sport 239
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between the first 6 months and the second
6 months, with an OR of 1.39 (95%CI 1.32, 1.47,
Z=12.73, p <0.0001) found. Heterogeneity be-
tween studies was again apparent (Q value
=1416.9, df =252, p <0.001). For all analyses,
funnel plot assessments did not suggest publica-
tion bias was evident.
[53,54]
However, as evidence
for heterogeneity was consistent, follow-up sub-
group stratification analyses were conducted
(as recommended by Gelber and Goldhirsch
[55]
as well as Yusuf et al.
[56]
) to identify possible
sources of influence. Similar procedures were
used; however, only comparisons between Q1
versus Q4 and the first 6 months versus the
second 6 months were made.
3.2 Subgroup Results
3.2.1 Sex
Tables I and II, respectively, show the results
of ORs for individual male and female samples as
well as overall summary analyses. Of these sam-
ples, only 24 directly examined relative age effects
in female athletes, comprising 3321 (or 2%)ofall
participants. Considering samples available, sex
made little difference to the overall ORs, whether
based on quartile or half-yearly distributions
(males Q1 vs Q4 =OR: 1.65, 95%CI 1.54, 1.77;
first 6 months vs second 6 months =OR: 1.39,
95%CI 1.32, 1.47; females Q1 vs Q4 =OR: 1.21,
95%CI 1.10, 1.33; first half vs second half =OR:
1.39, 95%CI 1.26, 1.54).
3.2.2 Age Category
To consider age as a moderator of risk, ages
within samples were categorized into child
(<11 years), junior (11–14 years), adolescent
(15–18 years) and senior (>18 years) for sub-
category analyses. We excluded samples from the
analysis where ages spanned across these cate-
gories (e.g. Baxter-Jones et al.
[25]
). Table III
summarizes results of age-category analyses.
Overall summary calculations identified small
significant effects across age categories, regard-
less of whether relative age was considered in
quarter- or half-yearly distributions. Risk pro-
gressively increased with age from the child
category to the adolescent (15–18 years) age
range. For the comparison between Q1 and Q4,
small-moderate effects (OR: 2.36, 95%CI 2.00,
2.79) were evident at the adolescent stage, before
declining at the senior (19 years plus) age cate-
gory (OR: 1.44, 95%CI 1.35, 1.53).
3.2.3 Skill Level
Prior to analysis, all samples were categorized
into one of four skill levels: recreational (e.g.
leisure and house leagues), competitive (often
associated with juniors and amateurs), re-
presentative (often associated with regional and
national representation) and elite (regarded as
professional or senior national representative).
Overall, summary results identified small signifi-
cant ORs regardless of skill category (see table IV);
however, risk increased with skill level, with the
highest risk evident at the representative (pre-
elite) stage (i.e. Q1 vs Q4 =OR: 2.77, 95%CI 2.36,
3.24). Interestingly, summary ORs suggest that
the risks of RAEs are lower at the elite stage than
in the representative stage (OR: 1.42, 95%CI
1.34, 1.51).
3.2.4 Sport Context
While 14 sports have been assessed for relative
age effects, most studies focused upon ice hockey
(32.8%), soccer (30%) and baseball (13%). Re-
gardless of whether quartile or half-yearly sum-
mary ORs were considered, small effects were
apparent in these sports, as well as in basketball
and volleyball (i.e. the next two mostly examined
contexts; see table V). Only in American Football
were ORs non-significant. Other sports contexts
were not examined due to low sample numbers.
4. Discussion
4.1 General Findings
This article represents the first meta-analytical
study of RAEs, synthesizing data from samples in
previous research (spanning 1984 to January
2007) into a single analysis, whilst partially con-
trolling for wider population trends in this peri-
od. Its primary purpose was to determine the
overall prevalence and strength of RAEs in sport.
A secondary purpose was to identify risk change
according to moderator variables, with such
240 Cobley et al.
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Table I. Unadjusted odds ratios (ORs) for independent male subjects examining relative age effect in sport
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Grondin et al.
[2]
8–9 Ice hockey Junior AA 94 3.09 (1.27, 7.51) 2.54 (1.03, 6.27) 1.90 (0.75, 4.82) 1.93 (0.48, 3.95)
8–9 Ice hockey Junior BB 171 2.59 (1.35, 4.96) 2.27 (1.17, 4.38) 1.90 (0.97, 3.72) 1.67 (0.59, 2.82)
8–9 Ice hockey Junior CC 256 2.37 (1.43, 3.94) 1.67 (0.99, 2.82) 1.35 (0.78, 2.3) 1.72 (0.65, 2.64)
8–9 Ice hockey Novice recreation 110 1.60 (0.74, 3.45) 2.05 (0.96, 4.34) 0.85 (0.36, 1.95) 1.97 (0.51, 3.81)
10–11 Ice hockey Junior AA 124 5.27 (2.33, 11.9) 2.90 (1.24, 6.78) 2.09 (0.87, 5.01) 2.64 (0.52, 4.99)
10–11 Ice hockey Junior BB 206 2.31 (1.31, 4.07) 1.93 (1.08, 3.44) 1.18 (0.64, 2.18) 1.94 (0.61, 3.14)
10–11 Ice hockey Junior CC 273 1.58 (0.96, 2.61) 2.04 (1.25, 3.32) 1.30 (0.78, 2.17) 1.57 (0.66, 2.38)
10–11 Ice hockey Novice recreation 138 1.24 (0.62, 2.44) 1.03 (0.51, 2.07) 1.48 (0.76, 2.88) 0.91 (0.56, 1.63)
12–13 Ice hockey Junior AA 120 3.05 (1.45, 6.44) 1.94 (0.89, 4.2) 1.05 (0.45, 2.43) 2.42 (0.52, 4.61)
12–13 Ice hockey Junior BB 202 3.09 (1.66, 5.74) 3.04 (1.63, 5.65) 2.04 (1.07, 3.88) 2.01 (0.61, 3.28)
12–13 Ice hockey Junior CC 298 1.21 (0.76, 1.93) 1.46 (0.93, 2.3) 0.96 (0.6, 1.55) 1.36 (0.67, 2.02)
12–13 Ice hockey Novice recreation 90 1.09 (0.47, 2.48) 1.00 (0.43, 2.29) 1.00 (0.43, 2.29) 1.04 (0.48, 2.13)
14–15 Ice hockey Youth AA 131 2.30 (1.15, 4.58) 1.56 (0.76, 3.19) 0.82 (0.37, 1.79) 2.11 (0.54, 3.89)
14–15 Ice hockey Youth BB 194 2.00 (1.12, 3.56) 1.39 (0.76, 2.53) 1.48 (0.81, 2.69) 1.36 (0.61, 2.22)
14–15 Ice hockey Youth CC 301 1.29 (0.81, 2.05) 1.65 (1.05, 2.59) 0.98 (0.6, 1.58) 1.48 (0.67, 2.2)
14–15 Ice hockey Novice recreation 67 0.87 (0.32, 2.34) 1.31 (0.51, 3.35) 1.00 (0.37, 2.63) 1.09 (0.43, 2.5)
Senior Ice hockey NHL professional 386 1.71 (1.14, 2.58) 1.42 (0.93, 2.15) 1.29 (0.85, 1.96) 1.36 (0.7, 1.93)
Senior Ice hockey Varsity 177 1.30 (0.72, 2.33) 1.17 (0.64, 2.12) 0.95 (0.51, 1.74) 1.26 (0.59, 2.11)
Senior Ice hockey College AAA 150 2.20 (1.13, 4.27) 1.91 (0.98, 3.74) 1.12 (0.55, 2.29) 1.94 (0.56, 3.41)
16–19 Ice hockey Junior elite developmental 171 3.47 (1.82, 6.62) 2.00 (1.01, 3.92) 1.66 (0.83, 3.31) 2.05 (0.58, 3.49)
14–15 Ice hockey Youth AAA elite developmental 167 3.05 (1.57, 5.91) 2.60 (1.32, 5.08) 1.70 (0.84, 3.42) 2.09 (0.58, 3.58)
12–13 Volleyball Junior 46 1.66 (0.45, 6.12) 3.00 (0.87, 10.3) 2.00 (0.55, 7.16) 1.55 (0.36, 4.26)
14–15 Volleyball Youth cadet 31 0.77 (0.19, 3.16) 0.88 (0.22, 3.52) 0.77 (0.19, 3.16) 0.93 (0.29, 3.17)
16–17 Volleyball Youth juvenile 24 0.83 (0.16, 4.29) 1.00 (0.2, 4.95) 1.16 (0.24, 5.61) 0.84 (0.24, 3.38)
14–15 Volleyball Provincial youth cadet 211 1.21 (0.7, 2.08) 1.17 (0.67, 2.01) 1.10 (0.63, 1.91) 1.13 (0.62, 1.8)
16–17 Volleyball Provincial youth juvenile 210 1.17 (0.67, 2.03) 0.93 (0.53, 1.64) 1.45 (0.85, 2.48) 0.85 (0.62, 1.37)
17–19 Volleyball Provincial youth junior 64 1.14 (0.42, 3.09) 0.92 (0.33, 2.58) 1.50 (0.56, 3.95) 0.82 (0.42, 1.93)
Senior Volleyball Provincial senior 50 2.80 (0.77, 10.1) 3.20 (0.89, 11.4) 3.00 (0.83, 10.7) 1.50 (0.38, 3.94)
Continued next page
Relative Age Effects in Sport 241
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This material is
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Table I. Contd
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Barnsley et al.
[1]
16–20 Ice hockey WHL amateur developmental 698 4.56 (3.23, 6.42) 3.23 (2.27, 4.59) 2.10 (1.46, 3.03) 2.50 (0.76, 3.27)
16–20 Ice hockey OHL amateur developmental 350 3.76 (2.36, 5.98) 2.84 (1.76, 4.56) 1.60 (0.97, 2.65) 2.53 (0.68, 3.69)
Senior Ice hockey NHL professional 715 1.97 (1.45, 2.67) 1.83 (1.35, 2.49) 1.35 (0.98, 1.85) 1.61 (0.77, 2.09)
Daniel and
Janssen
[41]
Senior Basketball NBA professional 297 1.11 (0.7, 1.75) 0.94 (0.59, 1.5) 1.18 (0.75, 1.86) 0.94 (0.67, 1.39)
Senior Baseball MLB professional 682 1.24 (0.92, 1.67) 1.11 (0.82, 1.5) 0.90 (0.66, 1.23) 1.23 (0.77, 1.60)
Senior Football CFL professional 342 1.00 (0.64, 1.54) 1.17 (0.76, 1.79) 1.32 (0.87, 2.02) 0.93 (0.69, 1.34)
Senior Football CFL professional 436 0.89 (0.61, 1.3) 0.96 (0.66, 1.4) 0.93 (0.64, 1.35) 0.96 (0.72, 1.33)
Senior Football AFC professional 777 1.25 (0.94, 1.66) 1.21 (0.91, 1.61) 1.18 (0.89, 1.57) 1.12 (0.78, 1.44)
Senior Football NFC professional 749 1.15 (0.86, 1.53) 0.98 (0.73, 1.31) 1.09 (0.82, 1.46) 1.01 (0.78, 1.3)
Senior Ice hockey NHL professional 103 1.10 (0.52, 2.33) 0.60 (0.26, 1.36) 0.96 (0.45, 2.06) 0.87 (0.51, 1.7)
Senior Ice hockey NHL professional 318 0.92 (0.59, 1.41) 0.87 (0.56, 1.35) 0.81 (0.52, 1.27) 0.98 (0.68, 1.44)
Senior Ice hockey NHL professional 320 1.05 (0.67, 1.63) 0.97 (0.62, 1.51) 1.12 (0.73, 1.74) 0.95 (0.68, 1.39)
Senior Ice hockey NHL professional 355 0.94 (0.62, 1.43) 1.03 (0.68, 1.56) 0.96 (0.63, 1.46) 1.00 (0.69, 1.44)
Senior Ice hockey NHL professional 775 2.14 (1.59, 2.88) 2.05 (1.52, 2.75) 1.37 (1.00, 1.87) 1.76 (0.78, 2.26)
Senior Ice hockey NHL professional 217 2.21 (1.29, 3.78) 1.31 (0.74, 2.31) 1.18 (0.66, 2.09) 1.61 (0.62, 2.57)
Barnsley and
Thompson
[3]
7–8 Ice hockey Junior-minor-mite 1 676 1.12 (0.93, 1.36) 1.08 (0.89, 1.31) 0.98 (0.81, 1.19) 1.11 (0.84, 1.31)
9–10 Ice hockey Junior-minor-mite 1 839 1.10 (0.91, 1.32) 1.17 (0.97, 1.4) 1.07 (0.89, 1.29) 1.09 (0.85, 1.28)
11–12 Ice hockey Junior-minor-pee wee 1 536 1.13 (0.92, 1.38) 1.23 (1.01, 1.51) 1.05 (0.86, 1.29) 1.15 (0.84, 1.37)
13–14 Ice hockey Junior-minor-bantam 1 112 1.37 (1.08, 1.75) 1.24 (0.97, 1.59) 0.89 (0.68, 1.15) 1.39 (0.8, 1.71)
15–16 Ice hockey Youth-minor midget 815 1.18 (0.89, 1.56) 1.39 (1.05, 1.83) 1.19 (0.9, 1.58) 1.17 (0.78, 1.48)
17–18 Ice hockey Youth-minor-juvenile 220 1.45 (0.85, 2.48) 1.52 (0.89, 2.59) 1.02 (0.58, 1.78) 1.47 (0.63, 2.33)
19–20 Ice hockey Youth representative 115 0.90 (0.44, 1.85) 0.66 (0.31, 1.4) 0.90 (0.44, 1.85) 0.82 (0.53, 1.55)
7–8 Ice hockey Mite-lowest tier 1 676 1.12 (0.93, 1.36) 1.08 (0.89, 1.31) 0.98 (0.81, 1.19) 1.11 (0.84, 1.31)
9–10 Ice hockey Mite-low tier 764 0.80 (0.6, 1.06) 0.88 (0.67, 1.17) 0.96 (0.72, 1.27) 0.86 (0.78, 1.1)
9–10 Ice hockey Mite-mid tier 789 1.08 (0.81, 1.43) 1.18 (0.89, 1.56) 1.06 (0.8, 1.41) 1.09 (0.78, 1.39)
9–10 Ice hockey Mite-upper tier 286 3.15 (1.88, 5.28) 2.90 (1.73, 4.88) 1.87 (1.09, 3.21) 2.10 (0.66, 3.18)
11–12 Ice hockey Pee wee low tier 461 0.73 (0.5, 1.07) 1.06 (0.74, 1.52) 1.00 (0.7, 1.44) 0.89 (0.72, 1.23)
11–12 Ice hockey Pee wee mid tier 746 1.02 (0.76–1.36) 1.21 (0.91, 1.61) 1.02 (0.76, 1.37) 1.10 (0.77, 1.41)
11–12 Ice hockey Pee wee upper tier 329 2.40 (1.54, 3.75) 1.69 (1.06, 2.67) 1.23 (0.76, 1.98) 1.83 (0.68, 2.68)
Continued next page
242 Cobley et al.
ª2009 Adis Data Information BV. All rights reserved. Sports Med 2009; 39 (3)

This material is
the copyright of the
original publisher.
Unauthorised copying
and distribution
is prohibited.
Table I. Contd
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Barnsley and
Thompson
[3]
13–14 Ice hockey Bantam low tier 586 0.81 (0.58, 1.13) 0.86 (0.62, 1.2) 1.14 (0.83, 1.56) 0.78 (0.75, 1.04)
13–14 Ice hockey Bantam mid tier 206 1.47 (0.85, 2.55) 1.28 (0.73, 2.24) 1.14 (0.64, 2.01) 1.28 (0.62, 2.07)
13 Ice hockey Junior AA minor 183 4.23 (2.16, 8.26) 3.23 (1.63, 6.39) 2.29 (1.13, 4.62) 2.26 (0.59, 3.8)
14 Ice hockey Junior AA major 137 3.35 (1.63, 6.88) 2.58 (1.24, 5.38) 1.11 (0.49, 2.5) 2.80 (0.54, 5.15)
15–16 Ice hockey Youth midget low 463 0.91 (0.63, 1.33) 1.18 (0.82, 1.69) 1.10 (0.77, 1.59) 0.99 (0.72, 1.36)
15–16 Ice hockey Youth midget mid 227 1.24 (0.72, 2.13) 1.60 (0.94, 2.7) 1.20 (0.69, 2.05) 1.29 (0.63, 2.03)
15 Ice hockey Youth AA major 125 2.81 (1.32, 5.98) 2.25 (1.04, 4.85) 1.75 (0.79, 3.85) 1.84 (0.53, 3.41)
17–18 Ice hockey Youth mid tier 220 1.88 (1.06, 3.31) 1.08 (0.59, 1.98) 2.20 (1.26, 3.85) 0.92 (0.62, 1.48)
19–20 Ice hockey Youth mid tier 115 1.21 (0.61, 2.38) 0.66 (0.32, 1.38) 0.90 (0.45, 1.83) 0.98 (0.54, 1.8)
Boucher and
Halliwell
[57]
Senior Ice hockey NHL professional 1 116 2.15 (1.68, 2.74) 1.86 (1.45, 2.38) 1.28 (0.99, 1.66) 1.75 (0.81, 2.15)
8–17 Ice hockey Junior regional representative 1 085 2.53 (1.96, 3.26) 2.18 (1.68, 2.83) 1.76 (1.35, 2.29) 1.70 (0.81, 2.1)
Grondin and
Trudeau
[58]
Senior Ice hockey NHL professional 388 1.55 (1.02, 2.34) 1.82 (1.21, 2.74) 1.24 (0.81, 1.9) 1.50 (0.7, 2.12)
Senior Ice hockey NHL professional 79 1.62 (0.67, 3.92) 1.00 (0.39, 2.54) 1.31 (0.53, 3.23) 1.13 (0.46, 2.43)
Senior Ice hockey NHL professional 54 1.45 (0.49, 4.26) 1.54 (0.53, 4.5) 0.90 (0.29, 2.84) 1.57 (0.39, 3.99)
Thompson et al.
[7]
Senior Baseball MLB professional 682 1.36 (1.00, 1.84) 1.30 (0.95, 1.76) 1.10 (0.81, 1.5) 1.26 (0.77, 1.64)
Senior Baseball MLB professional 837 1.29 (0.99, 1.7) 1.12 (0.85, 1.47) 1.03 (0.78, 1.36) 1.19 (0.79, 1.5)
Barnsley et al.
[11]
Senior Soccer Professional national 528 1.50 (1.05, 2.12) 1.38 (0.96, 1.96) 1.40 (0.98, 1.99) 1.20 (0.74, 1.61)
16–17 Soccer Junior elite national 287 5.86 (3.35, 10.2) 4.40 (2.5, 7.77) 1.77 (0.95, 3.28) 3.70 (0.64, 5.7)
19–20 Soccer Developmental elite national 288 6.13 (3.51, 10.7) 4.27 (2.42, 7.53) 1.68 (0.90, 3.12) 3.88 (0.64, 5.99)
Brewer et al.
[27]
16–17 Soccer National junior 59 34.0 (4.09, 281) 12.0 (1.37, 104) 12.0 (1.37, 104) 3.53 (0.38, 9.13)
Glamser and
Marciani
[59]
18–24 Football College 59 4.75 (1.29, 17.3) 5.00 (1.37, 18.2) 4.00 (1.07, 14.8) 1.95 (0.4, 4.8)
18–24 Baseball College 26 1.75 (0.33, 9.02) 2.75 (0.56, 13.3) 1.00 (0.17, 5.82) 2.25 (0.25, 8.85)
18–24 Football College 49 12.0 (2.31, 62.2) 5.00 (0.9, 27.7) 6.50 (1.20, 35.0) 2.26 (0.36, 6.15)
18–24 Baseball College 20 1.50 (0.25, 8.81) 1.25 (0.20, 7.61) 1.25 (0.20, 7.61) 1.22 (0.21, 5.59)
Thompson et al.
[8]
4–6 Baseball T-ball beginners 335 1.47 (0.96, 2.27) 1.14 (0.73, 1.78) 1.23 (0.79, 1.91) 1.17 (0.68, 1.70)
7–9 Baseball Junior 894 0.99 (0.76, 1.29) 0.93 (0.72, 1.22) 0.90 (0.69, 1.17) 1.01 (0.79, 1.27)
10–12 Baseball Junior 1 235 1.13 (0.90, 1.41) 0.97 (0.78, 1.22) 0.95 (0.76, 1.19) 1.07 (0.82, 1.30)
13–15 Baseball Junior league 823 0.97 (0.74, 1.27) 0.81 (0.62, 1.07) 0.96 (0.73, 1.26) 0.91 (0.78, 1.15)
16–18 Baseball Youth league 127 1.40 (0.70, 2.82) 1.37 (0.68, 2.75) 0.92 (0.44, 1.92) 1.44 (0.54, 2.64)
10 Baseball Minor 321 1.00 (0.64, 1.53) 0.78 (0.50, 1.23) 0.98 (0.64, 1.52) 0.89 (0.68, 1.31)
10 Baseball Major 35 1.50 (0.41, 5.47) 1.00 (0.25, 3.88) 0.87 (0.21, 3.48) 1.33 (0.31, 4.21)
Continued next page
Relative Age Effects in Sport 243
ª2009 Adis Data Information BV. All rights reserved. Sports Med 2009; 39 (3)

This material is
the copyright of the
original publisher.
Unauthorised copying
and distribution
is prohibited.
Table I. Contd
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Thompson et al.
[8]
11 Baseball Minor 105 0.93 (0.44, 1.96) 0.61 (0.27, 1.34) 0.83 (0.39, 1.77) 0.84 (0.51, 1.63)
11 Baseball Major 342 1.62 (1.05, 2.48) 1.40 (0.91, 2.17) 1.15 (0.73, 1.79) 1.40 (0.69, 2.03)
12 Baseball Minor 49 0.40 (0.12, 1.25) 0.30 (0.08, 1.00) 0.75 (0.26, 2.11) 0.40 (0.36, 1.09)
12 Baseball Major 383 1.09 (0.73, 1.63) 1.11 (0.75, 1.66) 0.86 (0.57, 1.29) 1.18 (0.70, 1.68)
13 Baseball Senior 145 0.88 (0.45, 1.72) 0.88 (0.45, 1.72) 1.37 (0.72, 2.58) 0.74 (0.56, 1.31)
13 Baseball Junior 239 1.26 (0.77, 2.06) 0.75 (0.44, 1.27) 0.90 (0.54, 1.50) 1.06 (0.64, 1.64)
14 Baseball Senior 207 0.92 (0.54, 1.59) 0.89 (0.52, 1.53) 0.80 (0.46, 1.39) 1.00 (0.62, 1.61)
14 Baseball Junior 23 0.44 (0.08, 2.31) 0.33 (0.05, 1.90) 0.77 (0.17, 3.55) 0.43 (0.23, 1.87)
10–14 Baseball Lower level junior 827 0.90 (0.69, 1.18) 0.76 (0.57, 1.00) 0.96 (0.73, 1.25) 0.85 (0.78, 1.07)
10–14 Baseball High level junior 1 022 1.27 (0.99, 1.62) 1.07 (0.83, 1.37) 0.94 (0.73, 1.22) 1.20 (0.80, 1.48)
10–14 Baseball Tournament juniors 410 1.83 (1.22, 2.74) 1.82 (1.21, 2.72) 1.36 (0.90, 2.07) 1.54 (0.71, 2.16)
10–14 Baseball Recreation juniors 951 0.99 (0.77, 1.27) 0.81 (0.63, 1.05) 0.81 (0.63, 1.05) 0.99 (0.80, 1.24)
Verhulst
[10]
Senior Soccer Professional div 1 369 1.11 (0.74, 1.68) 1.28 (0.85, 1.92) 0.98 (0.65, 1.50) 1.20 (0.70, 1.72)
Senior Soccer Professional div 2 342 1.47 (0.96, 2.25) 1.22 (0.79, 1.89) 1.18 (0.76, 1.83) 1.23 (0.69, 1.78)
Senior Soccer Professional div 1 411 2.19 (1.47, 3.26) 1.52 (1.00, 2.29) 1.43 (0.94, 2.16) 1.52 (0.71, 2.13)
Senior Soccer Professional div 2 768 1.90 (1.43, 2.53) 1.22 (0.90, 1.65) 1.39 (1.03, 1.87) 1.30 (0.78, 1.67)
Senior Soccer Professional div 2 399 1.77 (1.19, 2.65) 1.55 (1.03, 2.33) 1.22 (0.8, 1.85) 1.50 (0.71, 2.11)
Senior Soccer Professional div 2 401 1.76 (1.18, 2.61) 1.45 (0.97, 2.17) 1.12 (0.73, 1.69) 1.51 (0.71, 2.13)
Boucher and
Mutimer
[9]
8–9 Ice hockey Junior novice 68 1.83 (0.69, 4.85) 1.50 (0.55, 4.04) 1.33 (0.48, 3.64) 1.42 (0.43, 3.26)
10–11 Ice hockey Atom 213 2.75 (1.56, 4.87) 1.96 (1.09, 3.53) 1.62 (0.89, 2.94) 1.80 (0.62, 2.89)
12–13 Ice hockey Pee wee 224 3.34 (1.84, 6.07) 3.13 (1.72, 5.69) 2.26 (1.22, 4.18) 1.98 (0.62, 3.15)
14–15 Ice hockey Bantam 302 3.10 (1.9, 5.06) 2.18 (1.32, 3.62) 1.86 (1.11, 3.10) 1.84 (0.67, 2.75)
16–17 Ice hockey Midget 144 3.60 (1.72, 7.51) 2.66 (1.25, 5.65) 2.33 (1.09, 4.99) 1.88 (0.56, 3.34)
Senior Ice hockey NHL professional 884 2.28 (1.72, 3.01) 2.09 (1.58, 2.77) 1.36 (1.01, 1.83) 1.85 (0.79, 2.33)
Dudink
[60]
Senior Soccer Professional premier 761 2.11 (1.59, 2.81) 1.39 (1.03, 1.88) 1.08 (0.79, 1.47) 1.68 (0.77, 2.16)
Senior Soccer Professional div 1 734 1.79 (1.34, 2.39) 1.14 (0.85, 1.55) 1.04 (0.77, 1.42) 1.43 (0.77, 1.85)
Senior Soccer Professional div 2 673 1.91 (1.41, 2.58) 1.28 (0.93, 1.75) 0.93 (0.67, 1.30) 1.64 (0.76, 2.14)
Senior Soccer Professional div 3 609 2.12 (1.53, 2.94) 1.65 (1.18, 2.31) 1.18 (0.83, 1.67) 1.73 (0.75, 2.28)
Edwards
[16]
Senior Cricket Professional county 292 NR NR NR 1.16 (0.67, 1.73)
Continued next page
244 Cobley et al.
ª2009 Adis Data Information BV. All rights reserved. Sports Med 2009; 39 (3)

This material is
the copyright of the
original publisher.
Unauthorised copying
and distribution
is prohibited.
Table I. Contd
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Baxter-Jones
[14]
11–17 Soccer Elite junior 65 7.40 (2.32, 23.5) 3.20 (0.94, 10.8) 1.40 (0.36, 5.33) 4.41 (0.39, 11.1)
11–17 Swimming Elite junior 54 2.85 (0.90, 8.97) 2.71 (0.86, 8.56) 1.14 (0.32, 4.04) 2.60 (0.38, 6.79)
9–17 Tennis Elite junior 74 3.54 (1.40, 8.97) 1.27 (0.45, 3.52) 0.90 (0.31, 2.65) 2.52 (0.44, 5.72)
Baxter-Jones et al.
[25]
9–18 Gymnastics Elite junior 38 1.25 (0.34, 4.55) 1.12 (0.30, 4.16) 1.37 (0.38, 4.94) 1.00 (0.33, 3.00)
Brewer et al.
[61]
16–17 Soccer Youth elite development 59 34 (4.09, 281) 12 (1.37, 104) 12 (1.37, 104) 3.53 (0.38, 9.13)
Senior Soccer Professional national 16 17 (0.71, 405)
a
11 (0.44, 272)
a
7 (0.27, 184)
a
4.33 (0.15, 28.1)
Stanaway and
Hines
[62]
Senior Baseball MLB professional 600 1.22 (0.88, 1.68) 1.25 (0.90, 1.72) 0.97 (0.69, 1.34) 1.25 (0.75, 1.65)
Senior Football Hall of fame 167 1.20 (0.64, 2.25) 1.58 (0.86, 2.91) 1.11 (0.59, 2.1) 1.31 (0.59, 2.23)
Helsen et al.
[30]
Senior Soccer Professional 408 1.78 (1.2, 2.64) 1.44 (0.96, 2.16) 1.13 (0.74, 1.71) 1.51 (0.71, 2.13)
10–16 Soccer Junior elite national 369 4.62 (2.92, 7.30) 2.37 (1.47, 3.84) 1.97 (1.20, 3.21) 2.35 (0.69, 3.39)
6–16 Soccer Youth elite club 485 2.62 (1.79, 3.82) 1.95 (1.32, 2.88) 1.77 (1.19, 2.62) 1.65 (0.73, 2.25)
6–10 Soccer Youth leagues 270 1.84 (1.13, 2.97) 1.46 (0.89, 2.39) 1.10 (0.66, 1.83) 1.57 (0.65, 2.38)
12–16 Soccer Junior leagues 226 1.16 (0.68, 1.96) 1.08 (0.63, 1.84) 1.28 (0.75, 2.15) 0.98 (0.63, 1.54)
Montelpare
et al.
[51]
Senior Ice hockey NHL professional 1 090 NA NA NA 1.78 (0.81, 2.19)
16–18 Ice hockey Junior national 231 NA NA NA 1.85 (0.64, 2.92)
19–26 Ice hockey College representative 2 047 NA NA NA 1.44 (0.86, 1.67)
Senior Ice hockey Amateur
representative
476 NA NA NA 1.78 (0.73, 2.45)
8–16 Ice hockey Junior and youth
representative
474 NA NA NA 1.49 (0.73, 2.05)
8–16 Ice hockey Junior minor 974 NA NA NA 1.17 (0.80, 1.46)
Musch and
Hay
[63]
Senior Soccer Professional 207 1.58 (0.91, 2.77) 1.38 (0.78, 2.43) 1.33 (0.75, 2.34) 1.27 (0.62, 2.04)
Senior Soccer Professional 61 1.76 (0.66, 4.72) 1.00 (0.35, 2.84) 0.92 (0.32, 2.65) 1.44 (0.41, 3.45)
Senior Soccer Professional 486 1.70 (1.18, 2.45) 1.49 (1.03, 2.16) 1.39 (0.95, 2.01) 1.33 (0.73, 1.82)
Senior Soccer Professional 355 1.31 (0.87, 1.98) 1.06 (0.69, 1.61) 0.95 (0.62, 1.45) 1.21 (0.69, 1.74)
Senior Soccer Professional 360 2.20 (1.44, 3.35) 1.78 (1.15, 2.74) 1.01 (0.64, 1.61) 1.97 (0.69, 2.84)
Helsen et al.
[64]
10–12 Soccer Junior representative 410 2.15 (1.45, 3.19) 1.57 (1.04, 2.35) 1.12 (0.73, 1.72) 1.75 (0.71, 2.46)
10–12 Soccer Junior representative 507 1.78 (1.25, 2.54) 1.13 (0.78, 1.65) 1.52 (1.06, 2.18) 1.15 (0.73, 1.56)
12–14 Soccer Junior representative 452 2.15 (1.45, 3.19) 1.95 (1.31, 2.90) 1.84 (1.23, 2.75) 1.44 (0.72, 1.98)
12–14 Soccer Junior representative 520 1.87 (1.33, 2.63) 1.02 (0.71, 1.48) 1.14 (0.79, 1.64) 1.35 (0.74, 1.82)
14–16 Soccer Youth representative 449 3.58 (2.41, 5.32) 1.98 (1.3, 2.99) 1.44 (0.94, 2.22) 2.27 (0.71, 3.16)
Continued next page
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Table I. Contd
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Helsen et al.
[64]
14–16 Soccer Youth representative 385 1.35 (0.95, 1.92) 1.03 (0.72, 1.49) 1.17 (0.82, 1.68) 1.09 (0.73, 1.49)
16–18 Soccer Youth representative 458 2.24 (1.55, 3.23) 1.26 (0.86, 1.87) 1.07 (0.72, 1.59) 1.69 (0.72, 2.33)
16–18 Soccer Youth representative 501 0.68 (0.47, 0.97) 0.66 (0.46, 0.94) 0.97 (0.69, 1.36) 0.68 (0.73, 0.92)
Hoare
[65]
15–16 Basketball Junior regional representative 130 9.14 (3.64, 22.9) 4.85 (1.88, 12.5) 3.57 (1.35, 9.41) 3.06 (0.53, 5.74)
15–16 Basketball Junior regional representative 113 5.22 (2.15, 12.6) 3.44 (1.39, 8.53) 2.88 (1.15, 7.24) 2.22 (0.51, 4.29)
17–18 Basketball Junior regional representative 118 3.25 (1.52, 6.93) 1.75 (0.78, 3.88) 1.37 (0.60, 3.12) 2.10 (0.52, 3.99)
Senior Basketball Professional 89 2.42 (1.03, 5.71) 1.78 (0.74, 4.30) 1.14 (0.45, 2.88) 1.96 (0.48, 4.09)
Grondin and
Koren
[66]
Senior Baseball MLB professional 5 033 1.12 (1.00, 1.25) 1.02 (0.91, 1.14) 0.97 (0.86, 1.08) 1.09 (0.90, 1.20)
Senior Baseball MLB professional 1 123 1.25 (0.99, 1.58) 1.16 (0.92, 1.47) 0.99 (0.78, 1.26) 1.21 (0.81, 1.48)
Senior Baseball MLB professional 1 260 1.17 (0.94, 1.46) 1.08 (0.87, 1.35) 0.95 (0.76, 1.19) 1.15 (0.82, 1.40)
Senior Baseball MLB professional 1 032 1.04 (0.82, 1.33) 1.01 (0.79, 1.28) 0.89 (0.70, 1.14) 1.08 (0.80, 1.34)
Senior Baseball MLB professional 1 000 0.98 (0.76, 1.25) 0.98 (0.77, 1.26) 0.88 (0.69, 1.13) 1.04 (0.80, 1.29)
Senior Baseball MLB professional 1 303 1.21 (0.97, 1.5) 1.08 (0.87, 1.35) 1.01 (0.81, 1.26) 1.13 (0.82, 1.37)
Senior Baseball MLB professional 1 514 1.31 (1.07, 1.61) 1.19 (0.97, 1.46) 1.02 (0.83, 1.26) 1.23 (0.83, 1.47)
Senior Baseball MLB professional 1 405 1.48 (1.19, 1.83) 1.37 (1.10, 1.69) 1.25 (1.01, 1.56) 1.26 (0.83, 1.51)
Senior Baseball Professional 744 2.39 (1.77, 3.23) 1.82 (1.34, 2.48) 1.47 (1.07, 2.02) 1.70 (0.77, 2.19)
Simmons and
Paull
[67]
15–16 Soccer Youth national developmental 79 12.2 (3.70, 40.4) 4.50 (1.28, 15.7) 2.00 (0.51, 7.73) 5.58 (0.41, 13.4)
9–16 Soccer Junior/youth representative 8 857 5.04 (4.59, 5.54) 2.84 (2.58, 3.13) 1.09 (0.98, 1.22) 3.76 (0.92, 4.06)
14–15 Soccer Junior national 78 16.6 (4.43, 62.6) 5.33 (1.33, 21.2) 3.00 (0.70, 12.7) 5.50 (0.41, 13.2)
15–16 Soccer Junior national 63 2.23 (0.85, 5.80) 0.61 (0.19, 1.89) 1.00 (0.35, 2.82) 1.42 (0.42, 3.36)
Musch
[68]
7–8 Soccer Junior leagues 4 795 0.97 (0.87, 1.09) 0.90 (0.81, 1.01) 0.86 (0.77, 0.97) 1.00 (0.90, 1.11)
9–10 Soccer Junior leagues 5 332 1.12 (1.01, 1.25) 0.96 (0.86, 1.07) 1.04 (0.93, 1.16) 1.02 (0.91, 1.12)
11–12 Soccer Junior leagues 5 417 1.14 (1.03, 1.27) 1.01 (0.91, 1.13) 1.00 (0.89, 1.11) 1.08 (0.91, 1.18)
13–14 Soccer Junior leagues 4 478 1.23 (1.09, 1.38) 1.13 (1.00, 1.27) 0.96 (0.85, 1.08) 1.20 (0.90, 1.33)
15–16 Soccer Junior leagues 3 266 1.24 (1.08, 1.42) 1.17 (1.02, 1.34) 1.02 (0.88, 1.17) 1.19 (0.88, 1.34)
17–18 Soccer Junior leagues 2 033 1.24 (1.04, 1.48) 1.12 (0.94, 1.34) 1.05 (0.88, 1.25) 1.15 (0.86, 1.34)
Glamser and
Vincent
[69]
17–18 Soccer Youth regional representative 147 3.00 (1.48, 6.05) 2.66 (1.31, 5.41) 1.50 (0.70, 3.18) 2.26 (0.56, 4.03)
O’Donoghue et al.
[17]
Senior Cricket Professional 120 1.37 (0.67, 2.78) 1.33 (0.65, 2.71) 0.74 (0.34, 1.59) 1.55 (0.53, 2.90)
Senior Cricket Professional 75 1.00 (0.41, 2.43) 0.90 (0.36, 2.22) 0.85 (0.34, 2.11) 1.02 (0.45, 2.24)
Senior Netball Professional 128 1.58 (0.80, 3.11) 0.86 (0.41, 1.78) 0.96 (0.47, 1.97) 1.24 (0.54, 2.27)
Senior Netball Professional 119 0.90 (0.43, 1.85) 0.80 (0.38, 1.67) 1.12 (0.55, 2.27) 0.80 (0.53, 1.49)
Continued next page
246 Cobley et al.
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Table I. Contd
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Edgar and
O’Donoghue
[70]
Senior Soccer Professional 345 1.40 (0.92, 2.14) 1.01 (0.65, 1.57) 1.24 (0.81, 1.90) 1.07 (0.69, 1.55)
Senior Soccer Professional 92 1.13 (0.50, 2.56) 0.90 (0.39, 2.09) 1.13 (0.50, 2.56) 0.95 (0.49, 1.94)
Senior Soccer Professional 69 1.21 (0.49, 2.98) 0.73 (0.28, 1.92) 0.68 (0.25, 1.80) 1.15 (0.44, 2.62)
Senior Soccer Professional 92 0.85 (0.36, 2.01) 1.23 (0.54, 2.79) 1.28 (0.57, 2.89) 0.91 (0.49, 1.86)
Senior Soccer Professional 598 1.25 (0.90, 1.72) 0.99 (0.71, 1.37) 1.15 (0.83, 1.59) 1.04 (0.75, 1.37)
Senior Soccer Professional 115 1.03 (0.50, 2.11) 0.45 (0.19, 1.02) 1.22 (0.60, 2.47) 0.66 (0.52, 1.26)
Senior Soccer Professional 23 0.12 (0.01, 1.34) 0.87 (0.18, 4.07) 0.87 (0.18, 4.07) 0.53 (0.23, 2.25)
Senior Soccer Professional 138 0.84 (0.43, 1.63) 0.53 (0.26, 1.09) 1.15 (0.60, 2.18) 0.64 (0.55, 1.15)
Senior Soccer Professional 736 1.16 (0.87, 1.54) 0.89 (0.66, 1.19) 1.15 (0.86, 1.53) 0.95 (0.77, 1.22)
Abernethy and
Farrow
[15]b
Senior Australian
Football
Professional 627 1.44 (1.05, 1.96) 1.26 (0.92, 1.74) 0.97 (0.69, 1.34) 1.37 (0.76, 1.80)
Senior Rugby union Professional 74 2.16 (0.84, 5.54) 1.66 (0.63, 4.36) 1.33 (0.49, 3.58) 1.64 (0.45, 3.64)
Senior Rugby union National professional 37 1.42 (0.37, 5.39) 1.71 (0.46, 6.31) 1.14 (0.29, 4.46) 1.46 (0.32, 4.50)
Senior Rugby league Professional 418 2.39 (1.60, 3.56) 1.90 (1.26, 2.86) 1.23 (0.80, 1.89) 1.92 (0.71, 2.69)
Senior Rugby league National professional 92 2.46 (1.07, 5.67) 1.53 (0.64, 3.66) 1.13 (0.45, 2.79) 1.87 (0.48, 3.85)
Senior Cricket Professional 151 1.13 (0.60, 2.11) 1.00 (0.52, 1.89) 0.84 (0.43, 1.61) 1.15 (0.57, 2.01)
Senior Cricket National professional 385 2.33 (0.63, 8.60) 1.83 (0.48, 6.95) 1.33 (0.33, 5.30) 1.78 (0.33, 5.37)
Senior Basketball Professional 94 1.94 (0.86, 4.35) 0.94 (0.39, 2.26) 1.33 (0.57, 3.07) 1.23 (0.49, 2.49)
Senior Basketball National professional 18 2.66 (0.41, 17.1) 1.33 (0.18, 9.72) 1.00 (0.12, 7.89) 2.00 (0.19, 10.2)
Edgar and
O’Donoghue
[13]
Senior Tennis ATP professional 237 1.65 (0.97, 2.81) 1.68 (0.99, 2.85) 1.46 (0.85, 2.50) 1.35 (0.64, 2.10)
14–18 Tennis ITF national juniors 237 2.19 (1.28, 3.74) 1.97 (1.15, 3.38) 1.41 (0.81, 2.47) 1.72 (0.63, 2.69)
Helsen et al.
[12]
14–18 Soccer Youth national 99 1.68 (0.79, 3.54) 1.45 (0.68, 3.09) 0.90 (0.40, 2.02) 1.64 (0.52, 3.15)
14–18 Soccer Youth national 90 4.12 (1.56, 10.8) 3.62 (1.36, 9.62) 2.50 (0.91, 6.84) 2.21 (0.47, 4.61)
14–18 Soccer Youth national 94 2.93 (1.31, 6.57) 0.81 (0.32, 2.05) 1.12 (0.46, 2.72) 1.76 (0.49, 3.58)
14–18 Soccer Youth national 41 3.00 (0.84, 10.6) 2.16 (0.59, 7.93) 0.66 (0.14, 3.08) 3.10 (0.32, 9.51)
14–18 Soccer Youth national 77 12.0 (3.15, 45.6) 6.0 (1.51, 23.7) 6.66 (1.69, 26.1) 2.34 (0.45, 5.21)
14–18 Soccer Youth national 50 3.60 (1.01, 12.7) 4.4 (1.26, 15.3) 1.00 (0.23, 4.33) 4.00 (0.35, 11.3)
14–18 Soccer Youth national 36 17.0 (1.84, 156) 11.0 (1.16, 103) 7.00 (0.70, 69.1) 3.50 (0.29, 11.7)
14–18 Soccer Youth national 101 3.07 (1.33, 7.08) 1.61 (0.66, 3.91) 2.07 (0.87, 4.91) 1.52 (0.50, 3.01)
14–18 Soccer Youth national 72 6.6 (2.09, 20.7) 5.00 (1.56, 15.9) 1.80 (0.50, 6.43) 4.14 (0.41, 9.94)
15–16 Soccer Youth national 288 6.4 (3.67, 11.1) 3.22 (1.80, 5.75) 2.45 (1.35, 4.44) 2.78 (0.65, 4.24)
Continued next page
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Table I. Contd
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Helsen et al.
[12]
17–18 Soccer Youth national 144 1.65 (0.84, 3.23) 1.69 (0.86, 3.30) 1.19 (0.59, 2.39) 1.52 (0.56, 2.69)
19–21 Soccer Youth national 159 1.07 (0.58, 1.97) 0.85 (0.45, 1.60) 0.95 (0.51, 1.76) 0.98 (0.58, 1.69)
11–14 Soccer Junior club tournament 677 2.01 (1.47, 2.76) 1.71 (1.24, 2.35) 1.47 (1.06, 2.04) 1.50 (0.76, 1.96)
Vaeyens et al.
[71]
Senior Soccer Professional 1 930 1.44 (1.20, 1.73) 1.40 (1.17, 1.68) 1.20 (0.99, 1.44) 1.29 (0.85, 1.51)
Senior Soccer Professional 827 1.45 (1.10, 1.91) 1.16 (0.87, 1.54) 1.21 (0.91, 1.60) 1.18 (0.78, 1.49)
Vincent and
Glamser
[72]
16–17 Soccer Developmental national 24 3.25 (0.66, 15.9) 0.50 (0.06, 3.84) 1.25 (0.22, 7.08) 1.66 (0.24, 6.76)
Esteva and
Drobnic
[73]
Youth Basketball Youth representative 157 9.75 (4.16, 22.8) 6.62 (2.78, 15.7) 2.25 (0.87, 5.77) 5.03 (0.54, 9.27)
Senior Basketball Professional 404 1.42 (0.95, 2.12) 1.62 (1.09, 2.41) 1.12 (0.74, 1.70) 1.43 (0.71, 2.01)
Senior Basketball NBA Professional 382 0.98 (0.66, 1.47) 0.93 (0.62, 1.4) 0.92 (0.62, 1.38) 1.00 (0.70, 1.41)
Co
ˆte
´et al.
[18]
Senior Ice hockey NHL professional 151 1.79 (0.94, 3.40) 1.13 (0.58, 2.22) 1.27 (0.65, 2.47) 1.28 (0.57, 2.24)
Senior Basketball NBA professional 436 1.21 (0.83, 1.77) 1.05 (0.71, 1.54) 1.22 (0.84, 1.78) 1.01 (0.72, 1.40)
Senior Baseball MLB professional 907 1.38 (1.06, 1.78) 1.15 (0.88, 1.50) 1.00 (0.76, 1.30) 1.26 (0.79, 1.58)
Senior Golf PGA professional 197 1.12 (0.64, 1.94) 0.91 (0.52, 1.61) 0.97 (0.55, 1.71) 1.03 (0.61, 1.67)
Senior Ice hockey NHL professional 549 1.54 (1.09, 2.16) 1.56 (1.11, 2.19) 1.12 (0.78, 1.60) 1.46 (0.74, 1.95)
Wattie et al.
[42]c
Senior Ice hockey NHL professional 146 1.07 (0.57, 2.03) 0.97 (0.51, 1.85) 0.78 (0.4, 1.53) 1.14 (0.56, 2.01)
Senior Ice hockey NHL professional 206 1.32 (0.76, 2.28) 1.13 (0.64, 1.96) 1.02 (0.58, 1.79) 1.21 (0.62, 1.95)
Senior Ice hockey NHL professional 252 1.14 (0.69, 1.90) 1.20 (0.72, 1.98) 1.31 (0.79, 2.16) 1.01 (0.65, 1.55)
Senior Ice hockey NHL professional 282 1.23 (0.77, 1.97) 1.17 (0.73, 1.88) 1.06 (0.66, 1.71) 1.16 (0.66, 1.75)
Senior Ice hockey NHL professional 284 0.81 (0.51, 1.28) 0.77 (0.49, 1.23) 0.70 (0.44, 1.12) 0.93 (0.66, 1.39)
Senior Ice hockey NHL professional 423 0.76 (0.51, 1.11) 0.87 (0.59, 1.27) 0.98 (0.67, 1.42) 0.82 (0.71, 1.14)
Senior Ice hockey NHL professional 698 1.50 (1.11, 2.03) 1.34 (0.99, 1.82) 1.20 (0.88, 1.63) 1.29 (0.77, 1.67)
Senior Ice hockey NHL professional 798 1.90 (1.43, 2.54) 1.79 (1.34, 2.40) 1.20 (0.88, 1.62) 1.68 (0.78, 2.14)
Senior Ice hockey NHL professional 600 1.55 (1.12, 2.15) 1.47 (1.06, 2.04) 1.05 (0.74, 1.47) 1.47 (0.75, 1.95)
Senior Ice hockey NHL professional 76 0.76 (0.30, 1.89) 1.33 (0.56, 3.12) 0.52 (0.19, 1.37) 1.37 (0.45, 3.00)
Summary effect size 121 159
d
1.65 (1.54, 1.77) NA NA 1.39 (1.32, 1.47)
a 0.5 added to raw data as Q4 =0, preventing OR calculation. Procedure recommended by Sutton et al.
[48]
b Authors also reported data for soccer at junior national (16–17 years old), developmental national (21–23 years old) and national professional (senior) levels. However, sample
totals were not available for OR calculation.
c Figure excludes total sample numbers from Edwards
[16]
and Montelpare et al.
[51]
d At the time of data collection this paper was in press and accepted for publication, but not published until June 2007.
AA, AAA, BB, CC =levels of ice hockey competition, where ‘As’ are more competitive or a higher level than subsequent letters of the alphabet; AFC =American Football Conference;
ATP =Association of Tennis Professionals; CFL =Canadian Football League; div =division; MLB =Major League Baseball; NBA =National Basketball Association; NFC =National
Football Conference; NR =sample information not reported; NA =quartile data not available following contact with lead author; original data presented in bi-monthly distributions;
NHL =National Hockey League; OHL =Ontario Hockey League; PGA =Professional Golfers’ Association; Q=quartile; WHL =Western Hockey League.
248 Cobley et al.
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information hopefully beneficial to strategies
motivated toward eradicating RAEs. Findings
suggest RAEs are robust and generally prevalent
across the sports contexts examined to date.
Across all samples, summary ORs indicate
that for every two participants born in the last
quartile of an annual age-group, over three
are participating from the first quartile of the
same age-group. Risk likelihoods increased when
the number of months away from the referent
group (i.e. quartile 4) was amplified, suggesting
a linear profile to RAEs. Findings identified that
the relatively youngest sport participants within
annual age-groups were (i) less likely to partici-
pate in recreational and competitive sport from
under 14 years of age; (ii) certainly less likely
to participate on representative teams during the
15- to 18-year-old bracket; and (iii) less likely to
become an elite athlete in the sport contexts
examined. In combining previous literature (e.g.
Helsen et al.
[30]
) with findings from the present
study, it seems that sport is less likely to be an
activity or career pathway for relatively younger
individuals, whose birth dates coincide with
the last 3 months of an annual age-grouping
strategy.
Several factors influenced the magnitude of
RAEs, notably age category, skill level and sport
context. Analyses identified sport contexts with
distinctive RAE risks; higher risks were asso-
ciated with basketball, soccer and ice hockey.
In these sports, mid to late adolescence (15–18
years) and the representative level of competition
(i.e. regional and national representation) were
most vulnerable to RAEs. In contrast, while
small significant effects remained, childhood
(under 11 years) and recreational sports contexts
reported the lowest risk of RAEs. In the seven
available American Football samples, no evidence
of RAEs was found. Related to these contexts,
low numbers of samples were available, so find-
ings should be evaluated with caution.
Taken together, findings partially reinforced
our hypothesis that RAEs are probably most
likely to occur in highly popular sports, prevail-
ing due to a combination of mechanisms pri-
marily associated with maturation and selection
of athletes within the developmental tiers and
structures of a sport.
[3,19,30]
Contrary to our hy-
potheses, RAE risk did not increase linearly with
skill level or age category. Rather, at the elite level
(professional or senior national representative)
risks decreased to below that of the youth re-
presentative. At senior ages (i.e. >18 years) RAE
risk also decreased to below that of the adolescent
ages. Nevertheless, RAEs persisted into older
cohorts.
The reduction of RAEs at the senior and
elite stages is difficult to explain, with several
mechanisms possible. For example, whilst ac-
knowledging that annual age-groupings within
sport generally terminate in senior sport (i.e. of-
ten 19–21 years old), it could be that differences
according to physical maturity become re-
dundant at the senior years,
[26]
allowing the rela-
tively younger athlete to perform on a more equal
footing. Nevertheless, such an explanation is re-
liant upon relatively younger athletes remaining
actively engaged in sport through years of un-
favourable selection and attainment. It is worth
reminding that Helsen et al.
[30]
and Barnsley
et al.
[11]
reported higher drop-out rates in relatively
younger players across junior and adolescent
ages. Another possibility is that senior athletes
transfer from one sport to another (even from
lower levels of involvement and in contexts where
RAEs are more or less likely), thereby avoiding
the disadvantaged developmental environment.
In elite team sports, Baker et al.
[74]
noted that
elite athlete development profiles were highly
variable, suggesting that this type of late-stage
transfer is possible, depending on the compat-
ibility of performance requirements.
An alternative explanation is that relatively
older athletes, originally selected for additional
training and higher levels of skill representation
during their junior and adolescent years, with-
draw from competitive levels of participation
preceding or during their senior years due to in-
jury, overtraining, burnout or boredom. In pop-
ular highly competitive sports (e.g. soccer and ice
hockey), many talented athletes in the adolescent
years (15–18) do not fulfil their early potential by
attaining a professional contract. Some limited
evidence suggests that highly specialized training
environments, such as those conducive to RAE
Relative Age Effects in Sport 249
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Table II. Unadjusted odds ratios (ORs) for female independent samples examining relative age effect in sport
Study Subject
age (y)
Sport Level of competition No. of
subjects
OR comparisons [Q1–4/1st and 2nd 6 mo)] (95%CI)
Q1 vs Q4 Q2 vs Q4 Q3 vs Q4 1st vs 2nd
Grondin et al.
[2]
12–13 Volleyball Junior 96 1.56 (0.67, 3.63) 2.37 (1.05, 5.35) 1.06 (0.43, 2.57) 1.9 (0.49, 3.86)
14–15 Volleyball Youth cadet 97 1.00 (0.46, 2.15) 1.1 (0.51, 2.36) 0.35 (0.14, 0.89) 1.55 (0.49, 3.11)
16–17 Volleyball Youth juvenile 56 1.14 (0.4, 3.2) 1.00 (0.35, 2.85) 0.85 (0.29, 2.49) 1.15 (0.4, 2.86)
14–15 Volleyball Provincial youth cadet 219 2.28 (1.3, 3.99) 2.12 (1.21, 3.73) 1.43 (0.79, 2.58) 1.8 (0.62, 2.87)
16–17 Volleyball Provincial youth
juvenile
188 1.25 (0.7, 2.25) 1.43 (0.8, 2.55) 1.12 (0.62, 2.03) 1.26 (0.6, 2.07)
17–19 Volleyball Provincial youth 59 1.06 (0.39, 2.86) 0.81 (0.29, 2.27) 0.81 (0.29, 2.27) 1.03 (0.41, 2.5)
Senior Volleyball Provincial senior 40 0.87 (0.22, 3.34) 1.62 (0.46, 5.62) 1.5 (0.42, 5.24) 1.00 (0.34, 2.92)
Baxter-Jones
[14]
11–18 Swimming Elite junior 60 2.11 (0.72, 6.14) 2.11(0.72, 6.14) 1.44 (0.47, 4.38) 1.72 (0.41, 4.19)
9–18 Tennis Elite junior 81 2.23 CI (0.9, 5.47) 1.84 CI (0.74, 4.6) 1.15 CI (0.43, 3.02) 1.89 CI (0.46, 4.07)
Baxter-Jones
et al.
[25]
9–18 Gymnastics Elite junior 81 1.64 (0.69, 3.89) 1.23 (0.5, 3) 0.88 (0.34, 2.23) 1.53 (0.46, 3.27)
Hoare
[65]
15–16 Basketball Junior regional
representative
130 5.72 (2.56, 12.7) 3.81 (1.67, 8.69) 1.27 (0.5, 3.21) 4.2 (0.52, 8.07)
15–16 Basketball Junior regional
representative
100 6.71 (2.54, 17.6) 3.42 (1.25, 9.39) 3.14 (1.13, 8.67) 2.44 (0.49, 4.94)
17–18 Basketball Junior regional
representative
98 1.77 (0.79, 3.97) 1.27 (0.55, 2.93) 1.38 (0.6, 3.16) 1.27 (0.5, 2.54)
Senior Basketball Professional 78 3.00 (1.15, 7.77) 1.8 (0.66, 4.87) 2.00 (0.74, 5.35) 1.6 (0.46, 3.47)
O’Donoghue
et al.
[17]
Senior Netball National professional 128 1.58 (0.8, 3.11) 0.86 (0.41, 1.78) 0.96 (0.47, 1.97) 1.24 (0.54, 2.27)
Senior Netball National professional 119 0.9 (0.43, 1.85) 0.8 (0.38, 1.67) 1.12 (0.55, 2.27) 0.8 CI (0.53, 1.49)
Edgar and
O’Donoghue
[13]
Senior Tennis ATP professional 211 1.94 (1.11, 3.38) 1.61 (0.91, 2.83) 1.3 (0.73, 2.32) 1.54 (0.62, 2.47)
14–18 Tennis ITF national juniors 239 1.85 (1.09, 3.13) 1.47 (0.86, 2.52) 1.65 (0.96, 2.8) 1.25 (0.64, 1.94)
Helsen et al.
[12]
17–18 Soccer Youth national 72 1.61 (0.62, 4.18) 2.00 (0.78, 5.08) 0.92 (0.33, 2.56) 1.88 (0.44, 4.24)
Vincent and
Glamser
[72]
17–18 Soccer State representative 804 1.11 (0.84, 1.47) 1.14 (0.86, 1.51) 1.10 (0.83, 1.45) 1.07 (0.78, 1.36)
17–18 Soccer Regional representative 71 1.33 (0.52, 3.4) 1.53 (0.6, 3.86) 0.86 (0.32, 2.33) 1.53 (0.44, 3.45)
17–19 Soccer Developmental national 39 3.00 (0.78, 11.5) 1.40 (0.32, 5.97) 2.40 (0.6, 9.44) 1.29 (0.33, 3.84)
Wattie et al.
[42]
Senior Ice hockey Senior women 299 1.04 (0.65, 1.65) 1.29 (0.82, 2.03) 1.05 (0.66, 1.67) 1.13 (0.67, 1.68)
Summary
effect size
3321 1.21 (1.10, 1.33) 1.39 (1.26, 1.54)
ATP =Association of Tennis Professionals; ITF =International Tennis Federation; Q=quartile.
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occurrence (i.e. through selection and identifica-
tion processes), are related to shorter playing
careers and increased rates of dropout at the
senior level.
[75]
On the whole, we can only spec-
ulate as to why RAEs decline at the elite stage.
Nonetheless, these results demonstrate that a
slightly ‘more even playing-field’ exists for those
relatively younger individuals within senior and
elite echelons of sport.
4.2 Context-Specific Findings
Meta- and substratification analyses were less
able to accurately account for the potential role
of context specificity, whereby unique socio-
cultural variables could amplify or reduce RAEs.
For example, soccer and ice hockey show con-
sistent RAEs, regardless of age and skill level.
However, identifying basketball as a context
for heightened risk of RAEs contradicts the
equivocal findings of some individual studies
(e.g. Daniel and Janssen
[41]
). Upon further
examination of basketball samples, over half of
the samples (i.e. eight) included in our OR cal-
culations were derived from the data of Hoare
[65]
examining Australian Basketball. In this context,
RAEs were exceptionally high, lending credence
to the suggestion that factors distinctive to the
developmental structure of Australian Basketball
may escalate RAEs. Likewise, factors distinct to
the developmental structure of American football
(e.g. drafting and selection at later ages) may also
reduce the likelihood of RAEs (as argued by
Daniel and Janssen
[41]
). These context-specific
findings suggest RAE risk is variable and that
those responsible for sport structures can modify
and potentially eradicate RAE inequalities.
4.3 Eliminating Relative Age Effects
Several recommendations have been proposed
to resolve RAEs. Initially these addressed annual
age-groupings, by advocating a change in the
age-group cut-off date (e.g. from January to
June), rotating cut-off dates from year to year
(Barnsley et al.
[1]
), or altering age-grouping
bandwidths. However, changing cut-off dates
only leads to a transfer of RAEs,
[71]
as ex-
emplified in Australian,
[63]
Belgian
[64]
and Eng-
lish
[67]
youth soccer. To prevent a ‘fixed-bias’
across sport development, Grondin et al.
[2]
pro-
posed an expansion of age-group bandwidths to
15 and 21 months, as opposed to the typical
12-month groupings, to rotate cut-off dates across
particular ages and constantly change group
composition. Similarly, Boucher and Halliwell
[57]
proposed a 9-month bandwidth (referred to as
the Novem system) to reduce potential age in-
equalities in a given group, whilst also ensuring
that the same participants (i.e. relatively older or
younger) were not disadvantaged year after year
during youth stages of competition (i.e. present
Under 10s to Under 16s). To address the RAE
inequality in Canadian ice hockey specifically,
Hurley et al.
[76]
presented the relative age fair
(RAF) cycle, whereby cut-off dates altered for
each and every consecutive year of participation.
In their plan, cut-off dates changed by 3 months
between seasons of competition, to ensure players
experienced being in each quartile position
Table III. Summary odds ratios (ORs) for relative age effects in sport according to age category
a
Age category Q1 vs Q4 1st vs 2nd 6 mo
no. of samples
(%of total)
summary OR
(95%CI)
no. of samples
(%of total)
summary OR
(95%CI)
£10 y 17 (6.91) 1.22 (1.08, 1.39) 17 (6.71) 1.12 (1.03, 1.22)
Junior (11–14 y) 42 (17.07) 1.29 (1.29, 1.96) 44 (17.39) 1.36 (1.15, 1.60)
Adolescent (15–18 y) 69 (28.04) 2.36 (2.00, 2.79) 70 (27.66) 1.72 (1.54, 1.92)
Senior (‡19 y) 107 (43.49) 1.44 (1.35, 1.53) 110 (43.47) 1.29 (1.24, 1.35)
a 11 samples from the Q1 vs Q4 comparison and 12 samples from the 1st vs 2nd 6-mo comparisons were excluded from the analysis due to
participant samples crossing age-category boundaries applied.
Q=quartile.
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(i.e. Q1, Q2, Q3 and Q4) across the competitive
junior structure of ice hockey. Both the Novem
and RAF strategy may help address the RAE
problem; however, there is foreseeable complex-
ity in re-structuring and implementing these op-
tions within youth sports.
On a separate but related point, Musch and
Grondin
[21]
noted that across sports contexts
(and education) many cut-off dates used for age-
grouping are actually similar (e.g. 1 September to
31 August in the UK). So, to prevent repeated
and consistent (dis)advantages from occurring,
they recommended that deliberate variation of
cut-off dates be used for across sports contexts.
This step would prevent generic RAEs across
sports contexts and may reduce the likelihood of
persistent negative experiences of sporting in-
volvement relative to age-matched peers, and
may help maintain sporting involvement in con-
texts with favourable conditions (i.e. in which
you are relatively older). Nevertheless, it may not
prevent RAEs within a given sport, thereby not
preventing their occurrence.
Other possible solutions have targeted ma-
turational differences and the process by which
athletes are selected. Barnsley and Thompson
[3]
advocated implementing player quotas, where
selection must meet specified birth-date distribu-
tions to prevent favouring of relatively older
players. More substantially, the average age of a
whole team,
[30,64]
the number of selections and
the distribution of playing time could be regu-
lated. Another popular solution has been to sug-
gest grouping participants according to physical
(i.e. height and weight) classification,
[14,21]
similar
to that routinely adopted in boxing and wrestling.
More sensitive to individual variability in physi-
cal characteristics, this may be sensible particu-
larly during developmental stages. Again, these
strategies may prove difficult to integrate into
sport systems and are as yet unproven in their
value for resolving RAEs.
A less challenging solution is to delay the
processes of selection, identification and re-
presentation beyond stages of puberty and ma-
turation (i.e. 15–16 years of age). Governing
bodies and coaches should reconsider the ne-
cessity for early selection, intensive training and
levels of representation at junior and child ages.
Admittedly, the path to success in sport does
require intensive long-term training and commit-
ment, often referred to as the ‘10-year rule of
attainment’.
[77]
Yet, peak performance in many
sports (e.g. soccer, ice hockey) is often not
attained until the late twenties and thirties, pro-
viding a sufficient window for training and
development subsequent to adolescence. This po-
sition is further substantiated by an expanding
literature illustrating concerns for the physiolo-
gical and psychosocial welfare of athletes in-
volved in intensive training from early ages.
[78-80]
Delaying selection might reduce RAEs and in-
directly help reduce the risk of compromising
health during an athlete’s development.
Another possibly beneficial approach would
be to raise awareness of RAEs among those re-
sponsible for the infrastructure and coordination
of youth sport. Sports contexts with higher risks
of RAEs (e.g. Canadian ice hockey and European
soccer) should be targeted. During adolescence,
coaches need to be attentive to the possibility that
physical attributes, such as height and weight
Table IV. Summary odd ratios (ORs) for relative age effects according to skill level
a
Skill level Q1 vs Q4 1st vs 2nd 6 mo
no. of samples (%of total) summary OR (95%CI) no. of samples (%of total) summary OR (95%CI)
Recreational 28 (11.38) 1.12 (1.05, 1.20) 28 (11.06) 1.09 (1.03, 1.15)
Competitive 53 (21.54) 1.63 (1.35, 1.97) 55 (21.73) 1.40 (1.21, 1.62)
Representative 70 (28.45) 2.77 (2.36, 3.24) 73 (28.85) 1.87 (1.68, 2.07)
Elite 95 (38.61) 1.42 (1.34, 1.51) 97 (38.33) 1.28 (1.22, 1.33)
a Samples that could not be clearly categorized into one of the above were excluded from comparison analyses.
Q=quartile.
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(which underpin speed, power and strength) are
being overlooked during early stages of athlete
development (i.e. 13–16 years old), conveying
selection advantages to the relatively older at a
time coinciding with intense identification and
selection to competition at representative levels.
Whilst monitoring for RAEs in selection and
participation, coaches should integrate more
movement and skill-based (e.g. movement accu-
racy, consistency and adaptability) criteria in se-
lection, reducing the association and dependence
upon physical attributes. Up to the mid-teenage
years (i.e. 15–16), governing bodies and coaches
should re-assess whether tiers of competition and
levels of representation could be removed to
support selection and representation in later
years.
To assist, it should be considered that coaches
within sport development systems are pressured
to obtain immediate performance success. As a
result, coaches may face a constant battle of
selecting individuals/teams that help guarantee
immediate success in youth ages (i.e. likely to be
relatively older with advanced physical char-
acteristics at the moment), as opposed to
individuals/teams that may be more successful in
the longer term. Strategies focusing on raising
awareness with recommendations pertaining to
the importance of delayed selection may help re-
duce the emphasis on striving for immediate
performance success in youth. Considerate of the
findings from the present study, it is certainly
feasible that if a relatively younger athlete main-
tains sport involvement, despite the constant and
disadvantaging annual age-grouping policy in
youth, this may prove beneficial in the senior
years.
4.4 Future Directions
This meta-analytical review has identified
several areas where further research is needed.
Data consistently and recurrently support the
presence of RAEs in specific sports, yet in other
contexts the data are less conclusive. For
example, data from basketball are somewhat
equivocal, while data from women’s sports are
particularly sparse. Initial data in women’s sports
(e.g. Wattie et al.
[42]
) suggests these contexts may
not be as susceptible to RAEs. One explanation
for the discrepancy associates differences in par-
ticipation rates and lower competition for selec-
tion into representative skill levels as reasons for
a reduced likelihood of RAE risk. Researchers
should also more broadly consider a range of
sociocultural contexts. Largely limited by studies
in North America, Europe and Australia to
date, examinations of RAEs in African, South
American and Asian countries might provide
valuable information about the role of sport infra-
structure in perpetuating RAEs.
Similarly, a comparison of RAEs between
sport development systems utilizing early talent
identification systems (e.g. Australia) and those
without explicit programmes may be useful. This
would determine whether RAEs are the result of
greater exposure to high-quality resources or due
to the effects of early success on the development
of self-efficacy and other feelings of competence.
Moreover, this research might prove useful for
Table V. Summary odds ratios (ORs) for relative age effects according to sport context
a
Sport context Q1 vs Q4 1st vs 2nd 6 mo
no. of samples (%of total) summary OR (95%CI) no. of samples (%of total) summary OR (95%CI)
Ice hockey 77 (31.30) 1.62 (1.45, 1.79) 83 (32.80) 1.40 (1.31, 149)
Soccer 76 (30.89) 2.01 (1.73, 2.32) 76 (30.03) 1.55 (1.37, 1.74)
Baseball 33 (13.41) 1.20 (1.12, 1.30) 33 (13.04) 1.14 (1.08, 1.20)
Basketball 15 (6.09) 2.66 (1.80, 3.93) 15 (5.92) 1.77 (1.34, 2.33)
Volleyball 14 (5.69) 1.33 (1.07, 1.65) 14 (5.53) 1.24 (1.03, 1.49)
American Football 7 (2.84) 1.24 (0.93, 1.65) 7 (2.76) 1.08 (0.94, 1.23)
a Samples examining other sport contexts (e.g. tennis) were not included in any Q1 vs Q4 or 1st vs 2nd 6 mo comparison analyses.
Q=quartile.
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determining the effectiveness of talent identifica-
tion systems, since one measure of utility is cer-
tainly the degree to which ‘talented’ or ‘gifted’
performers are missed by the system (a type II
error). For example, countries and/or sport-
governing bodies employing intensive early talent
identification and development systems in sport
may, ironically, be achieving the opposite effect
by constraining and reducing their talent pool
through early selection processes and generating
RAEs. In this scenario, young athletes may de-
part sport prior to full maturity, without oppor-
tunity to nurture their skills and inherent interest.
Qualitative idiographic investigations examining
developmental sport structures, coaching prac-
tice and the child/athlete experience within them,
will certainly strengthen our understanding of
how RAEs manifest and operate.
Atheoretical work has dominated the study of
RAEs to date and future studies should be
grounded in more theoretically sound founda-
tions. In addition to providing pieces to the
puzzle that are currently missing, such studies
would be valuable for creating a sound theoreti-
cal understanding of (i) the origins of RAEs,
(ii) their implications in human development, and
(iii) ways in which development systems can be
modified to reduce or remove RAEs in the future.
5. Conclusions
This meta-analysis suggests consistent small
risks of RAEs are apparent across sport, with the
relatively younger members of annual age-group
cohorts persistently disadvantaged. Risk size is
moderated by several factors, including chrono-
logical age differences (i.e. number of months)
between cohort members, age category, skill level
and sport context. Practices that produce RAEs
need to be revised, whilst interventions that re-
duce or eradicate this sporting inequality need
to be implemented and evaluated. These steps
are necessary as annual age-grouping and asso-
ciated processes appear to constrain the like-
lihood of immediate and long-term participation
as well as attainment in sport. Whether you are
motivated toward realizing the positive effects of
sport on youth development (e.g. promoting fun,
enjoyment and inclusive participation), or are
interested in elite athlete development, the pre-
sence of RAEs (we argue) appears contradictive
to both these outcomes. Certainly, notions of com-
petition, selection and talent identification, which
seem to create RAEs across developmental stages,
should be addressed by sport organizations. This
is the main challenge facing researchers, sport
governing bodies, coaches, parents and athletes
alike.
Acknowledgement
No funding was received for this review, and the authors
have no conflicts of interest that are directly relevant to the
content of this review.
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Correspondence: Dr Stephen Cobley, Room 124, Fairfax Hall,
Carnegie Faculty of Sport and Education, Headingley
Campus, Leeds Metropolitan University, West Yorkshire,
LS6 3QS, UK.
E-mail: s.cobley@leedsmet.ac.uk
256 Cobley et al.
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