Relative Age Influences Performance of World-Class Track and Field Athletes Even in the Adulthood

Abstract

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.

Full text

ORIGINAL RESEARCH
published: 18 June 2019
doi: 10.3389/fpsyg.2019.01395
Frontiers in Psychology | www.frontiersin.org 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,
Spain
Michael Romann,
Swiss Federal Institute of Sport
Magglingen SFISM, Switzerland
*Correspondence:
Corrado Lupo
corrado.lupo@unito.it
Specialty section:
This article was submitted to
Movement Science and Sport
Psychology,
a section of the journal
Frontiers in Psychology
Received: 17 April 2019
Accepted: 29 May 2019
Published: 18 June 2019
Citation:
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
INTRODUCTION
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.
MATERIALS AND METHODS
Design
Data were collected from the publically available web-site of
IAAF (International Association of Athletics Federations; https://
www.iaaf.org/home). 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.
Procedure
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 (WB−0.5) 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 e−b1 (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
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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 p≤0.05.
RESULTS
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.
DISCUSSION
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
OVERALL
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
100-M
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
110-M HURDLES
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
200-M
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
400-M
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
400-M HURDLES
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
800-M
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
1,500-M
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
3,000-M STEEPLECHASE
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
(Continued)
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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
5,000-m
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
HIGH JUMP
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
POLE VAULT
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
LONG JUMP
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
TRIPLE JUMP
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
SHOT PUT
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
DISCUS THROW
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
HAMMER THROW
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
JAVELIN THROW
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.
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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 | www.frontiersin.org 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.
CONCLUSION
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.
DATA AVAILABILITY
The datasets generated for this study are available on request to
the corresponding author.
AUTHOR CONTRIBUTIONS
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.
FUNDING
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
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Frontiers in Psychology | www.frontiersin.org 9June 2019 | Volume 10 | Article 1395