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Relationship between the relative age effect and anthropometry, maturity and performance in young soccer players

Abstract

Abstract The presence of the relative age effect (RAE) has been widely reported; however, its underlying causes have not yet been determined. With this in mind, the present study examined if anthropometry and performance were different amongst older and younger soccer players born in the same year. Eighty-eight young soccer players participated in the study (age 9.75 ± 0.30). Anthropometric measurements, physical tests (sprint, agility, endurance test, jump and hand dynamometry) and the estimation of the maturity status were carried out. Most players (65.9%) were born in the first half of the year. Older players were taller (P < 0.05), had longer legs (P < 0.01) and a larger fat-free mass (P < 0.05). Maturity offset was smaller in the older boys (P < 0.05); however, age at peak height velocity was similar. Older boys performed better in velocity and agility (P < 0.05) and particularly in the overall score of performance (P < 0.01). Stepwise regression analysis revealed that chronological age was the most important variable in the agility test and the overall score, after the skinfolds (negative effect). We report differences in anthropometry and physical performance amongst older and younger pre-pubertal soccer players. These differences may underlie the RAE.
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Relationship between the relative age effect and
anthropometry, maturity and performance in young
soccer players
Susana Maria Gil a , Aduna Badiola a , Iraia Bidaurrazaga-Letona a , Jon Zabala-Lili a , Leyre
Gravina b , Jordan Santos-Concejero a , Jose Antonio Lekue c & Cristina Granados a
a Department of Physical Education and Sport , University of the Basque Country , (UPV/
EHU), Vitoria-Gasteiz , Spain
b School of Nursing , University of Basque Country , (UPV/EHU), Leioa , Spain
c Medical Services , Athletic Club de Bilbao , Lezama , 48195 , Spain
Published online: 20 Sep 2013.
To cite this article: Susana Maria Gil , Aduna Badiola , Iraia Bidaurrazaga-Letona , Jon Zabala-Lili , Leyre Gravina , Jordan
Santos-Concejero , Jose Antonio Lekue & Cristina Granados , Journal of Sports Sciences (2013): Relationship between the
relative age effect and anthropometry, maturity and performance in young soccer players, Journal of Sports Sciences, DOI:
10.1080/02640414.2013.832355
To link to this article: http://dx.doi.org/10.1080/02640414.2013.832355
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Relationship between the relative age effect and anthropometry,
maturity and performance in young soccer players
SUSANA MARIA GIL
1
, ADUNA BADIOLA
1
, IRAIA BIDAURRAZAGA-LETONA
1
, JON
ZABALA-LILI
1
, LEYRE GRAVINA
2
, JORDAN SANTOS-CONCEJERO
1
, JOSE ANTONIO
LEKUE
3
, & CRISTINA GRANADOS
1
1
Department of Physical Education and Sport, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain,
2
School of Nursing, University of Basque Country (UPV/EHU), Leioa, Spain, and
3
Medical Services, Athletic Club de
Bilbao, Lezama 48195, Spain
(Accepted 2 August 2013)
Abstract
The presence of the relative age effect (RAE) has been widely reported; however, its underlying causes have not yet been
determined. With this in mind, the present study examined if anthropometry and performance were different amongst older
and younger soccer players born in the same year. Eighty-eight young soccer players participated in the study (age
9.75 ± 0.30). Anthropometric measurements, physical tests (sprint, agility, endurance test, jump and hand dynamometry)
and the estimation of the maturity status were carried out. Most players (65.9%) were born in the rst half of the year. Older
players were taller (P< 0.05), had longer legs (P< 0.01) and a larger fat-free mass (P< 0.05). Maturity offset was smaller in
the older boys (P< 0.05); however, age at peak height velocity was similar. Older boys performed better in velocity and
agility (P< 0.05) and particularly in the overall score of performance (P< 0.01). Stepwise regression analysis revealed that
chronological age was the most important variable in the agility test and the overall score, after the skinfolds (negative
effect). We report differences in anthropometry and physical performance amongst older and younger pre-pubertal soccer
players. These differences may underlie the RAE.
Keywords: RAE, date of birth, performance test, soccer, performance
Introduction
Most sports systems group athletes according to their
chronological age. Thus, a selected date of birth is
used to group children into age-specicteams.This
particular date of birth, often known as the cut-off date,
is the 1st of January in most European countries. In
these countries, the majority of sports teams are made
up of participants born between the 1 January and
31 December of the same year, but occasionally span-
ning two consecutive years. Thus, a child born at the
beginning of a given year will be almost 12 months
older than another one born at the end of the same
year. Nevertheless, they will play sports together.
The term relative age refers to a personsagerelative
to that of his/her peers within the same annual group.
This characteristic depends on the date of birth relative
to the selection data used to place a child in a specic
age group (Wattie, Cobley, & Baker, 2008). The varia-
tions in age within an annual age group have been
referred to as relative age differences, and its
consequence as the relative age effect (RAE) (Wattie
et al., 2008). Barnsley, Thompson, and Barnsley
(1985) and, at about the same time, Grondin,
Deshaies, and Nault (1984) described for the rst
time RAE in sport among Canadian ice hockey players.
Since these initial reports, several studies have reported
the presence of RAE in many sports, particularly hockey
and soccer (Cobley, Baker, Wattie, & McKenna, 2009;
Musch & Grondin, 2001; Wattie et al., 2008).
It has been well documented that RAE is more
relevant in high-level teams. The date of birth of
3650% of soccer players was within the rst
3 months of the year, whereas about 417% was
within the last 3 months in selected players
(Carling, le Gall, Reilly, & Williams, 2009; Gil,
Ruiz, Irazusta, Gil, & Irazusta, 2007; Gravina et al.,
2008) and in Under-15 (U-15) to Under-18 inter-
national players (Augste & Lames, 2011; Helsen,
Van Winckel, & Williams, 2005; Williams, 2010).
Correspondence: Susana Maria Gil, Department of Physical Education and Sport, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain.
E-mail: susana.gil@ehu.es
Journal of Sports Sciences, 2013
http://dx.doi.org/10.1080/02640414.2013.832355
© 2013 Taylor & Francis
Downloaded by [Universidad Del Pais Vasco] at 06:12 20 September 2013
In contrast, the presence of RAE in teams with
lower levels of soccer skills is not obvious (Cobley
et al., 2009). The birth-date distribution of regional
youth soccer and school youth players has been
reported to be different to that of the general popu-
lation (Mujika et al., 2009). Nevertheless, the differ-
ences in these groups were moderate, whereas the
odds ratios were very high in the elite youth soccer
group. In the same way, Wattie et al. (2012) did not
nd RAE in a cohort of 1186 recreational soccer
players aged 1120 years.
Not only skill level, but also age seems to be
important in RAE. Thus, RAE appears to progres-
sively increase with age from the child category to
adolescence (Cobley et al., 2009). Most studies have
conrmed this effect in players aged 1314 and
older, but studies undertaken in younger players
have shown inconsistent results. In this sense, the
birth-date distribution was found to be uniform in a
group of players aged 610, whereas it was not so in
the groups of players aged 1216 years (Helsen,
Starkes, & Van Winckel, 1998). On the other hand,
Delorme, Boiché, and Raspaud (2010b) found a
signicant RAE in all young age categories of
French male soccer (i.e. from 7 years to 18 years).
Therefore, this effect seems to appear early.
Physical and physiological growth and maturation
have been hypothesised several times as the under-
lying cause of RAE (Brewer, Balsom, Davis, &
Ekblom, 1992; Carling et al., 2009; Cobley et al.,
2009; Musch & Grondin, 2001), but conclusive
results are scarce. In recent studies of basketball,
players born in the rst 3 months of the year were
found to be taller compared to those born towards
the end of the year (Delorme & Raspaud, 2009;
Torres-Unda et al., 2013). These results are con-
sistent with the idea that tallness is an important
competitive advantage for success in basketball
(Torres-Unda et al., 2013). However, the inuence
of body size on the overrepresentation of soccer
players born in the rst months of the year has
not been conrmed. Thus, Carling et al. (2009)
found that players born during the rst quarter of
the year were taller than younger players in a group
of highly selected elite players. In contrast, Malina,
Ribeiro, Aroso, and Cumming (2007) and also
Deprez, Vaeyens, Coutts, Lenoir, and Philippaerts
(2012) did not nd anthropometrical differences
between older and younger players. Moreover,
Hirose (2009) observed that anthropometrical vari-
ables were similar among Under-10 soccer players,
but, in the U-11 to U-14 groups, older players were
indeed taller and heavier.
In the light of conicting ndings regarding the
presence of RAE in young soccer players, we set out
to determine if RAE was present in a group of young
non-elite soccer players. To this end, we selected
young boys aged 910 and evaluated the hypothesis
that RAE would be also present in soccer players of
young age. We evaluated the hypothesis that players
born in the beginning of the year would have some
physical advantages and would also have better per-
formances than their younger peers, by comparing
anthropometric variables, maturity-related variables
and physical test performance amongst players of
different relative ages born in the same year.
Methodology
Participants
The sample included 88 soccer players from soccer
clubs in the Bizkaia province. They voluntarily par-
ticipated in the study. Players were born in 2001 and
measurements were undertaken in MarchApril
2011. All players trained twice a week (11.5 h of
training · day) and played a match during the week-
end. They all played in the same county league.
A written informed consent was received from all
players and parents after verbal and written explana-
tion of the experimental design and potential risks of
the study. The Ethics committee of the University of
Basque Country for Research on Human Subjects
approved this study. The measurements were per-
formed according to the ethical standards of the
Helsinki Declaration
The date of birth of the participants was divided in
4 quarters: 1 January31 March, 1 April30 June, 1
July30 September and 1 October31 December.
Chronological age was transformed into decimal
age (in years) and the 75th, 50th and 25th percen-
tiles were calculated. According to these percentiles,
4 groups of players were obtained, one for each age
quartile: Q1 = players whose age was above 75th
percentile (they were the eldest), Q2 = players with
an age between 75th and 50th percentile,
Q3 = players with an age between 50th and 25th
percentile and Q4 = players with an age below 25th
percentile (the youngest).
Protocol
Measurements were taken under the same external
conditions; for the anthropometric measurements,
players only wore shorts and for the performance
tests they wore shorts, T-shirt and soccer boots,
except from the jump test during which they wore
running shoes.
The following tests were carried out on all players
in the same sports hall at the same time of the day
and in the same order:
Anthropometric measurements. Height, sitting height
(Añó Sayol, Barcelona, Spain) and body mass
2S. M. Gil et al.
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(Seca, Bonn, Germany) were measured. Leg length
and the ratio between leg length and sitting height
were calculated. The body mass index (BMI) was
calculated from height and body mass (kg · m
2
).
Skinfold thicknesses (measured in mm) were mea-
sured at 6 sites (triceps, subscapular, abdominal,
suprailiac, thigh and calf) using a skinfold caliper
(Harpenden, England) and the sum of these 6 mea-
surements was calculated (sum of skinfolds). Fat-
free mass (in kg) was estimated using the equation
proposed by Slaughter et al. (1998). All measure-
ments were taken following the guidelines outlined
by the ISAK (International Society for the
Advancement of Kinanthropometry) by the same
researcher. The technical error of measurement was
less than 0.5% for height and weight and within the
range of 2.44.5% for the skinfolds. The coefcients
of variation for the performance tests used in this
study ranged from 1.2% to 5.5%
Maturity. The time before peak height velocity,
labelled maturity offset, was predicted using a for-
mula (Malina, Coelho E Silva, Figueiredo, Carling,
& Beunen, 2012; Mirwald, Baxter-Jones, Bailey, &
Beunen, 2002). Predicted age at peak height velocity
in years was estimated as chronological age minus
maturity offset (Malina et al., 2012).
Velocity test. In the sports hall, on articial turf, soc-
cer players performed a 30 m at sprint test.
Agility test. Players performed 30 m sprint tests
which were similar to the velocity tests, but in the
agility tests 10 cones were positioned aligned with a
distance of 3 m between each consecutive cone.
Footballers had to run dodging the cones on the
left and right consecutively, or vice versa.
In both, the velocity and the agility tests, running
times were measured using electronic timing lights
(Polifemo, Microgate, Italy) positioned at 15 m and
30 m. The starting position of the players was stand-
ing up, 2 m before the rst timing light.
Yo-yo intermittent recovery test (Yo-yo IR1). The
Yo-yo intermittent recovery test (Level 1) was per-
formed by all participants. Players run until they
could not keep pace and the covered distance was
measured in meters.
Jump test. In order to measure the explosive power of
the lower extremities, participants performed a
countermovement jump. The height (cm) of each
jump was measured using an optical measurement
system (Optojump, Microgate, Italy).
Hand dynamometry test. To measure the strength of
the upper extremities, soccer players performed a
handgrip test. They squeezed the dynamometer
(Jamar, Bolingbrook, IL, USA) with a maximum
isometric effort for 5 s. Maximum strength was regis-
tered (kp).
Except from the Yo-yo IR1 test, each test was
performed 3 times and for statistical analysis, the
best performance of each was used.
Statistics
A chi-square goodness-of-t test was used to assess
differences between the distributions of observed and
expected birth dates. The distribution of expected
birth dates was based on the distribution of live births
in the same county and also in the whole country in
2001 (National Institute of Statistics), following pre-
vious studies (Delorme, Boiché, & Raspaud, 2010a).
Anthropometric measurements and data from the
performance tests were analysed and compared
amongst the groups of players of the 4 age quartiles.
Data was displayed as mean ± s.
To identify signicant differences in all the vari-
ables among the players born in different quartiles, a
Studentst-test or a MannWhitney U-test was per-
formed. To measure the effect size, Cohensdand
the effectsize correlations were evaluated.
Threshold values for effect size statistics were 0.2,
0.5 and 0.8 for small, medium and large effect sizes,
respectively (Cohen, 1988).
In order to measure the overall performance, the
results of each test were transformed into z-scores
and summed up to make a total score of performance
(SCORE). First, the results of the velocity and agility
tests were calculated in m · s
1
instead of seconds,
because lower times in these tests mean better perfor-
mance. Thus the SCORE was as follows: velocity
(30 m) + agility (30 m) + Yo-yo IR1 + countermove-
ment jump. Moreover, the SCORE was calculated
with and without summing up the result of the hand-
grip test (SCORE
+HG
or SCORE, respectively),
because strength of the upper extremity may not be
directly related to performance in soccer.
To analyse the relevance of chronological age in
the performance, a multiple stepwise regression ana-
lysis was performed. The dependent variables were
as follows: velocity 30 m, agility 30 m, Yo-yo IR1,
countermovement jump, handgrip test, SCORE and
SCORE
HG
. The independent or predictor variables
included were as follows: chronological age, height,
sum of skinfolds, fat-free mass and maturity offset.
The level of signicance was set at P< 0.05.
Statistical analyses of data were performed using
the Statistical Package for the Social Sciences 17.0
software package (SPSS, Chicago, IL, USA).
Results
The distribution of birth dates is shown in Table I
and Figure 1. The observed distribution differs
RAE in young soccer players 3
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signicantly from the theoretical distribution
expected (P< 0.05). Of all the participants,
65.91% were been born in the rst half of the year.
Furthermore, there was a predominance of players
born in the rst quarter (34.09%).
Mean age of the participants was 9.75 ± 0.30 years
and the range was 9.1610.37 years. Percentiles were
distributed as follows: percentile 25th 9.54 years, per-
centile 50th 9.77 years and percentile 75th 10.00 years.
The mean chronological age of players of the rst
quartile was 10.15 ± 0.10 years, second: 9.89 ± 0.06
years, third: 9.68 ± 0.07 years and fourth:
9.33 ± 0.10 years. Differences among the 4 groups
were statistically signicant (P< 0.001).
Players born in Q1 were signicantly taller in
terms of both standing and sitting heights
(P< 0.05) (Table II). Lower extremities were also
longer in the boys born in the rst quartile
(P< 0.01). Also, maturity offset of players born in
Q1 was smaller (P< 0.05, large effect size).
Older players performed better in the velocity tests
at 15 m (P< 0.01) and 30 m (P< 0.01) and in the
agility tests at 15 m (P< 0.01) and 30 m (P< 0.05),
(Table III).
The overall performance of Q1 players was signif-
icantly better than that of players of Q4. Thus, older
players had a higher SCORE with the handgrip test
and without the handgrip test (P< 0.01).
Older players also performed better in the Yo-yo
IR1 and the countermovement jump but the differ-
ences were not statistically signicant. However, the
effect size was medium to large.
In order to investigate the inuence of the differ-
ent variables in performance, we carried out a multi-
ple regression analysis (Table IV). The different
entered predictors accounted for 58% of variability
in the 30 m velocity test, 27% of variability in the
30 m agility test, 33% of variability in the Yo-yo IR1,
27% of variability in the countermovement jump
test, 25% of variability in the handgrip test, 59% of
Table I. Quarterly distribution of the birth dates of soccer players compared to the number of births in the same county and in the whole
country in the year 2001.
Quarter of birth
Soccer players First Second Third Fourth Total χ
2
P
Number 31 27 15 15 88
% 35.23 30.68 17.05 17.05
Expected 21 23 22 22
County 10.18 0.017
Number 2174 2293 2284 2289 9040
% 24.05 25.37 25.37 25.32
Spain 10.87 0.012
Number 97,503 100,825 103,772 104,280 406,380
% 23.99 24.81 25.54 25.66
16
14
12
10
8
Percentages (%)
6
4
2
0
January February March April May June July Au
g
ust September October November December
Soccer players
Spain
County
Figure 1. Distribution of the birth dates (percentage per month) of the soccer players, the population of the same county and the population
of Spain (births in 2001).
4S. M. Gil et al.
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Table II. Anthropometric variables, maturity offset and estimated age at peak height velocity in soccer players of the rst (Q1), second (Q2),
third (Q3) and fourth (Q4) age quartiles.
Q1 (n= 22) Q2 (n= 22) Q3 (n= 22) Q4 (n= 22) Cohensd(Q1Q4)
Effectsize
correlation (r)
Weight (kg) 35.26 ± 5.87 34.15 ± 4.69 33.41 ± 5.71 32.85 ± 5.00 0.442 0.215
Height (cm) 140.85 ± 5.58* 140.45 ± 6.52 139.93 ± 7.15 137.13 ± 4.51 0.733 0.344
Sitting Height (cm) 74.74 ± 3.48* 73.80 ± 2.66 73.20 ± 3.57 73.01 ± 2.13 0.599 0.287
Leg length (cm) 66.10 ± 2.81** 66.64 ± 4.23 66.72 ± 4.21 64.11 ± 2.99 0.685 0.324
LL/SH 88.53 ± 3.67 90.25 ± 3.80 91.17 ± 4.31 87.81 ± 3.52 0.200 0.099
BMI (kg · m
2
) 17.70 ± 1.98 17.25 ± 1.49 16.93 ± 1.46 17.40 ± 1.94 0.153 0.070
skinfolds (mm) 46.57 ± 24.77 52.35 ± 19.61 55.12 ± 19.65 58.13 ± 29.81 0.421 0.206
Fat-free mass (kg) 29.54 ± 3.32* 28.71 ± 3.00 27.96 ± 3.64 27.35 ± 2.78 0.715 0.336
Mat. offset (years) 3.50 ± 0.36* 3.67 ± 0.28 3.79 ± 0.36 3.93 ± 0.23 1.423 0.579
APHV (years) 13.72 ± 0.30 13.69 ± 0.25 13.64 ± 0.32 13.52 ± 0.18 0.323 0.579
Notes: Sum of skinfolds, triceps, subscapular, abdominal, suprailiac, thigh and calf; LL/SH, ratio leg length/sitting height; BMI, body mass
index; skinfolds, sum of skinfolds; Mat., maturity; APHV, estimated age at peak height velocity.
Studentst-test: *P< 0.05: differences versus Q4, **P< 0.01: differences versus Q4.
Table III. Velocity, agility, endurance, jump, handgrip tests and also scores of performance tests in soccer players of the rst (Q1), second
(Q2), third (Q3) and fourth (Q4) age quartiles.
Q1 Q2 Q3 Q4
Cohensd
(Q1Q4)
Effectsize
correlation (r)
Velocity (s)
15 m 2.66 ± 0.18** 2.72 ± 0.23 2.76 ± 0.22 2.88 ± 0.27 0.580 0.432
30 m 5.07 ± 0.27** 5.16 ± 0.38 5.20 ± 0.35 5.37 ± 0.42 0.849 0.391
Agility (s)
15 m 2.97 ± 0.25** 3.03 ± 0.25 3.04 ± 0.22 3.19 ± 0.24 0.897 0.409
30 m 5.89 ± 0.38* 5.98 ± 0.43 6.05 ± 0.35 6.24 ± 0.42 0.873 0.400
Yo-yo IR1 (m) 626.66 ± 285.49 616.36 ± 241.15 514.54 ± 198.39 470.47 ± 216.85 0.616 0.294
CMJ (cm) 29.56 ± 3.13 28.70 ± 3.59 27.55 ± 3.09 27.10 ± 3.25 0.771 0.359
HG (kp) 19.18 ± 2.53 18.33 ± 3.32 17.81 ± 2.64 18.00 ± 3.22 0.407 0.199
SCORE 1.70 ± 3.32** 0.68 ± 3.47 0.40 ± 2.66 1.57 ± 2.89 1.030 0.465
SCORE
HG
2.03 ± 3.55** 0.89 ± 3.98 0.57 ± 2.79 1.66 ± 3.41 1.060 0.468
Notes: Yo-yo IR1, Yo-yo intermittent recovery test (Level 1); CMJ, countermovement jump test; HG, handgrip test (hand dynamometry);
SCORE: velocity 30 m + agility 30 m + Yo-yo IR1 + CMJ, with (SCORE
HG
) and without (SCORE) the handgrip test, respectively.
Studentst-test: *P< 0.05: differences versus Q4, **P< 0.01: differences versus Q4.
Table IV. Multiple stepwise regression analysis of the performance variables and the selected predictor variables.
Dependent variable Predictor variables R
2
R
2
change F(sig) df
Velocity 30 m SkF 0.292 0.292 33.397*** 1.81
SkF, FFM 0.424 0.132 29.398*** 2.80
SkF, FFM, CA 0.481 0.068 24.419*** 3.79
SkF, FFM, CA, Mat. offset 0.583 0.102 27.315*** 4.79
Agility 30 m SkF 0.180 0.180 17.818*** 1.81
SkF, CA 0.273 0.093 15.047*** 2.80
Yo-yo IR1 SkF 0.258 0.258 28.136*** 1.81
SkF, FFM 0.332 0.074 19.885*** 2.80
CMJ SkF 0.188 0.188 18.513*** 1.80
SkF, FFM 0.276 0.088 15.070*** 1.79
HG FFM 0.255 0.255 29.101*** 1.85
SCORE
30 m
SkF 0.338 0.338 40.829*** 1.80
SkF, CA 0.470 0.132 34.985*** 2.79
SkF, CA, FFM 0.511 0.059 29.169*** 3.78
SkF, CA, FFM, Mat. offset 0.596 0.067 28.373*** 4.77
SCORE
30 m + HG
SkF 0.246 0.246 25.800*** 1.79
SkF, FFM 0.452 0.205 32.117*** 2.78
SkF, FFM, CA 0.506 0.054 26.267*** 3.77
SkF, FFM, CA, Mat. offset 0.570 0.065 25.228*** 4.76
Notes: Yo-yo IR1, Yo-yo intermittent recovery test (Level 1); CMJ, countermovement jump test; HG, handgrip test (hand dynamometry); CA,
chronological age; Ht, height; SkF, sum of skinfolds (triceps, subscapular, abdominal, suprailiac, thigh and calf); FFM, fat-free mass; Mat., maturity.
***P< 0.001.
RAE in young soccer players 5
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variability in the SCORE and 57% of variability in
the SCORE
HG
.
Regarding the inuence of age, this parameter
accounted for 6% of variability in the velocity test,
9% of variability in the 30 m agility test, and 13%
and 5% of variability in the SCORE and the
SCORE
HG
, respectively.
Discussion
This study examined the prevalence of RAE in a
group of young soccer players and its relationship
to anthropometric characteristics, physical perfor-
mance and maturity-related parameters. It has been
suggested that RAE is due to a bigger body size and
due to an advanced physical maturity and also due to
the advantage in performance in the older players.
Thus, RAE has been widely reported, but its under-
lying causes largely remain a matter of speculation.
In the present study, we found an overrepresenta-
tion of players born at the beginning of the year,
thereby conrming the presence of RAE in this
group. In fact, almost 66% of the players were born
in the rst half of the year, while the birth percen-
tages for the same year (2001) of the general popula-
tion were equally distributed. These results are
consistent with previous studies in which school
soccer players were found to exhibit a slightly biased
distribution (54.4% vs. 45.6%, rst semester vs. sec-
ond semester, respectively) (Mujika et al., 2009) and
also with the results of Deprez et al. (2012) who
reported that 64% of players of the Under 1011
group were born in the rst semester. Furthermore,
this skewness was not as evident as that found in
higher level and elite players (Augste & Lames,
2011; Carling et al., 2009; Gil et al., 2007; Gravina
et al., 2008; Helsen et al., 2005; Williams, 2010).
Several studies have attributed RAE to physical
advantages (Brewer et al., 1992; Carling et al.,
2009; Cobley et al., 2009; Musch & Grondin,
2001). In the present study, we have found that
players born in the rst quartile were taller and had
longer legs. Similarly, Carling et al. (2009), Hirose
(2009) and Torres-Unda et al. (2013) found that
players born at the beginning of the year were sig-
nicantly taller. In contrast, Malina et al. (2007) and
Deprez et al. (2012) did not nd statistically signi-
cant differences in the body size of soccer players
born during the rst three months. Nevertheless, in
both studies, older players tended to be taller and
heavier than younger players.
Physical growth is a continuous process which
occurs during the years of infancy, childhood and
puberty until adult stature is reached. Consequently,
boys born earlier in the year can be up to 1012
months longer undergoing this growth process than
those born towards the end of the year, thus
acquiring a physical advantage. In the present
study, the difference in height between players born
in Q1 and Q4 was around 3.7 cm, which is consis-
tent with normal growth at this age (i.e. ~3 cm every
6 months).
Interestingly in this study, players of the rst age
quartile exhibited better results in the velocity and
agility tests (both 15 m and 30 m). Also, although
not statistically signicant, superior values were
reported in the Yo-yo IR1 and countermovement
jump in players born in the rst quartile (Cohens
d= 0.616 and 0.772, respectively). Moreover, they
exhibited a better overall performance, as indicated
by statistically better SCORE results with and with-
out the handgrip test.
Carling et al. (2009) carried out a study of 160
young elite soccer players to compare the anthropo-
metric and tness characteristics of boys born during
each of the 4 birth quarters. The authors did not nd
signicant differences across any of the measured
performance characteristics. Nevertheless, there was
a trend for the older players to outperform the
younger players in most of the tness tests.
In the same way, Malina et al. (2007) analysed the
physical performance of 69 soccer players. The study
of a subsample of players aged 14 showed no clear
trend in experience, size, functional capacities and
composite skill score. In this group, most players
were nearing maturity or already mature.
Therefore, the authors concluded that the homoge-
neous results probably reected low pubertal varia-
tion within the sample due to maturity having been
reached.
Regarding endurance, recently Deprez et al. (2012)
specically analysed the relationship between the Yo-yo
IR1 and RAE in soccer players aged 1019. These
authors did not nd any signicant differences between
the 4 birth quarters in any of the age groups. The
authors suggested that the lack of differences was due
to the fact that players had gone through a selection
process which had produced a homogeneous sample
of players, as commented on by Carling et al. (2009).
In the present study, we did not nd statistically signi-
cant differences in the Yo-yo IR1. Nevertheless, slightly
superior values (15%) for the endurance test were
recorded for players born in Q1 (P>0.05,medium
effect size) demonstrating that the players in our study
were more heterogeneous than those who had gone
through a selection programme.
Similarly, Torres-Unda et al. (2013) observed that
basketball players (aged 1314 years) born in the
rst semester were taller and heavier. These players
had a lower heart rate after the endurance test, and
they also performed better in the countermovement
jump and the dribbling tests. Moreover, they had a
higher score in the point average.
6S. M. Gil et al.
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Of all the variables included in the models of the
stepwise regression analysis of the performance tests,
the most important variable was the amount of body
fat, probably due to its negative effect on perfor-
mance. Also, fat-free mass was a signicant predictor
of velocity, the endurance test and the jump test. A
major component of fat-free mass is skeletal muscle,
the major work-performing tissue of the body, and
one would expect a positive association between fat-
free mass and performance (Malina, Bouchard, &
Bar-Or, 2004). Moreover, fat-free mass was the
only predictor of the handgrip test. It is interesting
to note that in the overall SCORE, the second most
important variable changed from being chronologi-
cal age to fat-free mass when the handgrip test was
included, reinforcing the inuence of fat-free mass
on handgrip test strength, and subsequently on the
SCORE
30 m+HG
. This coincides with the nding
reported by Tonson, Ratel, Le Fur, Cozzone, and
Bendahan (2008) that the maximal isometric
strength exerted by the forearm muscles in humans
is proportional to their size irrespective of age. Fat
free-mass is particularly related to static strength as
might be expected from the association between
strength and the cross-sectional area of a muscle
(Malina et al., 2004).
After the amount of body fat, the second most
important variable in the agility test and the SCORE
was chronological age. It accounted for 9% and 13%
of the variability, respectively. Neuromuscular
maturation may play an important role in motor per-
formance during childhood, as suggested by Malina
et al. (2004). As a consequence, chronological age,
due to its inuence on the maturation of the nervous
system, may have an important inuence on more
complex tasks in which coordination is important,
such as in the agility test and the overall SCORE,
independently of body size. In this sense, Sherar,
Baxter-Jones, Faulkner, and Russell (2007) observed
chronological age as a more important predictor than
age at peak height velocity and body mass during the
rst selection during a selection process of hockey
players. Besides, 77.5% of the boys selected at the
nal selection were born in the rst 6 months of the
year implying a close relationship between perfor-
mance and chronological age.
Older players were around 3.5 years from their
maturity offset, whereas younger players were almost
4 years. Thus, older players were closer to puberty.
This result is in agreement with Hirose (2009) who
found a trend for the maturation difference to be
smaller in players born during the rst quarter of
the year. It is important to remember that boys of
the rst quartile can be up to 1012 months older
than boys belonging to the last quartile. Therefore,
this result should be taken cautiously, because the
aforementioned difference in maturity offset may be
due to differences in chronological age rather than
due to an advanced maturity in the older boys. In
fact, age at peak height velocity was similar in all
boys denoting no particular differences in the matur-
ity status of the boys when they were divided accord-
ing to their chronological age.
Competition to obtain a place in a team seems to
be one of the causes of RAE. Moreover, this compe-
tition will come from the number of players available
for the places, and this number will depend on the
popularity of a given sport in a given country (Musch
& Grondin, 2001). Soccer is the most popular sport
in the world and also in the area where the present
study was undertaken. In this region, although the
vast majority of children have the opportunity to play
this sport, there is some competition to gain a place
in particular teams. In particular, some teams start
making some kind of selection towards the best
players in the area from the age of 910. In this
study, we observed that older boys were taller and
had longer legs, which may be considered a physical
advantage for soccer. Besides, they performed better.
Thus, one of the reasons for this uneven distribution
of birth dates may come from an external selection
implemented by the soccer teams choosing the best
players. In addition, as Delorme et al. (2010a)
pointed out, a self-restriction may be present which
prevents children born at the end of the year from
beginning to practice this sport; younger children
may see themselves as disadvantaged to play soccer
and opt to play another sport. Furthermore, a higher
number of dropouts have been observed in players
born towards the end of the year. Delorme et al.
(2010b) in their large study on 363,590 French
male soccer players observed that the number of
dropouts was signicantly higher among players
born in the last quarter of the year. This phenom-
enon was evident from the group of players Under-9
to Under-18. Thus, the competition for a place in a
team and external selection caused by an advanta-
geous physique and a better performance of older
boys, together with a self-restriction and/or a higher
number of dropouts in disadvantaged younger
players, may be the reasons why RAE is present in
particularly popular sports.
One of the limitations of this study is that techni-
cal and tactical aspects have not been measured.
However, in a recent study of the differences
between younger (aged 9.4) and older soccer players
(aged 11.8), most differences were found in physio-
logical performance, while the only technical skill
difference was observed in heading (Fernandez-
Gonzalo et al., 2010). Nevertheless, it would be
very interesting to measure similar parameters in a
larger sample of soccer players in order to ascertain if
some of the subtle differences found in this group of
88 players become more evident.
RAE in young soccer players 7
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Conclusion
In the present study, an overrepresentation of players
born during the beginning of the year, known as
RAE, was conrmed. In fact, the date of birth of
only 17% of the players was in the last 3 months.
Similar results have been widely reported for soc-
cer and other sports. Nevertheless, the reasons for
RAE have been hypothesised but not clearly demon-
strated. In this group of pre-pubertal soccer players,
born in the same year, older players were taller and
had longer legs. Moreover, they performed better in
the physical tests (velocity and agility), and the dif-
ference was more evident in the overall performance
score. Although larger studies are needed, we have
demonstrated here that both differences in body size
and also in physical performance may underlie RAE.
Acknowledgements
This study was partially supported by a grant from
the Basque Government [UE09+/07].
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Downloaded by [Universidad Del Pais Vasco] at 06:12 20 September 2013
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... There is known to be a relative age effect on the biological maturation of young football players, which not only results in anthropometric variances (e.g., height, leg length) (Gil et al., 2014) but also could lead to a systematic bias during talent development in favor of players born early in the year and early maturers (Williams, 2010). In terms of relative age effect on physical fitness, Gil and others discovered that nine-to 10-year-old male football players born in the early months of the year performed better on the 30 m run test, agility test, and overall performance score than their peers of the same age born later in the year (Gil et al., 2014). Therefore, while an age difference of less than 12 months may not be relevant for evaluating children's performance in basic physical education curriculum, it may be significant in high performance sports. ...
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Aim: The present study aimed to investigate the prevalence of RAE in soccer players from different positions in Series A and B in 2020 Brazilian soccer, as well as the impact of RAE on the estimated market value of these players. Methods: Data from 1080 male elite soccer athletes were analyzed. Athletes were grouped according to birth quarters: Q1 (January-March), Q2 (April-June), Q3 (July-September), and Q4 (October-December) and the competitive level (Series A or B). Chi-square tests (χ2) were performed to compare the birthdates’ distribution of athletes according to a competitive level and playing positions. Kruskal-Wallis test was used to compare the market values of players born in each of the quarters of the year across competitive levels and playing positions. The significance level was set at 5%. Results: The overall analyses showed the prevalence of RAE in Series A and B, with an overrepresentation of athletes born in the first two quarters of the year. The RAE analysis based on playing positions showed different from expected distributions for forwards, midfielders, and defenders in Series A. In Series B, only midfielders showed a difference from the expected distribution. As for the market values analyses, no differences were found based on the athletes’ birth quarters, regardless of competitive level or playing position. Conclusion: Our results indicate that, although RAE is prevalent in Series A and B of elite Brazilian soccer, it does not seem to influence players’ estimated market values.
... This finding might be consistent with the emerging evidence of advantages afforded to those with earlier maturation, potentially overlooking tactical and technical abilities (D. Deprez et al., 2013;Figueiredo et al., 2009;Gil et al., 2014b;Hirose, 2009;Malina et al., 2004;Parr et al., 2020). Those results are tentative though and would require verification over a longer time period and on more average sprinters to determine whether the less mature children would "catch up" with the others given sufficient time. ...
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... 9, 11 regarding performance characteristics it has been found that U14 soccer players with higher rates of height and body mass index perform better in vertical jump, speed, and V o 2max . 11 on the other hand players' higher rates of weight and body fat affect negatively performance, [12][13][14] speed and V o 2max . 15 apart from anthropometrical and performance factors the players should be able to master the basic technical skills of the game so as to produce successful performance. ...
... 3 The first study to examine RAE in sport was done by Grondin, Deschaies & Nault (1984). 8 The RAE, which describes the overall difference in age between individuals within each age group 9 can produce significant performance differences and is significant for children, although this effect is minor for adults. 10 Children born at the beginning of the year may be cognitive, emotionally 3, 11, and physically more advantageous than those born at the end of the year. ...
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