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The relative age effect (RAE) is a common phenomenon observed in youth sports and is characterized by a significant over-representation of athletes born close to the date of selection. However, there is a lack of research on RAE in world-class track and field athletes and it is not clear if this effect persists into adulthood. Thus, this study examined for the first time the prevalence and magnitude of RAE at world class level in all track and field disciplines. Birthdates of 39,590 athletes (51.6% females) ranked in the International Association of Athletics Federations top 100 official lists between 2007 and 2018 season of Under 18, Under 20, and Senior categories were collected. Under 18 and Under 20 athletes born in the first week of the year are about 2 to 3.5 times more likely to be included in the top-100 ranking than the athletes born in the last week of the year. RAE was overall larger in male compared to female athletes. In some disciplines (e.g., throwing events) RAE persists in Senior category. These findings suggest that in some disciplines relatively younger athletes may have less chances of reaching world-class performances even in the adulthood. Governing bodies should reflect upon their policies for athlete support and selection to minimize the RAE.
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published: 18 June 2019
doi: 10.3389/fpsyg.2019.01395
Frontiers in Psychology | 1June 2019 | Volume 10 | Article 1395
Edited by:
Miguel-Angel Gomez-Ruano,
Polytechnic University of
Madrid, Spain
Reviewed by:
Jorge Lorenzo Calvo,
Polytechnic University of Madrid,
Michael Romann,
Swiss Federal Institute of Sport
Magglingen SFISM, Switzerland
Corrado Lupo
Specialty section:
This article was submitted to
Movement Science and Sport
a section of the journal
Frontiers in Psychology
Received: 17 April 2019
Accepted: 29 May 2019
Published: 18 June 2019
Brustio PR, Kearney PE, Lupo C,
Ungureanu AN, Mulasso A, Rainoldi A
and Boccia G (2019) Relative Age
Influences Performance of
World-Class Track and Field Athletes
Even in the Adulthood.
Front. Psychol. 10:1395.
doi: 10.3389/fpsyg.2019.01395
Relative Age Influences Performance
of World-Class Track and Field
Athletes Even in the Adulthood
Paolo Riccardo Brustio 1, Philip Edward Kearney 2, Corrado Lupo 1
Alexandru Nicolae Ungureanu 1, Anna Mulasso 1, Alberto Rainoldi 1and Gennaro Boccia 1
1NeuroMuscular Function Research Group, School of Exercise and Sport Sciences, Department of Medical Sciences,
University of Turin, Turin, Italy, 2Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
The relative age effect (RAE) is a common phenomenon observed in youth sports and
is characterized by a significant over-representation of athletes born close to the date
of selection. However, there is a lack of research on RAE in world-class track and field
athletes and it is not clear if this effect persists into adulthood. Thus, this study examined
for the first time the prevalence and magnitude of RAE at world class level in all track and
field disciplines. Birthdates of 39,590 athletes (51.6% females) ranked in the International
Association of Athletics Federations top 100 official lists between 2007 and 2018 season
of Under 18, Under 20, and Senior categories were collected. Under 18 and Under 20
athletes born in the first week of the year are about 2 to 3.5 times more likely to be
included in the top-100 ranking than the athletes born in the last week of the year. RAE
was overall larger in male compared to female athletes. In some disciplines (e.g., throwing
events) RAE persists in Senior category. These findings suggest that in some disciplines
relatively younger athletes may have less chances of reaching world-class performances
even in the adulthood. Governing bodies should reflect upon their policies for athlete
support and selection to minimize the RAE.
Keywords: relative age effect, birthdate distribution, selection bias, talent, athlete development
In sports systems, young athletes are generally grouped according to their birth year with the
purpose to provide equal opportunities and experiences during competitions (Cobley et al., 2009;
Kearney et al., 2018). However, in accordance with the maturation-selection hypothesis, relatively
older athletes may have more favorable anthropometric and physical characteristics in comparison
with relatively younger peers (Cobley et al., 2009; Lovell et al., 2015; Romann and Cobley, 2015).
Additionally, relative older athletes may be advanced in term of cognitive development (e.g.,
decision-making, abstract thinking, and creativity) and psychological factors (e.g., motivation,
self-efficacy, and self-esteem) (Musch and Grondin, 2001; Cobley et al., 2009; Baker et al., 2014).
Therefore, as a result of the assignment to categories based upon chronological age, athletes
born close to the cut-off date of selection are supposed to be advantaged in sports performance
(Smith et al., 2018) and in the process of talent identification, because they are older than their
peers born far from the cut-off date. Moreover, the talent identification could be influenced
by environmental factors, concerning social constructs (Wattie et al., 2015) like physical and
sociocultural environment policies and social agents such as parents, coaches, or athletes (Hancock
et al., 2013). The term relative age effect (RAE) refers to an asymmetry in the birth distribution of
Brustio et al. RAE in World-Class Athletics
a population where there is an over-representation of athletes
born close to the date of selection (Cobley et al., 2009; Boccia
et al., 2017b). The RAE was first observed in Canadian ice hockey
(Barnsley et al., 1985) and subsequently in many other team
sports, such as soccer (Steingröver et al., 2017; Brustio et al.,
2018; Cumming et al., 2018; Doyle and Bottomley, 2018; Peña-
González et al., 2018), Australian football (Haycraft et al., 2018),
basketball (Arrieta et al., 2016), and rugby (Till et al., 2010), as
well as in individual sports, such as swimming (Cobley et al.,
2018) alpine ski (Müller et al., 2016; Bjerke et al., 2017) wrestling
(Fukuda et al., 2017), and track and field (Romann and Cobley,
2015; Brazo-Sayavera et al., 2017, 2018; Kearney et al., 2018;
Romann et al., 2018).
In many nations track and field events are characterized by a
significant over-representation of athletes born close to the date
of selection (Romann and Cobley, 2015; Brazo-Sayavera et al.,
2017; Kearney et al., 2018; Romann et al., 2018). For example,
Brazo-Sayavera et al. (2017) highlighted the influential role of the
RAE, mediated by age and gender, on the selection in Spanish
National Athletics Federation training camps. In an extensive
study on UK athletes, Kearney et al. (2018) showed a large over-
representation of female and male athletes born close to the date
of selection in the majority of considered disciplines (i.e., 100-m,
hurdles, 800-m, 1,500-m, high jump, shot put, discus throw, and
javelin throw) and age categories (i.e., Under 13, 15, 17, 20, and
Senior Category). As expected, the effect was larger for younger
categories than for senior levels and it was even larger for athletes
ranked in the top 20 nationally compared to the others.
Despite this consistent finding at national level, there is a
paucity of data regarding the international context. Hollings
et al. (2014) evaluated the RAE in three event groups (i.e.,
sprints and hurdles, jumps, and throws) within an international
context focusing only on finalists of Under 18 (athletes aged
16–17 years) and Under 20 (athletes aged 18–19 years) World
Athletics Championships and found a significant RAE in both
categories with a stronger effect for Under 18 groups compared
to Under 20 ones. However, the selection for World Athletics
Championships is not only based on individual performances,
but also on technical choice of national athletics federations.
Consequently, even if the data about participations in World
Athletics Championship are of interest, they do not represent the
whole sample of individuals competing at international levels.
Thus, a more comprehensive analysis of RAE at international
level, considering both youth and senior categories, is warranted.
Indeed, an extensive evaluation of RAE across ages would be able
to identify if the possible RAE in youth categories is transient or
if it persists in adult categories (Cobley et al., 2018). Therefore, to
address the aforementioned gap, we aimed to comprehensively
quantify the prevalence and magnitude of RAE at world class
level in all track and field disciplines. While we hypothesized
that RAE would decrease as age increased (Hollings et al.,
2014), no prediction would be possible due to lack of data
about international level adult athletes. Moreover, according to
previous studies (Hollings et al., 2014; Romann and Cobley,
2015; Brazo-Sayavera et al., 2017, 2018; Kearney et al., 2018) we
expected to observe a stronger RAEs within male athletes and in
disciplines with a greater emphasis on speed and/or strength.
Data were collected from the publically available web-site of
IAAF (International Association of Athletics Federations; https:// This database provides information about
track and field athletes’ performances and rankings for both
genders. The web-site reports the results of three different
categories: Under 18, Under 20, and Senior categories. According
to the technical rules of IAAF the Under 18 category is composed
of athletes aged 16 and 17 years, while the Under 20 category
of athletes aged 18 to 19 years. This study was approved by
the local ethics committee of the University of Turin (Italy)
and involved access to public available databases. Therefore, no
informed consent was sought.
Birthdates of athletes ranked in the top 100 official lists in each
season from 2007 to 2018 were collected. Since the data from
2007 to 2009 were not available for Under 18 and Under 20, these
categories were analyzed only from 2010 to 2018. Only results
obtained in outdoor competitions and with legal wind speed (i.e.,
2 m/s) were included. As previously suggested (Kearney et al.,
2018), each athlete was only counted once per age category. The
following track and field disciplines were considered: 100-m, 100-
m hurdles, 200-m, 400-m, 400-m hurdles, 800-m, 1,500-m, 3,000-
m steeplechase, 5,000-m, high jump, pole vault, long jump, triple
jump, shot put, discus throw, hammer throw, and javelin throw.
Athletes selected for this study were classified in accordance
with their birthdate. According to IAAF rules the competition
year was from 1st January to 31st December. First, the birth week
(WB) of each athlete was calculated. For example, an athlete born
between 1st and 7th January was categorized in WB1, athletes
born between 8th and 14th January were categorized in WB2
and so. Afterward, the time of birth (TB) i.e., how far from
the beginning of the year a athletes was born (ranged between
0 and 1), was computed according to the formula TB=(WB
0.5)/52 where (WB0.5) corresponds to the midpoint of
the week in which athlete was born (Brustio et al., 2018;
Doyle and Bottomley, 2018).
Statistical Analyses
As recently suggested, the birthdate data were analyzed using
Poisson regressions (Brustio et al., 2018; Doyle and Bottomley,
2018, 2019; Rada et al., 2018). Separate Poisson regressions were
performed considering disciplines and gender. Using the formula
y=e(b0+b1x) the Poisson regression enables the frequency count
of an event (y) to be described by an explanatory variable x. Thus,
in this study it has been calculated how the frequency of birth
in a given week (y) was explained by the TB(x). Additionally,
the Index of Discrimination (ID), which provides the relative
odds of being selected for an athlete born in the first vs. the
last week of the competition year, was calculated as eb1 (Doyle
and Bottomley, 2018, 2019). Likelihood ratio R2was computed
according to Cohen et al. (2013).
To allow comparisons with previous studies that did not
adopt Poisson’s regression analysis, all athletes were categorized
Frontiers in Psychology | 2June 2019 | Volume 10 | Article 1395
Brustio et al. RAE in World-Class Athletics
in four groups based on their month of birth. Specifically, players
born between January and March, April and June, July and
September, and October and December were classified into the
quartile 1 (Q1), quartile 2 (Q2), quartile 3 (Q3), and quartile
4 (Q4), respectively. Odds ratios (ORs) and 95% confidence
intervals [95% CIs] were calculated for the first and the last
quartile (i.e., Q1 vs. Q4). We compared the distribution of
athletes’ birthdates with an uniform distribution (i.e., 25% for
each quartile) (Delorme and Champely, 2013).
All data were analyzed with custom-written software in
MATLAB R2017b (Mathworks, Natick, Massachusetts). The level
of significance was set at p0.05.
A total of 98,984 records were downloaded. After removal of
missing data (about 9%) and duplicates (i.e., athletes that are
present in top 100 official lists for over 1 year in the considered
category) a total of 39,590 birthdates (51.6% females) were
analyzed. The mean and standard deviation of WBand TB, as
well as the results of Poisson regression equations, fit statistics
and ID for each event are presented in Table 1. The scatterplots of
RAE frequency by week of year both for male and female athletes
in Under 18, Under 20, and Senior categories are provided
in Figure 1.
When analyzing male athletes, the Poisson regressions were
significant for Under 18 (p<0.001; R2=0.91), Under 20 (p<
0.001; R2=0.86) and Senior categories (p<0.001; R2=0.30).
Specifically, ID showed that in Under 18 and Under 20 categories
the male athletes born right at the start of the year were 3.46 and
2.45 times, respectively, more likely to be included in top 100 rank
than those born at the end of the year. In Senior category the ID
score was lower (i.e., 1.29).
In general female athletes showed similar trends. Indeed, the
Poisson regressions were significant for Under 18 (p<0.001; R2
=0.84), Under 20 (p<0.0001; R2=0.72), and Senior categories
(p<0.001; R2=0.26). Specifically, ID showed that in Under 18
and Under 20 categories the female athletes born in the first week
of the year were 2.21 and 1.86 times, respectively, more likely to
be included in the top 100 rank than those born at the end of the
year. Again, in Senior category the ID was lower (i.e., 1.19).
When considering each event separately it is possible to
highlight a few peculiarities among disciplines. For example, in
males the Poisson regressions were significant in all disciplines
for Under 18 (all p<0.001; R2ranged =0.12–0.70) and Under 20
(all p<0.0001; R2ranged =0.11–0.59), while in Senior category
the Poisson regressions were significant only for 400-m hurdles
and throwing events (all p<0.01; R2ranged =0.16–0.27), but
not for the other disciplines (all p>0.05; R2ranged =0–0.05).
In females the trend was generally similar, but showed some
differences. For example, the Poisson regressions were significant
in all disciplines for Under 18 (all p<0.001; R2ranged =
0.11-0.63). In Under 20 the Poisson regressions were significant
in all disciplines (all p<0.001; R2ranged =0.06–0.34) with
the exception of pole vault (p=0.112; R2=0.06). Similarly
to males, in Senior category, two of the throwing events (shot
put and discus throw) showed significant Poisson regressions.
In addition, long jump showed significant Poisson regressions,
while triple jump showed a trend close to significance (p=0.054).
In Senior category Poisson regressions were not significant for
other disciplines (p>0.05; R2ranged =0–0.18).
Table 2 provides the odds ratios (ORs) and 95% confidence
intervals [95% CIs] of Q1 vs. Q4. Regardless of the gender,
ORs suggested that the likelihood of being included in the
top 100 rank is higher for an athlete born in the Q1
rather than in Q4 both in Under 18 (OR ranged =1.3–
5.2) and Under 20 (OR ranged =1.2–3.6) category, but not
in Senior category (OR ranged =0.8–1.5). Moreover, RAEs
are likely stronger in males compared with females in all
categories. Indeed, on average in Under 18, Under 20, and
Senior categories male athletes were 2.5, 2.0, and 1.2 times,
respectively, more likely to be born in Q1 than Q4, while female
athletes were 1.8, 1.6, and 1.1 times, respectively, more likely
to be born in Q1 than Q4. Of note, the ORs were generally
smaller in middle distance events (e.g., 1,500-m and 5,000-
m) and greater in throwing events in comparison with the
other disciplines.
This study examined the birthdate of 39,590 track and field
athletes, who were ranked in the world top-100 ranking
at least once in the last 10 years. The results showed
a large over-representation of athletes born close to the
beginning of the calendar year in Under 18 and Under 20
categories. In some disciplines, this trend is maintained in the
Senior category.
The Poisson regression analysis has recently been proposed
to be the most reliable method to identify the presence of the
RAE (Brustio et al., 2018; Doyle and Bottomley, 2018; Rada et al.,
2018). The Poisson regression analysis quantifies the magnitude
of the RAE through the Index of Discrimination (ID) which
consists in the relative odds of being selected for an athlete born
in the first vs. the last week of the competition year (Table 1).
Under 18 and Under 20 athletes born in the first week of the year
are about 2 to 3.5 times more likely to be included in the
top-100 ranking than the athletes born in the last week of the
year (see overall ID scores in Table 1 and Figure 1). Similar
trends can be observed adopting a more classical approach of
subgrouping athletes based on their birthdate quartiles (Table 2).
Indeed, the ORs between the athletes born in the first (i.e.,
between January and March) vs. the last (i.e., between October
and December) quartile ranged from 1.5 to 2.5 in the Under 18
and Under 20 categories. Together these indices clearly indicate
that being relatively older within a competition year confers a
large effect on athletics performances up to 19 years of age. It
is possible to suppose that differences in population distribution
at Under 18 and Under 20 are not (highly unlikely to be)
due to current maturational differences, but rather a relic of
maturational differences that existed at a younger age, the effects
of which were amplified by the actions of various social agents
(Hancock et al., 2013). Indeed, according to the framework of
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Brustio et al. RAE in World-Class Athletics
TABLE 1 | Relative Age Effect (RAE) according to the poisson regression for male and female athletes at each category of age group and event.
Male Female
Category NWBTBb0b1ID R2P N Wb Tb b0b1ID R2P
U18 5,950 21.26 ±14.62 0.40 ±0.28 5.297 1.241 3.46 0.91 <0.001 8,342 23.10 ±14.94 0.43 ±0.29 5.449 0.794 2.21 0.84 <0.001
U20 6,759 22.67 ±14.55 0.43 ±0.28 5.283 0.897 2.45 0.86 <0.001 5,786 23.84 ±14.98 0.45 ±0.29 5.005 0.618 1.86 0.72 <0.001
Senior 6,465 25.40 ±15.00 0.48 ±0.29 4.948 0.255 1.29 0.30 <0.001 6,288 25.75 ±15.22 0.49 ±0.29 4.880 0.172 1.19 0.26 <0.001
U18 556 20.89 ±14.49 0.39 ±0.28 2.963 1.332 3.79 0.56 <0.001 535 22.94 ±15.19 0.43 ±0.29 2.718 0.831 2.3 0.38 <0.001
U20 352 23.14 ±14.97 0.44 ±0.29 2.278 0.782 2.19 0.26 <0.001 302 23.80 ±15.50 0.45 ±0.30 2.056 0.627 1.87 0.13 0.002
Senior 439 26.92 ±14.69 0.51 ±0.28 2.085 0.097 0.91 0.00 0.559 398 26.57 ±15.55 0.50 ±0.30 2.027 0.017 0.98 0.00 0.923
U18* 129 19.60 ±14.83 0.37 ±0.29 1.563 1.181 3.26 0.30 <0.001 532 23.50 ±14.57 0.44 ±0.28 2.654 0.699 2.01 0.32 <0.001
U20 402 22.13 ±14.00 0.42 ±0.27 2.493 0.945 2.57 0.38 <0.001 385 23.75 ±14.00 0.45 ±0.27 2.304 0.638 1.89 0.19 <0.001
Senior 356 25.76 ±14.86 0.49 ±0.29 2.008 0.171 1.19 0.03 0.353 372 25.53 ±15.39 0.48 ±0.30 2.078 0.225 1.25 0.04 0.211
U18 563 21.32 ±14.46 0.40 ±0.28 2.933 1.225 3.4 0.64 <0.001 555 23.13 ±15.30 0.44 ±0.29 2.735 0.785 2.19 0.30 <0.001
U20 387 23.94 ±14.73 0.45 ±0.28 2.289 0.594 1.81 0.15 <0.001 345 23.14 ±15.52 0.44 ±0.30 2.259 0.784 2.19 0.19 <0.001
Senior 466 26.85 ±14.37 0.51 ±0.28 2.153 0.080 0.92 0.00 0.617 425 27.00 ±15.64 0.44 ±0.29 2.042 0.116 0.89 0.01 0.490
U18 512 21.25 ±14.79 0.40 ±0.28 2.845 1.242 3.46 0.51 <0.001 510 23.86 ±15.00 0.45 ±0.29 2.574 0.614 1.85 0.21 <0.001
U20 372 24.56 ±14.64 0.28 ±2.18 2.184 0.450 1.57 0.11 0.013 332 23.96 ±15.13 0.45 ±0.29 2.147 0.576 1.78 0.19 0.002
Senior 443 26.70 ±15.64 0.30 ±2.12 2.120 0.045 0.96 0.00 0.784 414 25.52 ±15.39 0.48 ±0.30 2.186 0.227 1.25 0.03 0.183
U18 340 18.53 ±12.66 0.35 ±0.24 2.639 1.683 5.38 0.56 <0.001 550 24.03 ±14.78 0.45 ±0.28 2.631 0.573 1.77 0.19 <0.001
U20 424 22.08 ±14.06 0.27 ±2.57 2.573 1.037 2.82 0.38 <0.001 351 24.51 ±14.91 0.46 ±02.9 2.131 0.461 1.59 0.1 0.013
Senior 399 23.71 ±15.13 0.29 ±2.34 2.345 0.649 1.91 0.27 <0.001 376 27.58 ±14.80 0.52 ±0.28 1.851 0.250 0.78 0.03 0.163
U18 573 21.31 ±14.60 0.40 ±0.28 2.952 1.228 3.41 0.55 <0.001 550 24.60 ±14.87 0.46 ±0.29 2.571 0.440 1.55 0.11 0.003
U20 435 22.94 ±14.74 0.43 ±0.28 2.511 0.830 2.29 0.31 <0.001 310 24.62 ±15.15 0.46 ±0.29 1.995 0.435 1.54 0.08 0.028
Senior 424 25.38 ±15.46 0.48 ±0.30 2.225 0.258 1.29 0.05 0.125 408 25.98 ±15.71 0.49 ±0.30 2.119 0.119 1.13 0.01 0.487
U18 542 22.97 ±15.20 0.43 ±0.29 2.728 0.824 2.28 0.32 <0.001 463 23.53 ±15.15 0.44 ±0.29 2.512 0.691 2.00 0.20 <0.001
U20 432 23.89 ±14.99 0.45 ±0.29 2.405 0.605 1.83 0.25 <0.001 331 23.36 ±15.03 0.44 ±0.29 2.194 0.731 2.08 0.23 <0.001
Senior 409 25.59 ±15.36 0.48 ±0.30 2.166 0.210 1.23 0.03 0.221 416 26.58 ±15.21 0.50 ±0.29 2.071 0.018 0.98 0.00 0.917
U18* 111 21.96 ±16.08 0.41 ±0.31 1.396 1.031 2.80 0.23 0.002 226 23.67 ±15.90 0.45 ±0.31 1.780 0.657 1.93 0.12 0.005
U20 416 23.26 ±14.32 0.44 ±0.28 2.411 0.672 1.96 0.21 <0.001 402 24.23 ±14.39 0.46 ±0.28 2.297 0.527 1.69 0.11 0.002
Senior 348 25.42 ±14.94 0.48 ±0.29 2.024 0.250 1.28 0.04 0.178 375 25.22 ±14.68 0.48 ±0.28 2.120 0.297 1.35 0.05 0.098
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Brustio et al. RAE in World-Class Athletics
TABLE 1 | Continued
Male Female
Category NWBTBb0b1ID R2P N Wb Tb b0b1ID R2P
U18 183 23.16 ±16.38 0.44 ±0.32 1.656 0.767 2.15 0.19 0.003 244 24.39 ±15.30 0.46 ±0.29 1.780 0.488 1.63 0.11 0.029
U20 391 23.68 ±15.30 0.45 ±0.29 2.328 0.656 1.93 0.23 <0.001 400 23.81 ±15.75 0.45 ±0.30 2.336 0.624 1.87 0.19 <0.001
Senior 437 26.41 ±15.79 0.50 ±0.30 2.139 0.021 1.02 0.00 0.899 475 26.66 ±15.29 0.50 ±0.29 2.193 0.038 0.96 0.00 0.813
U18 488 22.41 ±14.52 0.42 ±0.28 2.680 0.958 2.61 0.42 <0.001 522 22.07 ±14.41 0.41 ±0.28 2.782 1.042 2.83 0.52 <0.001
U20 383 21.69 ±13.88 0.41 ±0.27 2.511 1.134 3.11 0.42 <0.001 275 24.52 ±15.43 0.46 ±0.30 1.887 0.460 1.58 0.09 0.029
Senior 350 25.97 ±14.65 0.49 ±0.28 1.967 0.123 1.13 0.01 0.508 362 25.85 ±15.47 0.49 ±0.30 2.015 0.151 1.16 0.01 0.409
U18 356 20.36 ±14.37 0.38 ±0.28 2.569 1.468 4.34 0.56 <0.001 473 24.62 ±15.22 0.46 ±0.29 2.418 0.436 1.55 0.12 0.007
U20 384 22.36 ±14.86 0.42 ±0.29 2.445 0.970 2.64 0.37 <0.001 317 25.16 ±15.06 0.47 ±0.29 1.958 0.310 1.36 0.06 0.112
Senior 344 25.41 ±14.65 0.48 ±0.28 2.013 0.252 1.29 0.04 0.178 316 25.30 ±14.34 0.48 ±0.28 1.940 0.278 1.32 0.04 0.155
U18 547 20.63 ±14.66 0.39 ±0.28 2.972 1.398 4.05 0.70 <0.001 549 22.34 ±15.29 0.42 ±0.29 2.805 0.975 2.65 0.40 <0.001
U20 401 22.85 ±15.07 0.43 ±0.29 2.439 0.853 2.35 0.31 <0.001 332 23.82 ±14.73 0.45 ±0.28 2.149 0.623 1.86 0.24 0.001
Senior 420 25.88 ±15.00 0.49 ±0.29 2.160 0.144 1.15 0.02 0.394 392 24.56 ±15.03 0.46 ±0.29 2.236 0.450 1.57 0.09 0.011
U18 521 20.93 ±14.40 0.39 ±0.28 2.894 1.323 3.75 0.49 <0.001 570 23.25 ±15.03 0.44 ±0.29 2.750 0.758 2.13 0.33 <0.001
U20 374 23.21 ±15.07 0.44 ±0.29 2.332 0.767 2.15 0.26 <0.001 322 24.76 ±15.43 0.47 ±0.30 2.018 0.402 1.49 0.06 0.038
Senior 362 25.24 ±14.71 0.48 ±0.28 2.082 0.291 1.34 0.05 0.111 336 24.92 ±15.36 0.47 ±0.30 2.043 0.365 1.44 0.05 0.054
U18* 95 21.35 ±15.01 0.40 ±0.29 1.209 0.945 2.57 0.22 0.009 522 21.43 ±14.04 0.40 ±0.27 2.847 1.199 3.32 0.63 <0.001
U20 405 20.40 ±13.78 0.38 ±0.26 2.677 1.387 4.00 0.53 <0.001 354 22.36 ±13.94 0.42 ±0.27 2.340 0.886 2.43 0.34 <0.001
Senior 346 23.27 ±14.47 0.44 ±0.28 2.248 0.752 2.12 0.24 <0.001 318 24.00 ±14.35 0.45 ±0.28 2.087 0.580 1.79 0.16 0.003
U18* 61 21.95 ±13.45 0.41 ±0.26 0.705 0.476 1.61 0.06 0.310 473 20.99 ±14.67 0.39 ±0.28 2.792 1.309 3.70 0.61 <0.001
U20 404 20.13 ±13.71 0.38 ±0.26 2.718 1.527 4.60 0.59 <0.001 337 22.17 ±14.55 0.42 ±0.28 2.312 0.934 2.54 0.30 <0.001
Senior 302 23.94 ±14.71 0.45 ±0.28 2.042 0.595 1.81 0.16 0.003 304 24.00 ±15.14 0.45 ±0.29 2.042 0.580 1.79 0.18 0.004
U18* 122 21.28 ±14.89 0.40 ±0.29 1.371 0.713 2.04 0.12 0.031 514 22.00 ±14.84 0.41 ±0.29 2.774 1.058 2.88 0.44 <0.001
U20 380 23.19 ±13.95 0.44 ±0.27 2.35 0.772 2.16 0.24 <0.001 352 23.23 ±15.25 0.44 ±0.29 2.269 0.761 2.14 0.22 <0.001
Senior 284 23.87 ±14.75 0.45 ±0.28 2.01 0.616 1.85 0.17 0.003 290 25.25 ±15.49 0.48 ±0.30 1.860 0.289 1.34 0.04 0.156
U18* 251 21.82 ±14.56 0.41 ±0.28 2.059 1.030 2.80 0.28 <0.001 554 23.19 ±14.67 0.44 ±0.28 2.727 0.772 2.16 0.33 <0.001
U20 417 22.09 ±14.65 0.42 ±0.28 2.556 1.037 2.82 0.34 <0.001 339 24.43 ±15.12 0.46 ±0.29 2.105 0.480 1.62 0.12 0.011
Senior 336 23.48 ±14.85 0.44 ±0.29 2.197 0.702 2.02 0.23 <0.001 311 25.99 ±15.23 0.49 ±0.29 1.846 0.117 1.12 0.01 0.552
*In these disciplines the sample size was small because few U18 athletes competed with the Senior rules and tool weights. U18, Under 18; U20, Under 20.
Frontiers in Psychology | 5June 2019 | Volume 10 | Article 1395
Brustio et al. RAE in World-Class Athletics
FIGURE 1 | Scatterplot of birthdate frequency by week for Under 18, Under 20, and Senior categories both for male (upper panel) and female athletes (bottom panel).
The red line represents the best fit of the Poisson regression.
the Social Agent Model (Hancock et al., 2013) parents, coaches,
or athletes may all amplify at a different level the RAE. Initially,
parents may influence the RAE by enrolling more frequently
relatively older than younger athletes. Furthermore, coaches
might place greater expectations on relatively older athletes and
consequently advantage them (e.g., more attention during the
training sessions). Additionally, athletes themselves may affect
the RAE through their self-expectations, influenced by coaches
and parents, affording continued success (e.g., apply yourself
in the training sessions). The IDs of Under 20 athletes was
smaller than Under 18 ones (Table 1), highlighting that RAE
decreases with the transition to the upper category. This is in
line with the trends evident in national Spanish (Brazo-Sayavera
et al., 2017, 2018) and UK athletes (Kearney et al., 2018) and
in World Championship fields (Hollings et al., 2014). However,
it interesting to note that in the study conducted by Hollings
et al. (2014) in occasion of the Under 18 World Championship,
the ORs were larger than those of the present study both for
males (World Championship: OR =3.7; world top-100 ranking:
OR =2.4) and females (World Championship OR =2.1;
world top-100 ranking: OR =1.7). This difference may suggest
that the selection in to compete at the World Championship
may furthermore accentuate the RAE with respect to what
can be expected from the athletes’ performances. However,
this difference in the effect size between the data of Hollings
et al. (2014) and the present findings disappear in the Under
20 category.
The comparison between different disciplines may be of
particular interest. In general, RAE in youth categories was
generally weaker for the middle-distance events (e.g., 1,500-
m and 5,000-m) with respect to the other disciplines. This
may suggest that endurance capacity was less influenced by
the relative age. The disciplines of 110 hurdles and 400 m
hurdles were more affected by the RAE compared to the
100-m and 400-m races in line. This may suggest that the
RAE may be of particular benefit in these disciplines where
a more developed anthropometric profile (i.e., longer limbs)
may confer an advantage in dealing with the distance between
hurdles. Within the throwing events, the shot-put and discus
throw were more influenced by the RAE than the hammer
and javelin throw, both in males and females. These results at
world class level reinforce the conclusion observed in national
(Kearney et al., 2018) and World Athletics Championship
(Hollings et al., 2014) where RAEs also are likely to be
larger in events with a greater emphasis on speed and/or
strength (Hollings et al., 2014; Kearney et al., 2018).
RAE was generally larger in males compared to female
athletes. This finding was valid for all disciplines. Indeed, both
IDs and ORs were overall higher in males (IDs ranged =1.29–
3.46; ORs ranged =1.2–2.4) than in females (IDs ranged =
1.19–2.21; ORs ranged =1.1–1.7) underlining that RAE has
a smaller but consistent influence on female sports (Brazo-
Sayavera et al., 2017, 2018; Kearney et al., 2018). Different
speculative explanations may support these data. The inferior
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Brustio et al. RAE in World-Class Athletics
TABLE 2 | Relative Age Effect (RAE) according Odds Ratio for male and female athletes at each category of age group and event.
Male Female
U18 U20 Senior U18 U20 Senior
Disciplines OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI] OR [95% CI]
Overall 2.4 [2.1–2.7] 2.0 [1.8–2.2] 1.2 [1.1–1.3] 1.7 [1.6–1.9] 1.6 [1.4–1.7] 1.1 [1.0–1.2]
100–m 2.7 [1.9–3.8] 1.8 [1.2–2.7] 0.9 [0.6–1.3] 1.8 [1.3–2.5] 1.4 [0.9–2.2] 0.9 [0.6–1.4]
110–m Hurdles 2.8 [1.4–5.9] 2.5 [1.6–3.8] 1.1 [0.7–1.7] 1.6 [1.1–2.3] 1.7 [1.1–2.6] 1.1 [0.8–1.7]
200–m 2.5 [1.8–3.6] 1.6 [1.0–2.3] 1.0 [0.7–1.4] 1.7 [1.2–2.4] 1.6 [1.0–2.4] 0.9 [0.6–1.3]
400–m 2.5 [1.7–3.6] 1.4 [0.9–2.1] 1.1 [0.7–1.6] 1.6 [1.1–2.2] 1.4 [0.9–2.2] 1.2 [0.8–1.7]
400–m Hurdles 5.2 [3.1–8.8] 2.4 [1.6–3.6] 1.5 [1.0–2.2] 1.5 [1.1–2.1] 1.5 [1.0–2.3] 0.8 [0.5–1.2]
800–m 2.3 [1.6–3.2] 1.9 [1.3–2.7] 1.2 [0.8–1.7] 1.4 [1.0–2.0] 1.6 [1.0–2.5] 1.1 [0.7–1.6]
1,500–m 1.6 [1.1–2.2] 1.4 [1.0–2.0] 1.1 [0.7–1.6] 1.6 [1.1–2.3] 1.8 [1.2–2.8] 1.0 [0.7–1.5]
5,000–m 1.7 [1.0–3.0] 1.6 [1.1–2.4] 1.0 [0.7–1.4] 1.5 [0.9–2.5] 1.5 [1.0–2.2] 1.0 [0.7–1.4]
3,000–m Steeplechase 2.2 [1.0–4.7] 1.8 [1.2–2.6] 1.1 [0.7–1.7] 1.5 [0.9–2.5] 1.6 [1.1–2.3] 1.2 [0.8–1.8]
High Jump 2.0 [1.4–2.8] 2.7 [1.7–4.1] 1.0 [0.7–1.6] 2.1 [1.5–3.0] 1.3 [0.8–2.0] 1.1 [0.7–1.6]
Pole Vault 2.8 [1.8–4.4] 1.9 [1.3–2.8] 1.1 [0.7–1.7] 1.3 [0.9–1.8] 1.2 [0.8–1.9] 1.2 [0.8–1.8]
Triple Jump 2.8 [1.9–4.0] 1.7 [1.2–2.6] 1.2 [0.8–1.8] 1.7 [1.2–2.4] 1.4 [0.9–2.2] 1.2 [0.8–1.8]
Long Jump 2.7 [1.9–3.8] 1.9 [1.3–2.9] 1.2 [0.8–1.7] 1.9 [1.4–2.6] 1.5 [1.0–2.3] 1.2 [0.8–1.8]
Shot Put 2.4 [1.0–5.6] 3.4 [2.2–5.2] 1.5 [1.0–2.4] 2.5 [1.7–3.6] 2.3 [1.5–3.6] 1.5 [1.0–2.4]
Discus Throw 2.5 [0.8–7.4] 3.6 [2.3–5.5] 1.4 [0.9–2.3] 2.6 [1.8–3.9] 2.2 [1.4–3.4] 1.5 [1.0–2.4]
Hummer Throw 2.4 [1.2–5.1] 1.7 [1.1–2.6] 1.5 [1.0–2.5] 2.2 [1.6–3.2] 1.7 [1.1–2.6] 1.3 [0.8–2.1]
Javelin Throw 2.1 [1.2–3.4] 1.9 [1.3–2.8] 1.5 [1.0–2.4] 1.7 [1.2–2.3] 1.4 [0.9–2.1] 1.1 [0.7–1.6]
odds ratios (ORs) and 95% confidence intervals (95% CI) first vs. last quartile. U18, Under 18; U20, Under 20.
popularity of the sports and the consequent more opportunities
to be selected (Brazo-Sayavera et al., 2018), as well as the early
maturation of females (Smith et al., 2018), may have minimized
the RAE. The female pole vault was the only discipline at Under
20 that did not show a clear evidence of RAE. This may be linked
to the fact that many female pole vaulters started their early sport
career as gymnasts, a sport in which the typical RAE has not been
found (Baker et al., 2014).
In the Senior category, the prevalence of RAE decreases but
does not totally disappear (Tables 1,2). In fact, the chance of
being in the world top-100 ranking was about 1.2–1.3 times
greater for athletes born in the first compared to the last week
of the year (Table 1). However, this effect was mainly driven by
some specific disciplines. In males this effect was present only in
400-m Hurdles and throwing events (Tables 1,2). The throws in
athletics are particularly influenced by the anthropometric and
strength features of athletes, thus being relatively more mature
may confer a great advantage in the early phase of an athlete’s
development (Hollings et al., 2014; Kearney et al., 2018). The
fact that this effect was maintained at senior level suggests that
the relatively older throwers had more chances of continuing
their sport career up to the senior level. In females this effect
was present in shot put and discus throw but not in javelin
and hammer throw. In addition, it was present also in long and
triple jump. For females, the senior trends are more difficult to
explain and require further investigations. However, this data
showed that at international level the large initial benefit observed
in younger category has a long-lasting effect only for some
disciplines. Minimizing the RAE in these disciplines is crucial to
give the chance of accessing a world class career to athletes born
late in the year. Furthermore, the finding that some disciplines
showed RAE in youth but not in the Senior category may in part
explain why previous studies showed that excelling at younger
age grades is not a strong predictor of success in adulthood
(Boccia et al., 2017a, 2018; Kearney and Hayes, 2018). Indeed, it is
possible to speculate that some athletes born late in the year could
reach the world class level only in the senior category, when the
effect of relative age tends to disappear. However, this is not a
prospective study, thus this is only a speculation that remains to
be confirmed by future studies.
While increased coach and parent education has been
proposed as a method for reducing RAEs (e.g., Musch and
Grondin, 2001; Andronikos et al., 2016), Mann and van
Ginneken (2017) illustrated that knowledge of the effect is
insufficient to influence selection decisions. A number of
structural solutions have been proposed to address RAEs,
including systems for rotating cut off dates on a yearly basis
(e.g., Hurley et al., 2001), classifying athletes by maturation
status (e.g., Cumming et al., 2017), or applying a correction
factor to performance results (e.g., Romann and Cobley, 2015;
Cobley et al., 2019). However, there is a paucity of research
investigating the long term effectiveness of these proposals
(Haycraft et al., 2018).
Some limitations should be highlighted when interpreting
the current data. In some countries (e.g., UK) the cut-off
date for youth category is August 31st and this may have
affected our results. However, according to IAAF rules we
defined the cut-off for Under 18 and Under 20 date on the
Frontiers in Psychology | 7June 2019 | Volume 10 | Article 1395
Brustio et al. RAE in World-Class Athletics
31st December in the year of competition. Furthermore, it
should be underlined that regarding Under 18 and Under 20
categories we analyzed each calendar year from 2010 to 2018.
Thus, each young athlete had the chance of compare in the
ranking both in the first or the second constituent year of
each category. For this reason, we do not expect any bias
caused by the fact that youth categories are constituted by two
competitive years.
This is the first study examining the prevalence of RAE at world
class level (i.e., athletes in the world top-100 ranking) in both
youth and senior categories in all track and field disciplines.
In conclusion, the present study underlined that relative age
affected the performance of Under 18 and Under 20 world class
athletes. The athletes born close to the cut-off date of selection
had an increased chance of being included in the world top-
100 ranking. This effect was larger in male compared to female
athletes. The RAE may induce a bias in the talent identification
process by decreasing the chance of selection for talented athletes
born late in the year of consideration. This was evident in some
peculiar disciplines, namely the 400-m Hurdles and throwing
events for males, and shot put, discus throw, long and triple jump
in females.
The datasets generated for this study are available on request to
the corresponding author.
Conceptualization: PB and GB. Investigation: PB and AU. Formal
analysis: PB. Funding acquisition: AR and CL. Supervision:
GB, AR, and CL. Writing—original draft: PB, GB, and PK.
Writing—review and editing: PB, GB, PK, CL, AU, AM,
and AR.
All the funding regarding the realization of this study were
received internally to the authors’ organization (CL’s and
AR’s Departmental funding; Department of Medical Sciences,
University of Torino, Turin, Italy). There was no additional
external funding received for this study.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2019 Brustio, Kearney, Lupo, Ungureanu, Mulasso, Rainoldi and
Boccia. This is an open-access article distributed under the terms of the Creative
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are credited and that the original publication in this journal is cited, in accordance
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Frontiers in Psychology | 9June 2019 | Volume 10 | Article 1395
... Moreover, the RAE has not been consistently confirmed in female athlete groups, and, when it has been confirmed, the effect size has frequently been smaller than in males (Cobley et al., 2009). Interestingly, this phenomenon is also present in world-class track and field athletes (Brustio et al., 2019). Thus, a higher prevalence of the RAE in male than female judo athletes can be explained by a higher competitiveness in males due to the longer existence of competitive opportunities, generating a biased selection for male athletes. ...
... Briefly, a 10-month difference between athletes when they are Cadets represents a higher percentage of their total age compared to when the same athletes are Seniors. Furthermore, the prevalence of the RAE in the Cadet and Junior categories but not in the Senior category may in part be explained by the fact that excelling at a younger age is not a strong predictor of success in adulthood (Boccia et al., 2017;Brustio et al., 2019). In fact, Julio et al. (2011) showed that successful competitive performance in early judo state-level competition was not associated with success later in adulthood. ...
... In fact, Julio et al. (2011) showed that successful competitive performance in early judo state-level competition was not associated with success later in adulthood. Thus, some judo athletes born late in the year may not reach the world-class level until reaching the Senior category when the relative age effect tends to disappear (Brustio et al., 2019). Therefore, selection bias at an earlier age may result in the representation of the RAE throughout the sporting career of these athletes. ...
This study was conducted to determine whether the relative age effect (RAE) is present in different age groups, weight categories, sexes, and across different time frames in international-level judo competition. A total of 9451 judo athletes competing at the Olympic Games and/or World Championships in the Cadet, Junior and/or Senior age groups between 1993 and 2020 were considered. Athletes' birthdate distributions were grouped in four quartiles (Q1: January-March; Q2: April-June; Q3: July-September; Q4: October-December) and compared to a day-corrected theoretical distribution using Chi-squared analysis. Poisson regression was also used to evaluate the ability to explain weekly birth count. RAE was more prevalent in males than females (p < .05), and for Cadets and Juniors compared to Seniors (p < .05). Heavyweight and middleweight categories presented RAEs in Senior and Junior males, while for females it was present in Cadet heavyweights (p < .05). RAE was more prevalent in recent years (2009-2021) for Senior male judo athletes (p < .05). Poisson analysis illustrated some nuanced information, including RAE detection during an earlier time frame, not readily apparent with the traditional analysis.
... Furthermore, high-performance during adolescence may not necessarily mean a higher performance at later ages [16,17]. Many factors have an impact on the performance of athletes, like the relative age effect [57], biological age, hormones [58] and so on. Further research is also needed to delve into the optimal development for adolescents that contributes to their performance in adulthood. ...
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Purpose: The aims of this study were: (1) to profile anthropometric, physical fitness, and specific throwing strength characteristics among 14–18 years boys and girls throwers; (2) to evaluate which factors vary with age, and which correlate with specific throwing strength; (3) to identify the measured variables that best predict specific throwing strength. Methods: Anthropometric, physical fitness, and specific throwing strength of 154 boys and 104 girls, who participated in track-and-field throw (Shot put, Javelin, Discus and Hammer throw) from four age categories (U15, U16, U17, U18), were measured in September 2022. The differences and correlations in parameters among different age, sex and throwing groups were analyzed using parametric and non-parametric testing. Multivariate linear regression analysis was used to identify the variables that best explain the specific throwing strength. Results: Disparities in height between boys and girls of the same age have consistently existed, however, the dissimilarity in weight tends to diminish as they grow older. Boys and girls of identical age groups exhibit noteworthy disparities in terms of speed, agility, and jumping prowess. These disparities tend to amplify as they advance in age. Significant differences were observed among boys of different ages in Height (p = 0.038), Body Mass (p = 0.02), BMI (p = 0.025), sit and reach test (p = 0.035), standing long jump (p = 0.012), standing triple jump (p < 0.01), forward overhead medicine ball throw (p = 0.002) and the hexagon agility test (p < 0.01). No differences were found in anthropometric measurements among girls, but differences were found in the hexagon agility test (p = 0.017) and plank test (p = 0.041). Specific throwing strength exhibits variations due to differences in events, age, and gender. Additionally, physical fitness performance, especially lower limb power, linear sprint speed, forward overhead medicine ball throw and backward overhead shot throw, have a high correlation with specific throwing strength. Conclusions: These findings broaden the existing knowledge base for coaches and practitioners, enabling them to discern the distinctive attributes of track and field throwers and capture the crucial physical markers that are pivotal for nurturing the progression of track-and-field throwers. The study suggests that throwers aged 14 to 18 should strive to comprehensively cultivate their athletic abilities.
... The greater utilization of the rotational technique among males could be influenced by its potential advantages in terms of power generation and competitive success. On the other hand, the prevalence of the glide technique among females might stem from its emphasis on finesse and technique, which may be favoured in certain training environments (Arrieta et al., 2016;Brustio et al., 2019;Schleichardt et al., 2019). The analysis of distance covered by different throwing techniques among male and female athletes offers valuable insights into the relationship between technique selection and performance outcomes. ...
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The present study delves into the realm of collegiate shot-put athletes' throwing techniques, aiming to undertake a comprehensive comparative video analysis of various throwing styles. The primary objective is to visually explore these athletes' diverse throwing techniques and shed light on the critical performance factors associated with each style. By employing video analysis tools and techniques, this research intends to provide a detailed examination of the biomechanical nuances and body movements in different throwing methods. Through an extensive collection of video footage featuring collegiate shot-put athletes, the study will meticulously dissect and compare the variations in throwing techniques. The aim is to analyse the different types of throwing techniques used and the technique that produces the best result by collegiate shot-put athletes in and around Bangalore. 220 shot-put athletes of age group between 18-25 years, representing colleges from different Universities were approached to analyse their shot-put throwing technique with two high-definition cameras and analysed using Kinovea software. The mean rank for velocity was higher in the glide technique for both male and female athletes compared to the rotational technique. Male athletes with higher BMI used rotational technique in comparison to athletes with lower BMI, in female athletes with higher BMI used glide technique in comparison to athletes with lower BMI (p-0.0001.) Based on the results obtained from our study, the glide technique was the most prevalent technique used by collegiate athletes. The gliding technique showed better throwing distance than the rotational technique. The findings of this study hold the potential to offer valuable insights to coaches, athletes, and researchers in the field of shot-put training and sports biomechanics. The comparative video analysis approach provides a unique visual perspective that supplements the existing body of knowledge concerning shot put techniques.
... In sports where the selection cut-off date is January 1st, the number of athletes born between January and March may be several times higher than the number of athletes born between October and December [3]. Thus the RAE is widespread among young male athletes (age 15-18 years) performing competitively in soccer, athletics, and basketball [1,[3][4][5][6]. Many studies have examined the wide spread of the RAE among soccer players of different ages and levels of competition [7][8][9][10]. ...
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Background: The relative age effect (RAE) is most prevalent in highly competitive youth soccer and persists to a lesser extent in senior soccer. However, it is known that soccer players born in the second half of the year are as successful at senior level, indicating that they are equally talented although under-represented at youth level due to bias during the selection process, in which the emphasis is on more pronounced physical qualities in a specific period of time. Examining the prevalence of the RAE among professional soccer players depending on the level of competition and playing position, as well as analyzing the relationship between the player's birth quarter and market value, are of scientific interest. Methods: The dates of birth, playing position, and market value of all adult male soccer players included in the final rosters of teams from the top-division of 54 European countries, listed on on August 15th, 2020, were analyzed (18,429 soccer players in total). All players were categorized into four groups according to the quarter of birth (Q) and playing position. All teams were further sub-divided in groups depending on the soccer clubs' level of representation in the UEFA Champions League. Results: Of 18,429 players, 30.9% were born in Q1, 25.7% in Q2, 23.8% in Q3 and 19.6% in Q4. The number of soccer players born in Q1 was lower in less competitive leagues. The number of players born in Q1 decreased as the level of competition decreased; the highest percentage of these players was observed in clubs that are among the top 50 ranked in UEFA or compete in the most prestigious European championships. The RAE was less pronounced in teams that participate in lower competitive championships. There was no significant difference in market value between players playing position and level of competition when born in different quarters. Although, the most expensive soccer players in the European championships were late-born forwards. Players of various groups differed in their market value. Conclusions: The RAE is currently prevalent in all the most competitive senior soccer leagues and teams in Europe regardless of playing position. There are no significant differences in market value between players of the same playing position and level of competition when born in different quarters. The most expensive soccer players in the European championships are forwards born in Q4. These findings may indicate that the under-representation of "late-born" soccer players in youth, and then consequently in adult soccer, is not associated with lower talent, but with other factors, possibly based on physiological characteristics and socio-cultural factors. Further measures are needed to mitigate the discriminatory effects of selection bias based on the RAE.
Children born in the first months of the same year are physically more advantageous than those born in the last months, and this advantage decreases as the age of the athletes gets older. Athletes born in the last months of the year and unsuccessful may leave their careers at a young age. The aim of the study was to examine the relationship between tournament success and birth months in 11 years old Freestyle and Greco-Roman style wrestlers. It was hypothesized that wrestlers born in the first months of the year would be more successful than those born in the last months. Competition ranking and birth date information of 327 wrestlers who participated in the 11 Years Old Men's Freestyle and Greco-Roman Style Wrestling Turkey Championship were used. In order to examine the relationship between athlete success and birth months, Chi-Square analysis was performed by grouping birth months into four quarters of the year. It was observed that the success rankings of both Freestyle and Greco-Roman style wrestlers decreased from the first quarter to the last quarter of the year (Freestyle: X2 = 42.749, df = 3, p = .000; Greco-Roman style: X2 = 25.627, df = 3, p = .000). It is thought that birth months should be given importance when grouping at young ages, especially in sports branches such as wrestling, where physical contact is high.
The relative age effect (RAE) is a selection bias resulting from the interaction between the selected dates and birthdates. Nevertheless, the impact of birthdate on the junior-to-senior transition in international track and field is unclear. This study aimed to quantify the RAE's magnitude and test if birthdate affects the junior-to-senior transition rate. The birthdate and performances of 5,766 sprinters (female: 51.0%) and 5,863 jumpers (female: 45.9%) were collected. Elite athletes (operationally defined as the World's all-time Top 200, 100 and 50 athletes) were identified according to Under 18 and Senior categories. Skewed quartile distributions were observed in the Under 18 (effect size ranged = 0.15-0.10) but not in the Senior category. RAE magnitude increased according to performance level (i.e., from Top 200 to Top 50) and was higher in males than females. Relatively younger athletes showed significantly higher transition rates with a higher chance of maintaining top level in the senior category (odds ratio (OR) ~ 1.64). The probability of maintaining success was lower for sprinters than jumpers (OR ~ 0.70), influenced by decade of birth and continental place but similar for male and female athletes. Data corroborate that relatively younger athletes are disadvantaged in the junior category but advantaged when transitioning to the senior category.
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The demand for creativity in team sports, and specifically in a highly unpredictable activity such as soccer, has generated great interest from academics and practitioners. Creative players can bring the unforeseeable into the game that can allow teams to keep an edge over their opponents and is considered a key element of performance. Current theoretical approaches highlight that nur- turing creativity in soccer should be encouraged throughout youth developmental stages; thus, practitioners must create an enriching and supportive environment for creativity to thrive. The development of creativity comprises long-term work on the part of the young player, coupled with the corresponding planning, implementation, and patience from practitioners. As such, the first part of this chapter frames the concept and presents comprehensive frameworks (e.g., the Tactical Creativity Approach, Memmert, 2013; Creativity Developmental Framework, Santos et al., 2016) to aid creative play to flourish. The second part of this chapter provides a detailed description of small-sided games and movement variability features to encourage exploratory be- haviour and complement soccer training tasks. Moreover, in this section, an overview of current evidence-based interventions is centrally discussed. The third part of this chapter offers a review across creativity training programmes, such as Skills4Genius (Santos et al., 2017) and The Crea- tive Soccer Platform (Rasmussen & Østergaard, 2016), to provide further guidance and strategies for soccer practitioners to design for creativity developmental outcomes. Lastly, in order to ad- vance this field of practice, considerations for researchers and practitioners are outlined.
Purpose: Given that previous research on relative age effects (RAEs) has only focused on organized sport, the aim of this exploratory study was to examine whether this phenomenon also existed among self-organized practitioners. In relation to that, a second aim was to know whether self-organized sport practices could be favored by late-born practitioners as a result of a strategic adaptation. Method(s): Representative sub-samples of 474 soccer players, 363 basketball players, 2,536 swimmers, 1,788 strength training practitioners, 1,873 pétanque players, 973 table tennis players and 2,136 runners were analyzed. Results: The results did not show any significant RAEs, including in sport practices that are sensitive to this phenomenon such as soccer or basketball. The results did not show any significant overrepresentation of late-born people either. Conclusion: This study suggests that self-organized sport practices are not impacted by the RAEs. This finding is interesting because self-organized sport practice is the most important one in numbers.
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This study aimed to clarify whether the relative age effects exist among Japan national athletes who participated in the Olympics and World Athletics Championship from 1991 to 2021 by discipline. We obtained 500 athletes' birth month from a website. The following disciplines were classified: short distance (sprints, hurdles, relays: n=168), middle-to long-distance (middle-distance, long-distance, marathon, walking race: n=252), jumping (n=52), throwing (n=24), and combined (n=7). Athletes' birth months were divided into four quarters: April–June, July–September, October–December, January–March. Chi-squared goodness of fit test and residual analysis were used to confirm biased distribution of birth month. The proportion of male short-distance athletes who were born between April and June was significantly higher, which may be due to advantage of physique (e.g., height and BMI). On the other hand, the proportion of male jumping athletes who were born between January and March was significantly higher, indicating that discipline transfer in their youth may have an influence on the relative age effect of jumping athletes. In conclusion, the relative age effect was seen among male short-distance and jumping athletes. These results suggest that athletic training systems in Japan have yet to mitigate the advantages caused by the relative age effect because young athletes' disciplines are selected too early, without careful consideration of the relative age effect.
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Background Relative age effect (RAE) is a concept related to the possible advantage that older athletes would have over younger ones within the same category. Although many studies have approached this subject in individual sports, there are few clippings by events within the sport. More detailed analyses are necessary for a better understanding of how RAE behaves in sports, especially in athletics, the subject of this study. The objective of this study was to analyze the RAE on speed in track and field events as a whole, separating the flat races from the hurdles races. Methods The Brazilian Ranking of Brazilian Athletics Confederation was used for data analysis, and the sample was composed of the 50 best-placed marks in the ranking of speed events in athletics in the categories Under(U)-16 and U-18 (female and male). Statistical analysis was calculated by chi-square, and the effect size was checked by Cramer’s V. Likelihood-ratio test (L-Ratio) assessed the probability of the RAE occurring in the total sample and by age groups. Results In the total sample the results pointed to the emergence of RAE in males in both categories (U-16: p < 0.001; V: 0.13; L-Ratio: 3.64, U-18: p < 0.001; V: 0.13; L-Ratio: 3.80), whereas in females no such effect was found in any category (U-16: p = 0.6; V: 0.09; L-Ratio: 0.09, U-18: p = 0.6; V: 0.07; L-Ratio: 0.12). When the results were separated by type of event, there was only a RAE in the shallow event in the U-18 female category (p = 0.3; V: 0.11; L-Ratio: 8.72). Conclusion The results allow us to conclude that there is a RAE in the speed trials of Brazilian athletics in the U16 and U18 categories for men, while this effect appears only in the shallow trials of the U18 category for women, indicating that the RAE has incidence when there is more participation and competition in the sport.
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Many disciplines of scholarship are interested in the Relative Age Effect (RAE), whereby age-banding confers advantages on older members of the cohort over younger ones. Most research does not test this relationship in a manner consistent with theory (which requires a decline in frequency across the cohort year), instead resorting to non-parametric, non-directional approaches. In this article, the authors address this disconnect, provide an overview of the benefits associated with Poisson regression modelling, and two managerially useful measures for quantifying RAE bias, namely the Indices of Discrimination and Wastage. In a tutorial-like exposition, applications and extensions of this approach are illustrated using data on professional soccer players competing in the top two tiers of the “Big Five” European football leagues in the search to identify paragon clubs, leagues, and countries from which others may learn to mitigate this form of age-discrimination in the talent identification process. As with OLS regression, Poisson regression may include more than one independent variable. In this way we test competing explanations of RAE; control for unwanted sources of covariation; model interaction effects (that different clubs and countries may not all be subject to RAE to the same degree); and test for non-monotonic versions of RAE suggested in the literature.
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The main objective of this research was to determine the existence of relative age effect (RAE) in five European soccer leagues and their second-tier competitions. Even though RAE is a well-known phenomenon in professional sports environments it seems that the effect does not decline over the years. Moreover, additional information is required, especially when taking into account second-tier leagues. Birthdates from 1,332 first-tier domestic players from France, England, Spain, Germany and Italy and birthdates from 1,992 second-tier domestic players for the 2014/2015 season were taken for statistical analysis. In addition to standard statistical tests, the data were analyzed using econometric techniques for count data using Poisson and negative binomial regressions. The results obtained confirmed a biased distribution of birthdates in favor of players born earlier in the calendar year. For all of the five first-tier soccer leagues there was an unequal distribution of birthdates (France χ2 = 40.976, P
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The relative age effect is a well-researched phenomenon, however there is still a dearth of understanding in track and field and female sport. This study investigated the role of relative age on selection for international competition of Spanish age group athletes between 2006-2014. Six hundred and forty two athletes competed for Spain at U20 or U18 age group international competition (n = 359 males; 283 females) across 9 years. The birthdates of these athletes were compared against the population of registered athletes at that time (14,502 males; 10,096 females). The results highlighted the influential role of relative age on selection to these opportunities. In line with previous research, this effect was mediated by age and gender, with stronger effects for both males and younger athletes (U18). The data best supported the 'maturation-selection' hypothesis as a mechanism for RAEs. These results highlight the need to carefully consider the role and need for international competitive opportunities at different age groups. A number of possible context relevant solutions are discussed , including correction adjustments techniques and competition structure within track and field.
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The relative age effect (RAE; i.e., an asymmetry in the birth distribution) is a bias observed in sport competitions that may favour relatively older athletes in talent identification. Therefore, the aim of this study was to investigate the presence of RAE in elite soccer players competing in the Italian championships, even considering the discriminations of younger and older Serie A players (in relation to the median age of the sample), and different positional roles (i.e., goalkeeper, defender, midfielder, forward) for each observed category. A total of 2051 players competing into the 2017–2018 Italian under-15 (n = 265), under-16 (n = 362), under-17 (n = 403), Primavera (n = 421) and Serie A (n = 600) championships were analysed. The birth-date distributions, grouped in four quartiles (i.e., January-March, Q1; April-June, Q2; July-September, Q3; October-December, Q4), were compared to a uniform distribution using Chi-squared analysis. The week of birth was analysed using Poisson regression. The results showed a large over-representation of players born in Q1 in all soccer player categories. However, the effect size of this trend resulted smaller as age increased. Individuals born in Q1 have about two-folds more chances to become a Serie A player compared to those born in Q4. The Poisson regression analysis showed that RAE was greater for defenders than for forwards among all categories. Therefore, a strongly biased selection emerged among elite soccer players competing in Italian championships, highlighting how young individuals born in the first three months have many more chances to become elite players compared to the others.
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Enhancing our understanding of athlete development would be valuable for coaches, parents and administrators to set realistic performance expectations and to advance youth sport policy. To this end, a database of track and field performances was examined. Records of 134,313 performances by athletes aged between 12 and 35 years in sprinting, throwing, jumping and middle distance events were analysed. Results revealed that a minority (Male, 9%; Female, 13%) of top 20 ranked senior athletes were also ranked in the top 20 at Under 13 (U13). These results were supported by the finding that a minority of athletes retained their top 20 ranking at subsequent age grades (36.3% U13-U15; 23% U13-U17; 13% U13-U20; 43.3% U15-U17; 22.1% U15-U20; 41.8% U17-U20). By U20, less than 30% of athletes who had been ranked in the top 20 at U13 were still listed on the national rankings. Examining a broader sample of athletes revealed weak to moderate correlations between performances at different age grades until at least Under 17-Under 20. These findings reinforce the message that excelling at youth level in competitive athletics is not a prerequisite for senior success.
Objectives: (1) Generate accurate estimates of the relationship between decimal age (i.e., chronological and relative) with swimming performance based on longitudinal data. (2) Determine whether corrective adjustment procedures can remove Relative Age Effects (RAEs) from junior/youth swimming. Design: Longitudinal and repeated years of cross-sectional performance data were examined. Methods: (1) Participants were 553 male 100 m Freestyle swimmers (10–18 years) who participated in ≥five annual events between 1999–2017. Growth curve modelling quantified the relationship between age and swimming performance, permitting corrective adjustment calculations. (2) Participants were N = 2141 male 100 m Freestyle swimmers (13–16 years) who swam at state/national events in 2015–2017. Relative age distributions for ‘All’ ‘Top 50%’ ‘25%’ and ‘10%’ of swimming times were examined based on raw and correctively adjusted swim times. Chi-square, Cramer's V and Odds Ratios (OR) determined whether relative age (quartile) inequalities existed according to age-groups, selection level and correctively adjusted swim times. Results: Based on raw swim times, for ‘All’ swimmers RAEs was evident at 13 and 14 years-old and dissipated thereafter. But, RAE effect sizes substantially increased with selection level, with large-medium effects between 13–15 years-old (e.g., 15 years — Top 50% Q1 v Q4 OR = 2.28; Top 10% = 6.02). However, when correctively adjusted swim times were examined, RAEs were predominantly absent across age-group and selection levels. Conclusions: With accurate longitudinal reference data, corrective adjustment procedures effectively removed RAEs from 100 m Freestyle swimming performance, suggesting the potential to improve swimming participation experience and performance evaluation.
Objectives: The aim of this study was to describe and analyse the performance career trajectories for Italian athletes that participated in sprint, hurdles, discus throw, and shot-put athletics events. Design: Retrospective study, data collected between 1994 and 2014. Methods: A total of 5929 athletes (female: n=2977, 50.2%) were included in the study. The age of entering competition and personal best performance was identified in the official competition records. Personal best performances were ranked in percentiles and top-level athletes were considered those in the highest 4% of the performance distribution. Results: Overall, when controlling for the age of entering competition, top-level athletes reached their personal best later (i.e., around 23-25 years old) for all events compared to the rest of the athletes. Moreover, regression analysis showed that entering competitions later was linked to better performances during adulthood. Also, only 17%-26% [90% CI] of the top-level adult athletes were considered as such when they were 14-17 years old. Conclusions: Together, these findings suggest that early sport success is not a strong predictor of top-level performance at senior level. Entering sport-specific competitions later and lengthening the sports career at beyond 23-25 years of age may be important factors to reach top-level performance in sprint and throwing events.
Objectives: The main and interactive effects of biological maturity status and relative age upon self-regulation in male academy soccer players are considered. Consistent with the ‘underdog’ hypothesis, whereby relatively younger players may benefit from competitive play with older peers, it was predicted later maturing and/or relatively younger players would report more adaptive self-regulation. Design: Cross-sectional study. Method: Players (n = 171, aged 11–16 years) from four English professional soccer academies completed the modified Soccer Self-Regulation Scale. Date of birth, height, weight and parental height were obtained. Relative age was based on birth quarter for the selection year. Maturity status was based upon percentage of predicted adult height attained. Results: Linear regression models showed later maturation was inversely associated with adaptive self-regulation, while relative age was unrelated to self-regulation. Conclusions: In partial support of the underdog hypothesis, later maturing players appear to possess a psychological advantage.
Purpose: The talent identification and selection process in young male soccer players is mainly focused on anthropometrics and physical performance, but social factors are also considered in this process. The purpose of this study was to test the existence of the relative age effect and its possible influence on anthropometrics and physical performance and to analyze coaches' efficacy expectations. Method: Data for 564 young male soccer players (Mage = 13.7 ± 1.5 years; Mweight = 53.7 ± 11.6 kg; Mheight = 160.2 ± 11.6 cm) included their birth quartile, maturity status, anthropometrics, a physical test battery, and coaches' efficacy expectations. Results: Early-born players were overrepresented (p < .05). Early-born players were not statistically taller, heavier, or better at physical performance (p > .05) when maturation and chronological age were controlled as confounding factors. However, coaches expected more from early-born players (p < .05), and the inferential analysis showed likely to very likely worthwhile differences between the coaches' expectations for players born in the first quartile of the year and those born in the fourth quartile of the year. Conclusions: Anthropometrical and physical performance variables were not affected by birth quartile, and coaches' efficacy expectations were related to the relative age effect.
Objectives: To identify the influence of age-policy changes on the relative age effect (RAE) across the Australian Football League (AFL) talent pathway. Design: Retrospective cross-sectional analysis of junior AFL players attending the National Draft (National), State, and State Under 16s (U16) combines between 1999–2016. Methods: Birth-date data was obtained for players attending the AFL State U16 (n = 663, age: 15.9 ± 0.4 years), State (n = 803, age: 19.1 ± 1.7 years), National (n = 1111, age: 18.3 ± 0.8 years) combines. Corresponding aged-matched Australian general population birth rate data was also collected. Results: A chi-squared analysis comparing birth month distributions found all Combine groups differed significantly from the general population (Under 16s: χ2 = 62.61, State: χ2 = 38.83, National: χ2 = 129.13, p < 0.001). Specifically, Under 16s had greater birth frequencies for months January to March (≥2%, p < 0.05), with more State players born in January (4.9%, p < 0.05). Age-policy changes at the National level reduced birth distribution bias for some months, however the RAE remained for March, June and July (3.9%, 6.1%, 4.3%, p < 0.05). State U16s and National players had 2–9% lower birth frequencies for November–December births compared general population. Conclusions: Selection bias exists towards older players is present at the AFL’s State U16, and is maintained at State and National level combines. Age-policy changes are only partially successful at addressing the RAE at the National level, with alternative strategies also recommended in order to address the RAE across the AFL talent pathways. Keywords: talent identification; development; recruitment; selection bias; team sport; birth date distribution