Journal of Sports Sciences
ISSN: 0264-0414 (Print) 1466-447X (Online) Journal homepage: http://www.tandfonline.com/loi/rjsp20
Interrelationships among invasive and noninvasive indicators of biological maturation in
adolescent male soccer players
Robert M. Malina , Manuel J. Coelho E Silva , António J. Figueiredo ,
Christopher Carling & Gaston P. Beunen
To cite this article: Robert M. Malina , Manuel J. Coelho E Silva , António J. Figueiredo ,
Christopher Carling & Gaston P. Beunen (2012) Interrelationships among invasive and noninvasive indicators of biological maturation in adolescent male soccer players, Journal of
Sports Sciences, 30:15, 1705-1717, DOI: 10.1080/02640414.2011.639382
To link to this article: http://dx.doi.org/10.1080/02640414.2011.639382
Published online: 03 Feb 2012.
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Date: 20 November 2015, At: 18:01
Journal of Sports Sciences, November 2012; 30(15): 1705–1717
Interrelationships among invasive and non-invasive indicators of
biological maturation in adolescent male soccer players
ROBERT M. MALINA1,2, MANUEL J. COELHO E. SILVA3, ANTÓNIO J. FIGUEIREDO3,
CHRISTOPHER CARLING4, & GASTON P. BEUNEN5{
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1
Department of Kinesiology and Health Education, University of Texas at Austin, Austin, Texas, USA, 2Tarleton State
University, Stephenville, Texas, USA, 3Faculty of Sport Sciences and Physical Education, University of Coimbra, Coimbra,
Portugal, 4LOSC Lille Métropole Football Club, Domain de Luchin, Camphin-en-Pévèle, France and 5Faculty of Kinesiology
and Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
(Accepted 6 November 2011)
Abstract
The relationships among indicators of biological maturation were evaluated and concordance between classifications of
maturity status in two age groups of youth soccer players examined (11–12 years, n ¼ 87; 13–14 years, n ¼ 93). Data included
chronological age (CA), skeletal age (SA, Fels method), stage of pubic hair, predicted age at peak height velocity, and
percent of predicted adult height. Players were classified as on time, late or early in maturation using the SA-CA difference,
predicted age at peak height velocity, and percent of predicted mature height. Factor analyses indicated two factors in players
aged 11–12 years (maturity status: percent of predicted mature height, stage of pubic hair, 59% of variance; maturity timing:
SA/CA ratio, predicted age at peak height velocity, 26% of variance), and one factor in players aged 13–14 years (68% of
variance). Kappa coefficients were low (0.02–0.23) and indicated poor agreement between maturity classifications.
Spearman rank-order correlations between categories were low to moderate (0.16–0.50). Although the indicators were
related, concordance of maturity classifications between skeletal age and predicted age at peak height velocity and percent
predicted mature height was poor. Talent development programmes call for the classification of youth as early, average, and
late maturing for the purpose of designing training and competition programmes. Non-invasive indicators of maturity status
have limitations for this purpose.
Keywords: Skeletal age, age at peak height velocity, height prediction, sexual maturation, young athletes
Introduction
Growth and maturation characteristics of young
athletes are implicit in models of talent identification,
selection, and development (Reilly, Williams, &
Richardson, 2003; Williams & Reilly, 2000). Variation in size and performance associated with interindividual differences in biological maturation are
especially important during the transition into and
during male adolescence (Malina, Bouchard, & BarOr, 2004). With increasing age, progress through
puberty, and the growth spurt in males, samples of
athletes in several team sports (including soccer),
swimming and track and field athletics include
proportionally more players who are advanced
(early) in biological maturation and proportionally
fewer players who are delayed (late) in maturation
(Malina, 1994, 2003, 2011; Malina, Coelho e Silva,
& Figueiredo, 2012).
The more commonly used indicators of maturation in males include skeletal age (SA) and
secondary sex characteristics (pubic hair, genitals,
testicular volume). Skeletal age is applicable from
childhood through adolescence, while secondary sex
characteristics are limited to the pubertal years.
Other indicators include age at peak height velocity
and percentage of mature (adult) height attained at
a given age (Beunen, Rogol, & Malina, 2006;
Malina et al., 2004; Roche & Sun, 2003). Both
require longitudinal data for derivation and have
been used in analyses of interrelationships among
maturity indicators during adolescence (Bielicki,
1975; Bielicki, Koniarek, & Malina, 1984; Kaczmarek, 2002; Nicolson & Hanley, 1953) and in
comparisons of youth of contrasting maturity status
(Bayer & Bayley, 1959; Beunen et al., 1994, 2004;
Lefevre, Beunen, Steens, Claessens, & Renson,
1990; Tanner, 1962).
Correspondence: R. M. Malina, Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX 77414-9357, USA.
E-mail: [email protected]
ISSN 0264-0414 print/ISSN 1466-447X online Ó 2012 Taylor & Francis
http://dx.doi.org/10.1080/02640414.2011.639382
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R. M. Malina et al.
Stages of genital and/or pubic hair development and
skeletal age have been used most frequently in studies
of youth in general (Beunen et al., 2006; Malina et al.,
2004; Tanner, 1962) and in soccer players more
specifically (Malina, 1994, 2011). The indicators are
often labelled as invasive. Skeletal age requires a low
dose of radiation for a hand–wrist radiograph.
Exposure is minimal with modern technology, 0.001
millisievert (mSv), which is less than natural background radiation and radiation associated with the
equivalent of 3 h of television viewing per day
(Radiological Society of North America, 2009; US
Department of Energy, 2000). Skeletal age also has
associated expenses (radiologist, film, travel to clinic)
and requires specific training and expertise for
evaluation. Clinical assessment of pubertal status
requires direct examination of stage of genital and/or
pubic hair, or palpation of the genitals to estimate
testicular volume; the protocols are often viewed as an
invasion of personal privacy. Many studies now use
self-assessments of stage of genital and/or pubic hair.
Correlations between self and physician ratings of
pubertal status range from moderate to high (0.59 to
0.92; Matsudo & Matsudo, 1994), while some data
suggest a tendency for overestimation of early and
underestimation of later stages of sexual development
(Schlossberger, Turner, & Irwin, 1992).
More recently, two indicators of maturity status
that have minimal physical and/or psychological risk
for the individual (labelled ‘‘non-invasive’’) have
been introduced in studies of young athletes.
Current age, height, sitting height, estimated leg
length (height minus sitting height), weight, and
interaction terms are used to estimate time before or
after peak height velocity and in turn to predict age at
peak height velocity (Mirwald, Baxter-Jones, Bailey,
& Beunen, 2002). Predicted age at peak height
velocity has been used in research with athletes
(Malina et al., 2006a; Nurmi-Lawton et al., 2004;
Sherar, Baxter-Jones, Faulkner, & Russell, 2007;
Till, Cobley, O’Hara, Chapman, & Cooke, 2010).
Age at peak height velocity is also central to the longterm athlete development (LTAD) model; accordingly, ‘‘LTAD requires the identification of early,
average, and late maturers in order to help design
appropriate training and competition programs in
relation to optimal trainability and readiness. The
beginning of the growth spurt and the peak of the
growth spurt are very significant in LTAD applications’’ (Balyi, Cardinal, Higgs, Norris, & Way, 2005,
p. 23). Although there is a lack of empirical evidence
to support the contentions of the LTAD model
(Ford et al., 2011), predicted age at peak height
velocity is increasingly used by a number of English
professional football clubs to estimate maturity
timing in academy players (S. P. Cumming, University of Bath, personal communication).
Percentage of predicted adult (mature) height
attained at a given age requires current height and
a prediction of adult height; current height is
expressed as a percentage of the predicted mature
value. The rationale for the method is as follows: two
youth of the same age can have the same height, but
one is closer to mature height than the other. The
youngster who is closer to mature height is advanced
in maturity status compared with the one who is
further from mature height (Bayer & Bayley, 1959;
Beunen et al., 1997, 2006; Malina et al., 2004;
Roche, Tyleshevski, & Rogers, 1983). The protocol
has been used in studies of activity level (Cumming,
Standage, Gillison, Dompier, & Malina, 2009;
Cumming, Standage, Gillison, & Malina, 2008;
Eaton & Yu, 1989), perceived competence in youth
soccer players (Cumming, Battista, Standage, Ewing, & Malina, 2006), and injury risk in youth
American football (Malina, Cumming, Morano,
Barron, & Miller, 2005; Malina et al., 2006b).
Skeletal age, secondary sex characteristics, age at
peak height velocity, and percentage of mature height
reflect different but related aspects of biological
maturation during male adolescence. The two traditional indicators (skeletal age and stage of pubic hair)
and two non-invasive estimates (predicted age at peak
height velocity and percentage of predicted mature
height) are evaluated in male youth soccer players aged
11–12 and 13–14 years in the context of interrelationships among the four maturity indicators and the
concordance between classifications of players into
maturity categories with specific pairs of indicators.
Our first aim was to verify whether the non-invasive
techniques measure the same maturity construct as
skeletal age and stage of pubic hair. Based on
observations in two longitudinal studies (Bielicki
et al., 1984; Nicolson & Hanley, 1953), it was
hypothesized that relationships among indicators
would be different in the younger compared with the
older players. Grouping players by maturity status has
long been a focus of research in youth sports and more
recently has been included as central to the LTAD
model. Our second aim was to address the issue of
concordance between classifications based on the
non-invasive methods and skeletal maturity. Presently
available data for the non-invasive estimates, however,
are not sufficient to permit a reasonable hypothesis.
Methods
Sample
The cross-sectional sample included 181 boys from
soccer clubs in the districts of Aveiro and Coimbra
located about midway between Lisbon and Oporto.
All players except one were of Portuguese ancestry.
Players were born in 1989 through 1992 and
Indicators of biological maturation in young athletes
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represented two age groups for youth competitions,
11–12 years (infantiles) and 13–14 years (initiates),
defined by age on 1 January 2003. The younger boys
were from club teams in the respective regions, while
the older boys were from club and regional teams in
the respective districts, and included regional elite
and several national sub-elite youth.
The study received approval from the Scientific
Committee of the University of Coimbra and each
participating club. Parents of players provided
informed consent, while the athletes provided assent.
Participants were informed that participation was
voluntary and that they could withdraw at any time.
Protocol
Younger players (n ¼ 87) were aged 10.98 to 12.94
years at the time of study in December 2003 and
older players (n ¼ 93) were aged 13.30 to 15.25 years
at the time of study in April 2004 (4 boys were aged
15.0–15.2 years but were 515.0 years as of 1
January). Chronological age was calculated as the
difference between date of birth and date on which
the radiograph was taken. Data for all players
included chronological age, skeletal age, stage of
pubic hair, anthropometric characteristics (height,
weight, sitting height), and parents’ heights (Coelho
e Silva et al., 2010; Figueiredo, Gonçalves, Coelho e
Silva, & Malina, 2009b).
Skeletal Maturity. Radiographs of the left hand-wrist
were evaluated with the Fels method for assessing
skeletal maturity (Roche, Chumlea & Thissen, 1988).
The method was developed in the Fels Longitudinal
Study of children resident in south-central Ohio. The
majority of radiographs (88%) were assessed by a
single individual (AF) who was trained by an
experienced rater (RMM). The mean difference in
SA between replicates (*11% of the sample) was
0.03 + 0.04 year and the inter-observer technical
error of measurement was 0.12 year. The skeletal
maturity assessment of one player (14.1 years)
indicated mature status and he was excluded from
the analyses. An SA is not assigned to individuals who
are skeletally mature; they are simply noted as mature
(Roche et al., 1988). Prediction of adult height is not
applicable in skeletally mature youth (Tanner et al.,
2001). It likewise does not make biological sense to
predict age at PHV in skeletally mature individuals.
The SACA ratio (SA divided by CA) and SA-CA (SA
minus CA) were calculated.
Anthropometry. Weight, height and sitting height
were measured. Players wore shorts and a t-shirt;
shoes were removed. The anthropometry was done
within one week of the radiograph for most players
and within two weeks for several. A sample of 32
1707
players was measured on a second occasion within
one week. Intra-observer technical errors of measurement for the replicate sample were as follows:
weight (0.47 kg), height (0.27 cm), sitting height
(0.31 cm).
Sexual maturity. A physician experienced in the
protocol of Tanner (1962) assessed stage of pubic
hair at clinical examination. Replicate assessments
were not possible.
Predicted age at peak height velocity. The algorithm
derived from two longitudinal studies of Canadian
youth and one of Belgian twins was used to predict
the time before or after peak height velocity in years,
labelled maturity offset (Mirwald et al., 2002):
Maturity offset ðyearsÞ ¼ 9:236
þ ð0:0002708 ðLeg Length Sitting HeightÞÞ
þð0:001663 ðAge Leg LengthÞÞ
þ ð0:007216 ðAge Sitting HeightÞÞ
þ ð0:02292 ðWeight=Height 100ÞÞ
½R ¼ 0:94; R2 ¼ 0:89; and sx ¼ 0:59
Predicted age at peak height velocity (years) was
estimated as chronological age minus maturity offset.
The leg length/sitting height ratio is an important
variable in the equation. It is related to peak height
velocity. Means and standard deviations for ratios of
one of the longitudinal samples upon which the
protocol was developed and of the cross-sectional
sample of soccer players are summarized relative to
time before and after peak height velocity in Table I.
Table I. Ratio of leg length to sitting height (%) in one of the
longitudinal samples upon which the maturity offset method was
developed (BMAS)a and the cross- sectional sample of soccer
players by maturity offset groupb.
BMAS
Years from
peak height
velocity
74
73
72
71
0
1
2
3
a
Soccer players
n
mean + s
n
mean + s
24
68
98
125
141
110
68
25
87.9 + 3.1
89.6 + 3.4
91.1 + 3.4
92.5 + 3.9
93.2 + 4.0
92.3 + 4.2
90.4 + 3.9
89.6 + 3.0
18
62
33
36
28
3
92.0 + 5.7
90.4 + 4.6
94.4 + 3.8
96.0 + 4.8
93.9 + 4.3
88.4 + 6.0
Saskatchewan Pediatric Bone Mineral Accrual Study (Mirwald
et al., 2002).
b
Maturity offset groups: 73 ¼ 72.50 to 73.49, 72 ¼ 71.50
to 72.49, 71 ¼ 70.50 to 1.49, 0 ¼ 70.49 to þ0.49, 1 ¼ þ0.50
to þ1.49, 2 ¼ 41.50.
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R. M. Malina et al.
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Ratios, on average, tended to be slightly higher in
soccer players, which may reflect sampling and
perhaps ethnic variation in proportions. Mean ratios
increased as peak height velocity was approached and
then declined.
Percentage of Predicted Mature Height. CA, height and
weight of the player and midparent height were used
to predict mature (adult) height with the protocol
developed on the sample of the Fels Longitudinal
Study (Khamis & Roche, 1994). Parent heights were
extracted from national identification cards which
included height measured to the nearest centimeter.
Measurements were taken by experienced, but not
necessarily trained observers. A similar protocol was
used in surveys of conscripts in Portugal (Padez,
2003). The median error bound (median absolute
deviation) between actual and predicted mature
height at 18 years of age in the Fels Longitudinal
Study was 2.2 cm in males (Khamis & Roche, 1994).
Current height of each player was expressed as a
percentage of his predicted mature height as an
estimate of biological maturation at the time of
study.
Analysis
Descriptive characteristics of the sample were calculated for the two age groups. Frequencies of stages of
pubic hair were summarized in each age group.
Interrelationships among invasive (skeletal age/
chronological age [SA/CA] ratio, stage of pubic hair)
and non-invasive (percentage of predicted mature
height, predicted age at peak height velocity)
maturity indicators were evaluated in the two age
groups. Factor analysis using principal components
extraction and varimax rotation was used. Extracted
factors with an eigenvalue greater than 1.00 were
retained. The statistical software used was SPSS
version 14.0.
Players were classified into three maturity categories for analysis of concordance of classifications
with SA-CA, percentage of predicted mature
height, and predicted age at peak height velocity:
late (delayed), on time (average) or early (advanced). Corresponding classifications were not
possible for stage of pubic hair. A stage indicates
status at the time of examination; it does not
indicate when the stage was reached or how long
the individual was in the stage. Reference data, in
contrast, are reported as estimated ages at onset of
specific stages (Eveleth & Tanner, 1990; Malina
et al., 2004). Maturity classifications based on SACA, predicted age at peak height velocity, and
percentage of predicted mature height were summarized relative to stage of pubic hair in the two
age groups.
Skeletal maturation. Players were classified into three
categories based on the difference between skeletal
age and chronological age (SA-CA). On time or
average maturity status was a skeletal age within +1.0 year of chronological age; late maturating
was a skeletal age behind chronological age by more
than 1.0 year; and early maturing was a skeletal age in
advance of chronological age by more than 1.0 year.
The classification was similar to previous studies of
athletes and non-athletes using SA-CA (Malina,
2011). A band of +1.0 year approximates standard
deviations of skeletal ages within specific chronological age groups between 11 and 16 years with the
Fels and other methods of skeletal age assessment
(Malina, 2011), and provides for a broad range of
youth classified as on time or average in maturity
status. It also allows for error associated with
assessments; standard errors of estimate for the all
assessments ranged from 0.25 to 0.42 year, with a
median of 0.30 year.
Predicted mature (adult) height. Percentages of predicted mature height were expressed as z-scores
relative to age-specific means and standard deviations for percentage of mature height attained at halfyearly intervals by boys aged 11–15 years in the
Berkeley Guidance Study, University of California
(Bayer & Bayley, 1959). Corresponding data were
not available for a European sample. The z-scores
were used to estimate maturity status: on time, zscore between 71.0 and þ1.0; late, z-score below
71.0; early, z-score above þ1.0. The Guidance
Study sample was selected for several reasons: first,
Fels reference values for percentage mature height
were reported for yearly intervals with a narrow band
of variation, as participants were measured within
one month of their birthdays (Roche et al., 1983);
second, mean heights of boys aged 9–14 years in the
Fels and Berkeley samples were similar to each other
and to US reference values; third, means and
standard deviations for percentage mature height at
half-year intervals were reported for the Berkeley
study (Bayer & Bayley, 1959); fourth, the KhamisRoche method (Khamis & Roche, 1994) used the
same half-year age intervals; and fifth, mean percentage of mature height at each age from 9 to 15 years
was similar in the Fels and Berkeley samples (Bayer
& Bayley, 1959; Roche et al., 1983).
Age at PHV. Predicted ages at PHV were classified
relative to the three samples upon which the maturity
offset protocol was developed, two longitudinal
studies of Canadian boys and a longitudinal study
of Belgian male twins (Mirwald et al., 2002).
Respective ages at PHV were 13.4 + 0.7 yrs (11.1–
15.6 yrs, n ¼ 79), 14.0 + 1.0 yrs (11.4–16.5 yrs,
n ¼ 71) and 14.2 + 0.8 yrs (12.6–15.8 yrs, n ¼ 50).
1709
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Indicators of biological maturation in young athletes
Mean age at PHV for the three samples adjusted for
sample sizes was 13.8 years. Using the range of ages
at PHV for individual boys, 11.1 to 16.5 years, the
standard deviation for the combined sample was
estimated as 0.9 year, which was similar to standard
deviations for ages at PHV in longitudinal studies,
about +1.0 year (Malina & Beunen, 1996; Malina
et al., 2004). On time was defined as a predicted age
at PHV within + one SD of the overall mean age at
PHV (13.8 + 0.9 yrs or 12.9 to 14.7 yrs), late as an
age at PHV 414.7 years and early as an age at PHV
512.9 yrs.
Cross-tabulations of maturity status classifications
between each non-invasive indicator and skeletal
maturation and between the two non-invasive
indicators were calculated. Cross-tabulations show
the bivariate relationship between classifications
(late, on time, early) based on each pair of indicators
and include a cell for each combination of maturity
categories. Cohen unweighted kappa coefficients
(http://faculty.vassar.edu/lowry/VassarStats.html) were
calculated to evaluate the concordance of maturity
classifications with pairs of maturity indicators except
for stage of pubic hair. Spearman (rS) rank-order
correlations were also calculated between classifications and stage of pubic hair.
Percentage of predicted mature height and stage of
pubic hair have the highest loadings on the first
factor, accounting for 59% of the variance. The SA/
CA ratio and predicted age at peak height velocity
have the highest loadings on the second factor,
accounting for 26% of the variance. The two factors
account for 84% of the variance in the four maturity
indicators in players aged 11–12 years. In contrast,
only one factor is evident in players aged 13–14 years
and all four maturity indicators have high loadings.
This single factor accounts for about 68% of the
variance in players aged 13–14 years.
Cross-tabulations of maturity status classifications
based on SA-CA and percentage of predicted mature
height, SA-CA, and predicted age at peak height
velocity, and predicted age at peak height velocity
and percentage of predicted mature height are
summarized in Tables IV and V for the younger
and older age groups, respectively. Concordance
between SA-CA classifications and the two noninvasive classifications are 55% (predicted age at
peak height velocity) and 57% (percentage mature
height) among players aged 11–12 years, and 57%
Table III. Results of the principal components analysis of four
maturity indicators in the two age groups of soccer players.
11–12 years
(n ¼ 87)
Results
Age, height, weight, and maturity-related characteristics of players in the two competitive age groups are
summarized in Table II, while results of the age
group-specific factor analyses are summarized in
Table III. Two factors are evident for players aged
11–12 years. There is no evidence of cross loading of
maturity indicators for the two factors in younger
players, suggesting independence of the factors.
SA/CA ratio
Age at peak height velocity
Height as percent adult height
Stage of pubic hair
Eigenvalue
Percent variance
13–14 years
(n ¼ 93)
PC1
PC2
PC1
0.244
70.125
0.917
0.885
2.343
58.6
0.874
70.913
0.131
0.238
1.028
25.7
0.747
70.888
0.798
0.852
2.709
67.7
Table II. Descriptive statistics for growth and maturity characteristics and frequencies of stages of pubic hair in youth soccer players in two
competitive age groups.
11–12 years (n ¼ 87)
Variable
Age (years)
Skeletal age (years)
SA-CA (years)
SA/CA ratio
Predicted adult height (cm)
Height as percent of adult height (%)
Maturity offset (years)
Age at peak height velocity (years)
Stage of pubic hair (n)
1
2
3
4
5
13–14 years (n ¼ 93)
mean + s
min.
max.
mean + s
min.
max.
11.8 + 0.5
12.0 + 1.4
0.17 + 1.44
1.02 + 0.12
172.0 + 5.3
84.1 + 2.4
72.12 + 0.51
13.9 + 0.4
11.0
8.3
74.16
0.67
161.0
77.1
73.34
12.9
12.9
14.6
3.44
1.31
185.2
91.3
70.77
15.1
14.1 + 0.5
14.7 + 1.1
0.65 + 1.04
1.05 + 0.07
175.5 + 6.1
93.6 + 3.0
0.06 + 0.79
14.0 + 0.6
13.3
12.0
71.76
0.87
157.5
86.2
71.58
12.6
15.3
17.7
2.82
1.21
191.6
100.0
1.83
15.2
47
31
9
0
0
0
13
32
46
2
1710
R. M. Malina et al.
Table IV. Frequencies and cross-tabulations of maturity status classifications between specific pairs of maturity indicators, percentage
concordance, Spearman rank order correlations (rS), and Cohen’s kappa in soccer players aged 11–12 years.
Maturity indicator and categories
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Maturity indicator and categories
% predicted mature heighta
Late
On time
Early
Total
Age at peak height velocityc
Late
On time
Early
Total
% predicted mature heighta
Late
On time
Early
Total
Late
4
13
0
17
2
15
0
17
1
1
0
2
On time
Early
Total
% Agreement [95% CI]
rS
k
5
64
18
87
57% [47, 67]
0.27*
0.23
2
84
1
87
55% [44, 66]
0.43*
0.11
5
64
18
87
75% [64, 83]
0.26*
0.12
SA-CA differenceb
1
0
36
15
8
10
45
25
SA-CA differenceb
0
0
45
24
0
1
45
25
Age at peak height velocityc
4
0
63
0
17
1
84
1
a
Based on z-scores for boys in the University of California at Berkeley Guidance Study (see text): on time (average) is a z-score
between 71.0 and þ1.0; late is a z-score below 71.0; early is a z-score above þ1.0.
b
On time (average) is as a skeletal age within +1.0 year of chronological age; late is a skeletal age behind chronological age by more than 1.0
year; early is a skeletal age in advance of chronological age by more than 1.0 year.
c
On time (average) is an age at peak height velocity within +1 standard deviation of the mean age at peak height velocity for the three samples
upon which the maturity offset protocol is based (13.8 + 0.9 years); late is a peak height velocity 414.7 years; early is a peak height
velocity 512.9 years (see text for details).
*P 5 0.01.
Table V. Frequencies and cross-tabulations of maturity status classifications between specific pairs of maturity indicators, percentage
concordance, Spearman rank order correlations (rs), and Cohen’s kappa in soccer players aged 13–14 years.
Maturity indicator and categories
Maturity indicator and categories
% predicted mature heighta
Late
On time
Early
Total
Age at peak height velocityc
Late
On time
Early
Total
% predicted mature heighta
Late
On time
Early
Total
Late
0
4
0
4
4
0
0
4
0
13
0
13
On time
Early
SA-CA differenceb
0
0
44
19
11
15
55
34
SA-CA differenceb
9
0
46
31
0
3
55
34
Age at peak height velocityc
0
0
54
0
23
3
77
3
Total
% Agreement [95% CI]
rS
k
0
67
26
93
63% [53, 73]
0.47*
0.23
13
77
3
93
57% [46, 67]
0.29*
0.13
0
67
26
93
61% [51, 71]
0.34*
0.02
a
Based on z-scores for boys in the University of California at Berkeley Guidance Study (see text): on time (average) is a z-score
between 71.0 and þ1.0; late is a z-score below 71.0; early is a z-score above þ1.0.
b
On time (average) is as a skeletal age within +1.0 year of chronological age; late is a skeletal age behind chronological age by more than 1.0
year; early is a skeletal age in advance of chronological age by more than 1.0 year.
c
On time (average) is an age at peak height velocity within +1 standard deviation of the mean age at peak height velocity for the three
samples upon which the maturity offset protocol is based (13.8 + 0.9 years); late is a peak height velocity 414.7 years; early is a peak height
velocity 512.9 years (see text for details).
*P 5 0.01.
(predicted age at peak height velocity) and 63%
(percentage mature height) among players aged 13–
14 years. Expected chance agreements between
classifications are 51% and 45%, respectively, in
players aged 11–12 years, and 51% and 52%,
respectively, in players aged 13–14 years.
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Indicators of biological maturation in young athletes
Concordance between classifications based on the
two non-invasive estimates are 75% and 61% in
players aged 11–12 and 13–14 years, respectively;
corresponding expected chance agreements are 71%
and 60%, respectively. The expected chance agreements between specific pairs of classifications are
within the confidence intervals of the observed
concordances (Tables IV and V).
Kappa coefficients are low in the two age groups:
0.23 and 0.23 (SA-CA and percentage mature
height), 0.11 and 0.13 (SA-CA and age at peak
height velocity), and 0.12 and 0.02 (percentage
mature height and age at peak height velocity) for
boys aged 11–12 and 13–14 years, respectively. The
coefficients indicate relatively poor agreement between maturity classifications based on specific pairs
of indicators.
Spearman rank-order correlations between maturity classifications, although significant (P 5 0.01) are
low to moderate, 0.27 to 0.43 among players aged
11–12 years and 0.29 to 0.47 among players aged
13–14 years (Tables IV and V). Corresponding
correlations between stage of pubic hair and the
other maturity indicators are low to moderate (0.16
to 0.47) among players aged 11–12 years, but higher
(0.36 to 0.50) among players aged 13–14 years
(Table VI).
Discussion
The hypothesis that the factor structure of the
maturity indicators would be different in younger
Table VI. Distributions of maturity categories based on skeletal
age, age at peak height velocity, and percentage of predicted adult
height by stage of pubic hair development and Spearman rank
order correlations (rS) between each maturity indicator and stage
of pubic hair in soccer players aged 11–12 and 13–14 years.
Stage of pubic hair
11–12 years
Maturity indicator
and categories
1
2
SA-CA difference
Late
15
2
On time
24 18
Early
8
11
0.40*
rS
Age at peak height velocity
Late
2
0
On time
45 30
Early
0
1
rS
0.16
% predicted adult height
Late
5
0
On time
37 22
Early
5
9
rS
0.34*
*P 5 0.01.
3
4
13–14 years
5
1
2
3
4
5
0
3
6
3
9
1
1
0
23 22
8
24
0.40*
0
1
1
0
9
0
7
6
0
6
0
26 43
0
3
0.50*
0
2
0
0
5
4
0
12
1
0
0
28 26
4
20
0.36*
0
1
1
1711
compared with older youth players was generally
supported. Results indicated two factors for younger
players. The first (58% of the variance) appeared to
be an indicator of maturity status, that is, stage of
pubic hair and percentage of mature height attained
at the time of study. The second (26% of the
variance) appeared to be an indicator of maturity
timing, that is, skeletal age relative to chronological
age and predicted age at peak height velocity. In
contrast, only one factor (69% of the variance) was
evident for players aged 13–14 years and the four
maturity indicators loaded on it by about the same
magnitude.
Results of the factor analyses were consistent with
those for boys in the Berkeley (Nicolson & Hanley,
1953) and Wrocław (Bielicki et al., 1984) studies,
which considered interrelationships among ages at
attaining specific skeletal ages and percentages of
mature height and stages of pubertal development,
and ages at peak velocity for height and leg and trunk
lengths. The results from the two longitudinal
studies indicated a general maturity factor for
adolescent events occurring at about 14 years of
age and an independent cluster occurring during the
transition into or the early phase of puberty in males.
The latter was reflected in lower loadings for ages at
attaining skeletal ages of 11 and 12 years and 80% of
mature height in Wrocław (Bielicki et al., 1984) and
for ages at attaining a skeletal age of 11.25 years,
80% of mature height, and stage 2 of sexual
maturation (pubic hair and genital combined) in
Berkeley (Nicolson & Hanley, 1953) boys. The
results were consistent with an independent cluster
of maturity indicators that apparently mark
the transition into or the early phase of puberty in
boys.
Observations for players aged 11–12 years appeared to be consistent with the cluster of variables
marking the transition into or early puberty. Among
87 players, 77 were in pubic hair stages 1 (prepubertal) or 2 (early pubertal), skeletal age was not in
advance of chronological age, and percentage of
predicted mature height approximated the average in
the Berkeley longitudinal series. The two factors in
this age group suggested independence of maturity
status (factor 1) and timing (factor 2). Percentage of
predicted mature height and stage of pubic hair
indicated status of the players at the time of study,
while the SA/CA ratio and predicted age at peak
height velocity indicated timing since both have a
chronological age component.
The sample of soccer players aged 11–12 years of
age was, as a group, average in maturity status.
Although skeletal age was only slightly in advance of
chronological age, variation in skeletal age was not
quite three times the variation in chronological age.
Mean percentage of predicted mature height of the
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1712
R. M. Malina et al.
players (84.5 + 2.5%) was essentially the same as
that for the 12.0-year-old boys in the Fels Longitudinal Study (84.9 + 1.5%) upon which the prediction equation was developed (Roche et al., 1983).
In the Fels study, participants were measured close
to their birthdays so that the range of variation was
reduced. Mean percentage of predicted mature
height for the younger age group of soccer players
was also similar to that for the 12.0-year-old boys in
the Berkeley Longitudinal Study (84.0 + 2.2%)
(Bayley & Pinneau, 1952). Mean predicted age at
peak height velocity (13.9 + 0.4 years) was similar to
the mean age at peak height velocity for the boys
upon whom the maturity offset protocol was developed (13.8 + 0.9 years). However, the standard
deviation for predicted age at peak height velocity
in soccer players was about one-half of that for the
three longitudinal series. An estimate of age at peak
height velocity for the general population of Portuguese boys was not available.
The results for players aged 13–14 years were also
consistent with the general maturity factor observed
in Wrocław (Bielicki et al., 1984) and Berkeley
(Nicolson & Hanley, 1953) boys. In the Wrocław
Growth Study, the highest loadings (0.90) on the
general maturity factor (77% of the variance) were
for events occurring at about 14 years of age: ages at
peak velocity for height and trunk length (14.0 + 1.2
and 14.4 + 1.1 years respectively); ages at attaining
skeletal ages of 14 and 15 years (14.0 + 1.0 and
15.0 + 1.0 years respectively); ages at attaining 90%
and 95% of adult height (13.9 + 1.0 and 14.9 + 1.0
years respectively); and ages at attaining genital and
pubic hair stage 4 (14.6 + 1.0 and 15.1 + 1.1 years
respectively) (Bielicki et al., 1984).
Players aged 13–14 years (chronological age
14.1 + 0.6 years) were, as a group, advanced in
skeletal age by about 0.5 year compared with
chronological age. Percentage of predicted mature
height attained (94.1 + 3.2%) was in advance of the
corresponding means for the Berkeley and Fels
studies (91.0 + 4.0% and 92.7 + 1.9%, respectively)
(Bayley & Pinneau, 1952; Roche et al., 1983), but
was slightly less than that for early maturing boys in
the Berkeley study (95.8%) (Bayer & Bayley, 1959).
The majority of players were in pubic hair stages 3
(n ¼ 32) and 4 (n ¼ 46). It was not known, however,
when the players reached or how long they were in
the respective stages. Mean or median ages for onset
of stages 3 and 4 of pubic hair in samples of western
European boys ranged from 13.4 to 13.9 years and
from 14.1 to 15.0 years, respectively (Malina et al.,
2004). Among the 50 soccer players aged 13.0–13.99
years, 78% were in pubic hair stage 3 or beyond,
while among the 43 players 14 years of age, 65%
were in pubic hair stage 4 or higher. The data thus
suggested that the players were, as a group, advanced
in pubertal status. On the other hand, mean
predicted age at peak height velocity in players aged
13–14 years (14.0 + 0.6 years) was not consistent
with maturity status based on skeletal age and
percentage of predicted mature height, but was
similar to that for the longitudinal samples upon
which the offset protocol was developed (13.8 + 0.9
years).
The discordant observation for predicted age at
peak height velocity based on the maturity offset
protocol in soccer players aged 13–14 years may
reflect error in the prediction equation, which has a
95% confidence interval of 1.18 years (Mirwald
et al., 2002). The equation includes interaction
terms for leg length and sitting height, age and leg
length, and age and sitting height. The ratio of leg
length to sitting height was, on average, slightly
higher in the cross-sectional sample of soccer players
compared with one of the longitudinal series upon
which the maturity offset equation was developed,
specifically one year before, at and one year after
peak height velocity (Table I). Sampling per se and/or
population variation in the proportions of the
extremities (leg length) and trunk (sitting height)
may be additional factors.
Predicted age at peak height velocity based on the
maturity offset protocol in the total sample of youth
soccer players was 14.0 + 0.5 years. The mean was
consistent with estimates for three longitudinal
samples of youth soccer players that used different
models for the fitting of individual height records:
14.2 + 0.9 years (Danish, n ¼ 8; Froberg, Anderson,
& Lammert, 1991), 14.2 + 0.9 years (Welsh, n ¼ 32;
Bell, 1993), and 13.8 + 0.8 years (Belgian, n ¼ 33,
Philippaerts et al., 2006). The study of Belgian
players started when players were aged 10.4 to 13.7
years. Among those for whom complete longitudinal
data over 4–5 years were available, age at peak height
velocity could not be estimated in 43 boys. Twentyfive players experienced peak height velocity before
the start of the study; the players were early maturing
with a skeletal age (13.5 + 1.2 years) in advance of
chronological age (12.6 + 0.5 years) at the start of
the study. On the other hand, 18 boys did not attain
peak height velocity during the course of the study;
the players tended to be later maturing with a skeletal
age (11.1 + 1.1 years) behind chronological age
(11.5 + 0.8 years) at the start of the study. Allowing
for players among whom peak height velocity already
occurred and did not occur, the overall mean age
at peak height velocity for the total sample was
probably earlier than 13.8 years (Philippaerts et al.,
2006).
The standard deviations for ages at peak height
velocity in the three samples of youth soccer players
(0.8 and 0.9 year) and in the three samples upon
which the offset protocol was developed (0.7, 0.8,
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Indicators of biological maturation in young athletes
1713
velocity were based on a standard deviation of
approximately one year, while those based on
percentages of predicted mature height were based
on z-scores calculated relative to standard deviations
for the reference sample. Classifications based on the
SA-CA difference were based on skeletal age +1.0
year of chronological age; one year approximated
standard deviations for skeletal age within individual
age groups 11–16 years in the Fels Longitudinal
Study (Malina, 2011; Roche, Chumlea, & Thissen,
1988). Classifications based on predicted ages at
peak height velocity were based on the mean age at
peak height velocity + one standard deviation for the
three samples upon which the offset protocol was
developed (Mirwald et al., 2002), while those for
percentage of predicted mature height were based on
age-specific z-scores for the Berkeley sample (Bayer
& Bayley, 1959).
The limited concordance between maturity classification based on predicted age at peak height
velocity and the other two indicators was likely due
to the reduced standard deviations for predicted ages
at peak height velocity compared with that in the
samples upon which the offset protocol was developed and other longitudinal studies of boys.
Although age-specific means for predicted ages at
peak height velocity in soccer players did not differ
between 11 and 14 years, standard deviations
increased with chronological age (13.8 + 0.3,
14.1 + 0.4, 14.0 + 0.6, and 14.0 + 0.6, respectively). A similar age-related trend was noted in
female artistic gymnasts (Malina et al., 2006a).
The relatively narrow distributions of predicted
ages at peak height velocity with the offset protocol
calls into question the method’s ability to differentiate players by maturity status. Classifications of
players into maturity groups on the basis of predicted
ages at peak height velocity among players aged 11–
12 years (late 2, on time 84, early 1; Table IV) and
13–14 years (late 13, on time 77, early 3; Table V)
and 1.0 year) were larger than those for predicted
ages at peak height velocity in the current sample of
soccer players (0.5 year). Standard deviations of
predicted ages at peak height velocity were also less
in elite youth ice hockey players aged 14–15 years
(0.4 to 0.6 year; Sherar et al., 2007) and in junior
rugby players aged 12.9–15.9 years (0.5 to 0.6 year;
Till et al., 2010). Standard deviations for 38 mean
ages at peak height velocity in longitudinal studies of
European and North American boys using a variety
of protocols (graphic, splines, Preece-Baines model
I, kernel regression, etc.; Malina & Beunen, 1996;
Malina et al., 2004) ranged from 0.8 to 1.3 years with
a mean of 1.02 years. Predicted ages at peak height
velocity with the maturity offset protocol thus have a
relatively narrow distribution. The reduced variation
was also reflected in standard deviations for maturity
offset in half-year age groups of the youth players
(Table VII). Similar observations were noted in
female gymnasts (Malina et al., 2006a). Mean age at
peak height velocity in 13 gymnasts (Preece-Baines
model I) was 12.9 + 1.5 years; mean predicted ages
at peak height velocity based on the offset protocol
increased from 12.1 to 12.9 years with each
chronological age from 10 to 15 years, while
standard deviations of predicted ages at peak height
velocity ranged from 0.21 to 0.42 year (Malina et al.,
2006a).
Classification of youth into contrasting maturity
categories (late, on time, early) on the basis of
percentage of predicted adult height and predicted
age at peak height velocity with the offset protocol
have not been validated against classifications based
on an established maturity indicator in youth soccer
players. Kappa coefficients indicated relatively poor
agreement between maturity classifications based on
specific pairs of indicators. This may reflect in part
the methods of classifying players into late maturing,
on time, and early maturing categories. Classifications with SA-CA and predicted ages at peak height
Table VII. Sample sizes and descriptive statistics for chronological age and maturity offset by half-year age groups in the reference sample
upon which the protocol was developeda and predicted maturity offset in the cross-sectional sample of soccer players.
Reference sample
n
149
150
172
179
183
172
176
174
148
a
Soccer players
Age (years)
Maturity offset (years) (mean + s)
n
Age (years) (mean + s)
Maturity offset (years) (mean + s)
11.0
11.5
12.0
12.5
13.0
13.5
14.0
14.5
15.0
72.70 + 0.83
72.23 + 0.86
71.76 + 0.86
71.31 + 0.91
70.78 + 0.89
70.34 + 0.89
0.21 + 0.86
0.55 + 0.86
1.04 + 0.87
13
34
20
15
5
27
42
8
16
11.1 + 0.07
11.5 + 0.14
11.9 + 0.13
12.5 + 0.11
12.9 + 0.07
13.6 + 0.13
14.0 + 0.15
14.4 + 0.18
15.0 + 0.14
72.55 + 0.42
72.32 + 0.34
72.01 + 0.25
71.75 + 0.43
71.08 + 0.45
70.61 + 0.57
0.04 + 0.62
0.59 + 0.56
0.99 + 0.46
Data for the reference sample provided by R. L. Mirwald. Half-year age groups: 11.5 ¼ 11.25 to 11.74, 12.0 ¼ 11.75 to 12.24, etc.
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1714
R. M. Malina et al.
were quite different from classifications based on SACA and also from classifications based on percentage
of predicted adult height in each age group.
Although classifications were not expected to correspond exactly, the observation that the maturity
offset protocol classified the overwhelming majority
of players as on time in maturation has implications
for the application of the protocol to predict the
maturity timing of players in developmental programmes. For example, the LTAD model calls for
classification of youth based on late, average, and
early to design ‘‘appropriate training and competition programs in relation to optimal trainability and
readiness’’ (Balyi et al., 2005, p. 23). The limitation
of the maturity offset protocol to differentiate players
at the extremes of the maturity continuum requires
further evaluation.
Concordance of maturity classifications based on
skeletal age and percentage of predicted mature
height in youth American football players using the
same procedures as in the present study was similar
in 65 boys aged 11–12 years (kappa ¼ 0.24) but
better in 33 boys aged 13–14 years (kappa ¼ 0.38)
(Malina, Dompier, Powell, Barron, & Moore, 2007;
data were re-calculated so that age groups were
defined in the same way). Kappa coefficients were
0.23 in both age groups of soccer players (Tables IV
and V). Overall agreement between classifications in
the two age groups of American football players was
52% and 67%, respectively, which was generally
comparable to relative agreement in the two age
groups of soccer players (57% and 63%, respectively).
Distributions of stages of pubic hair relative to the
other maturity indicators were variable (Table VI).
With skeletal age as the criterion, late maturing 11–
12 year old players were pre- (pubic hair stage 1) or
early pubertal (stage 2), while the late maturing 13–
14 year old players were early- (stage 2) or midpubertal (stage 3). Pubic hair stages 1 through 3, and
stages 2 through 5 were represented among players
classified as on time and early in the younger and
older age groups, respectively. Variation was similar
with the two non-invasive indicators, but was limited
by the small number of late and early maturing
players with predicted age at peak height velocity as
the criterion, and of late maturing players with
percentage of predicted mature height as the
criterion. In longitudinal studies of European boys,
peak height velocity occurred most often when boys
were in pubic hair stages 3 and 4 (Malina et al.,
2004).
The four maturity indicators used in the present
study measured different but related aspects of
biological maturation during male adolescence.
Skeletal age reflects the maturation of the skeletal
system, specifically ossification of cartilaginous en-
dochondral bones of the hand–wrist. Stage of pubic
hair is only one aspect of the overt manifestation of
sexual maturation. In contrast, percentage of predicted mature height and predicted age at peak
height velocity are indicators of somatic maturation,
specifically progress in height towards the mature or
adult value and the timing of maximal rate of growth
in height during the growth spurt, respectively. Two
of the indicators marked maturity status (stage of
pubic hair, percentage of predicted mature height)
while two marked maturity timing (SA/CA ratio or
SA-CA, predicted age at peak height velocity).
Although the four maturity indicators were related,
interrelationships varied somewhat with age (Table
III). Among players aged 11–12 years, results of the
factor analysis indicated the two dimensions of
maturation, timing and status, but among players
aged 13–14 years, the four indicators were highly
related and suggested a general maturity factor at
ages associated, on average, with maximal growth in
height during the male adolescent spurt. Nevertheless, a portion of the variance in the two age
groups was not explained. It is thus possible that
differences in maturation among the specific systems
may have influenced the limited congruence between
specific pairs of indicators.
The prediction of mature height in the present
study included parents’ heights as reported on
national identifications cards. Heights were measured and recorded to the nearest centimetre.
Although the values had an error component, the
protocol was probably consistent with records of
military conscripts, which have been widely used in
studies of secular change. Heights of parents of
soccer players were 171.1 + 6.0 cm and 158.3 +
5.2 cm for fathers and mothers, respectively. The
parents were shorter, on average, compared with
parents in the Fels study upon whom the prediction
equations were based, but mean height of the fathers
was similar to that for 18-year-old males in the
Aveiro and Coimbra districts (Padez, 2003). Parent–
son correlations for heights of parents and soccer
players were generally consistent with the literature
for youth: father–son, 0.20; mother–son, 0.29;
midparent–son, 0.31 (Malina et al., 2004; Roche &
Sun, 2003).
Predicted height at 18 years of age of the soccer
players was 173.8 + 6.0 cm without skeletal age in
the equation (Khamis & Roche, 1994) and 173.2 +
6.0 cm with skeletal age in the equation (Khamis &
Guo, 1993). The mean predicted young adult height
was greater than that for 18-year-old males measured
in 2000 at the military district recruiting centres of
Aveiro and Coimbra (171.9 + 6.4 and 171.3 +
6.4 cm, respectively) and for the national sample of
18-year-old males (172.1 + 6.4 cm) (Padez, 2003).
The predicted young adult height of the soccer
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Indicators of biological maturation in young athletes
players (173.8 + 6.0 cm) was also similar to that for
non-elite U-19 soccer players (18.1 + 0.6 years)
from the Porto region (174.0 + 6.0 cm), but shorter
than the height of elite U-19 players (18.3 + 0.5
years) from the same region (176.0 + 7.0 cm) (A.
Seabra, unpublished).
Although the non-invasive maturity estimates have
limitations, they may be potentially useful in affording more opportunities to players who are less
mature than age peers and in developmental
programmes where technically skilled yet less mature
players may be overlooked due to maturity-associated limitations in physical and functional capacities (smaller size, less strength, power, and speed).
This is important in early- and mid-adolescence,
about 11–14 years, when sorting of players typically
occurs (Figueiredo, Gonçalves, Coelho e Silva, &
Malina, 2009a). Using overall performance as a
guide typically favours players who are more
advanced in maturity status (larger size, stronger,
more powerful, faster). Although this strategy may
enable a coach to field the best team at the current
time, it may increase the likelihood that potentially
talented players are lost to a sport since performance
advantages associated with early maturation are
generally transient (Malina et al., 2004). It is also
important to recognize the psychological and social
implications associated with asking players to train or
compete with players who are older or younger.
Younger players who are advanced in maturity status
may be unable to cope with the emotional or
cognitive demands associated with competing with
older athletes, while older players who are later in
maturity may perceive being asked to play with
younger players as a negative judgement on their
ability. The same applies when players move down or
up competitive categories. In any one age group of
youth players who differ substantially in biological
maturity, the variation in physical and behavioural
development among them is relatively large. By
inference, there is a need to further study the
behavioural implications of individual differences in
biological maturation among young athletes.
In summary, the results are limited to a crosssectional sample of Portuguese youth soccer players
11–14 years of age. Although the four indicators
considered were interrelated, there was relatively
poor agreement between maturity classifications
based on SA-CA and those based on percentage of
predicted mature height and predicted age at peak
height velocity. Use of pubic hair as an indicator was
limited by lack of comparative data, which are
generally reported as estimated ages at onset of
specific stages. Stage of pubic hair does not provide
information on the age at which a player entered the
stage or how long he has been in the stage. There is a
need for further consideration of non-invasive
1715
indicators relative to skeletal age based on the
Greulich-Pyle and Tanner-Whitehouse methods of
assessment (Malina, 2011). Care is thus warranted in
applying non-invasive protocols.
Given the worldwide popularity of soccer, the
ethnically diverse composition of professional teams,
and interest in youth players from developing
countries, several issues among others merit further
study. Skeletal and sexual maturation and the
proportions of sitting height and leg length to stature
vary among ethnic/racial groups (Malina, 2011;
Malina et al., 2004), and protocols for the prediction
of mature height are based on populations of
European ancestry. Further research with a larger
sample that includes players of different ethnic
backgrounds would be a start.
Acknowledgements
This research was partially supported by Fundação
para a Ciência e a Tecnologia [PTDC/DES/121772/
2010]. The patience and cooperation of the young
athletes and coaches are acknowledged and appreciated.
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