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Anthropometric and performance measures for thedevelopment of a talent detection and identificationmodel in youth handballHasan Mohamed a , Roel Vaeyens a , Stijn Matthys a , Marc Multael a , Johan Lefevre b ,Matthieu Lenoir a & Renaat Philippaerts aa Department of Movement and Sports Sciences, Faculty of Medicine and Health Sciences,Ghent University, Gentb Department of Biomedical Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences,Katholieke Universiteit Leuven, Leuven, BelgiumPublished online: 04 Feb 2009.

To cite this article: Hasan Mohamed , Roel Vaeyens , Stijn Matthys , Marc Multael , Johan Lefevre , Matthieu Lenoir & RenaatPhilippaerts (2009) Anthropometric and performance measures for the development of a talent detection and identificationmodel in youth handball, Journal of Sports Sciences, 27:3, 257-266, DOI: 10.1080/02640410802482417

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Anthropometric and performance measures for the developmentof a talent detection and identification model in youth handball

HASAN MOHAMED1, ROEL VAEYENS1, STIJN MATTHYS1, MARC MULTAEL1,

JOHAN LEFEVRE2, MATTHIEU LENOIR1, & RENAAT PHILIPPAERTS1

1Department of Movement and Sports Sciences, Faculty of Medicine and Health Sciences, Ghent University, Gent and2Department of Biomedical Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, Katholieke Universiteit Leuven,

Leuven, Belgium

(Accepted 16 September 2008)

AbstractThe first part of this study examined in which basic morphological and fitness measures Under-14 (n¼ 34) and Under-16(n¼ 47) male youth handball players differ from reference samples of the same age (n¼ 430 and n¼ 570, respectively). Tohelp develop a talent identification model, the second part of the study investigated which specific morphological andperformance measures describe differences between elite (n¼ 18) and non-elite (n¼ 29) Under-16 youth handball players.The results showed that Under-16 handball players were significantly taller than the reference group; this was not the case inthe Under-14 age group. Physical fitness in handball players was significantly better than in the reference groups.Multivariate analysis of covariance (maturation and chronological age as covariates) showed that the Under-16 elite playerswere heavier and had greater muscle circumferences than their non-elite peers. Elite players scored significantly better onstrength, speed and agility, and cardiorespiratory endurance but not on balance, upper limb speed, flexibility or upper bodymuscular endurance. Maturation was a significant covariate in anthropometric measures but not in physical performance.Discriminant analysis between elite and non-elite players revealed that height, running speed, and agility are importantparameters for talent identification. Specific anthropometric measures, in addition to some performance measures, are usefulfor talent identification in youth handball.

Keywords: Anthropometry, performance, talent, identification, team handball

Introduction

Talent identification has long been of great interest

to coaches, administrators, communities, and gov-

ernments (Abbott, Button, Pepping, & Collins,

2005; Abbott & Collins, 2004; Holt & Dunn,

2004; Morris, 2000; Nieuwenhuis, Spamer, & Van

Rossum, 2002). The talent identification process can

be separated into three stages or components:

detection, selection, and identification (Williams &

Franks, 1998). Talent detection refers to the

discovery of potential performers who are currently

not involved in the sport in question. Talent

identification alludes to the process of recognizing

current participants with the potential to become

elite players. Talent selection involves the ongoing

process of identifying players at various stages who

demonstrate prerequisite standards of performance

for inclusion in a particular team. It is focused on

choosing the most appropriate individual or group of

individuals who can best carry out the task within a

specific context. The concept of ‘‘talent’’ is even

more complex and refers both to a certain genetic

predisposition (Bouchard, Malina, & Perusse, 1997)

and to the capacity to improve performance char-

acteristics through intensive practice (Ericsson,

Krampe, & Tesch-Romer, 1993; Reilly, Williams,

Nevill, & Franks, 2000b). Identifying individuals

with the greatest potential to excel in sport presents

a major challenge for those involved (Abbott &

Collins, 2004).

Many sport scientists and coaches from relatively

small nations believe talent identification to be an

essential component of their elite sports development

programmes. The rationale is that these countries

do not have sufficiently large populations to rely on

Correspondence: R. Philippaerts, Department of Movement and Sports Sciences, Faculty of Medicine and Health Sciences, Ghent University, Watersportlaan

2, B-9000 Gent, Belgium. E-mail: renaat.philippaerts@ugent.be

Journal of Sports Sciences, February 1st 2009; 27(3): 257–266

ISSN 0264-0414 print/ISSN 1466-447X online � 2009 Taylor & Francis

DOI: 10.1080/02640410802482417

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a trial-and-error approach that eventually allows

some athletes to reach the elite standard in sport by

a process known as natural or self-selection

(Bloomfield, Ackland, & Elliott, 1994). Interest in

effectively identifying and developing sporting talent

has therefore grown in many countries over recent

years. A number of different approaches and models

have been adopted in the hope of finding a way to

identify talent (Falk, Lidor, Lander, & Lang, 2004;

Hoare & Warr, 2000; Lidor et al., 2005; Morris,

2000; Pienaar, Spamer, & Steyn, 1998; Reilly,

Bangsbo, & Franks, 2000a; Vaeyens et al., 2006;

Williams, 2000). Several studies have assessed the

anthropometric, physiological, psychological, and

motor skill attributes of individual sports (Bencke

et al., 2002; Gabbet, 2000; Kukolj, Ugarkovic, &

Jaric, 2003; Saenz-Lopez, Ibanez, Gimenez, Sierra,

& Sanchez, 2005; Thelwell, Greenlees, & Weston,

2006). The progression from youth to elite sport is a

complex process. Identifying talent for field games at

an early age is far from being a mechanistic process.

It is more complex in team sports than in individual

sports with discrete objective measures of perfor-

mance (Reilly et al., 2000b).

From the available literature, it is apparent that

relatively little work has been done on talent

identification in team handball. Most of the literature

on team handball focuses on specific injuries sus-

tained (Myer, Ford, Brent, & Hewett, 2007; Nielsen

& Yde, 1988; Olsen, Myklebust, Engebretsen, &

Bahr, 2005; Seil, Rupp, Tempelhof, & Kohn, 1998).

Only a few studies have evaluated specific perfor-

mance measures in relation to youth handball players

from different playing standards (Bencke et al., 2002;

Bergemann, 1999; Hatzimanouil & Oxizoglou,

2004). One paper questioned the use of a specific

test battery for identification of talented Israeli

handball players, stating that it is difficult to reflect

the requirements of senior players in the test battery

used at a younger age (Lidor et al., 2005). Identifying

characteristics important for talent identification and

selection in young handball players has proved to be a

challenging task.

Team handball is a dynamic sport characterized by

highly developed motor skills such as speed and

agility, reaction speed, explosive power, endurance,

strength, as well as its coordination (Hatzimanouil &

Oxizoglou, 2004). It is played at amateur, semi-

professional, and professional levels and it has been

an Olympic sport since 1972 (Langevoort,

Myklebust, Dvorak & Junge, 2007). Team handball

is a popular sport in Europe and North Africa, and

was first introduced in Belgium in 1946. Today,

there are 7886 registered players in Belgium, of

whom 5050 are enrolled in Flanders – out of a total

Flemish population of 6,079,000 (Studiedienst van

de Vlaamse Regering, 2007). Within Europe,

Belgium together with Slovenia, Cyprus, Luxemburg

and Malta are the five smallest countries. Bloomfield

et al. (1994) stated that small nations or federations

can profit from structured talent detection and

identification programmes using objective data. In

the present study, a multivariate approach to the

problem of talent identification and selection is

adopted.

The aims of this study were twofold. First, to

improve talent detection, we wished to determine the

morphological and fitness measures that differentiate

male youth team handball players from a Flemish

reference sample. Second, to aid in talent identifica-

tion, we wanted to identify specific morphological

and performance measures that differentiate between

elite and non-elite youth handball players, taking

maturation status into account.

Methods

Participants

In the first part of the 2005–2006 season (September,

October, and November, after the preparation

phase), 91 male youth handball players aged 12–16

years were enrolled. The Under-14 age group

consisted of 40 players (chronological age between

12.0 and 13.9 years), including six goalkeepers. The

Under-16 age group comprised 51 players (chrono-

logical age between 14.0 and 15.9 years), including

four goalkeepers. The participating clubs, all from

the Flemish part of the country, played in the first,

second or third division of the Belgian Handball

Federation.

The participants were assigned to groups based on

their expertise. Under-16 players who only repre-

sented their club were assigned to a non-elite group,

whereas those who were selected for the Flemish

selection team were assigned to an elite group

(selection was based on observations by the coaches

responsible from the federation). As there is no elite

standard at the Under-14 level, this resulted in a

non-elite group only. All players were tested at the

same location (research laboratory) to ensure the test

environment remained constant. The day of testing

was scheduled to replace a training session. No

further physical training was performed during the

test session.

In general, all handball players participated in two

training sessions and one game per week during the

competition. Training sessions last about 2 h,

including the warm-up and cool-down. The elite

Under-16 players did not perform additional training

sessions during the week. Occasionally, one week

before a selection game, elite players assembled for a

training session. In Flanders, it is not common to

organize systematic training for elite youth players.

258 H. Mohamed et al.

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Because of the specific profile of goalkeepers,

they were excluded from further analysis, resulting

in 34 Under-14 and 47 Under-16 (29 non-elite

and 18 elite) outfield players. The non-elite players

were from eight different clubs, whereas the elite

players played for several other clubs in the

Flemish region. The study was approved by the

Ethics Committee of Ghent University Hospital.

Informed consent was provided by the players and

their parents.

Flemish reference values for general physical

fitness were obtained from a large-scale study

conducted in 2004–2005 in a representative sample

of Flemish secondary schools with respect to

geographic region, school system (Catholic, govern-

ment, provincial, city), and education system (gen-

eral, technical, vocational). More information

regarding this study can be found in Duvigneaud

et al. (2006).

Procedures

PART ONE: TALENT DETECTION

Morphological and physical fitness measures. The

EUROFIT test battery (Council of Europe, 1988)

was used to assess physical fitness. All tests were

performed in bare feet (except the endurance shuttle

run) following EUROFIT guidelines. In addition to

these fitness tests, height, body mass, and five

skinfolds (biceps, triceps, subscapular, anterior

suprailiac, and medial calf) were measured with the

participants wearing minimal clothing.

PART TWO: TALENT IDENTIFICATION

Anthropometry. In addition to traditional anthropo-

metric data (height, body mass, and skinfolds),

specific anthropometric measurements, including

sitting height, arm length, arm span, dominant hand

length, dominant hand span, four muscle circumfer-

ences (extended and flexed upper arm, mid thigh

and maximum calf), were made following standar-

dized protocols (Lohman, Roche, & Martorell,

1988). Height was measured with a portable

Martin anthropometer (+0.1 cm; Siber-Hegner,

Switzerland) and body mass with a Seca balance

(+0.1 kg). Skinfolds were measured with a Harpen-

den calliper, circumferences with a metal tape

(Holtain, UK). Arm length was calculated as the

difference between acromial and dactylion height.

Acromial and dactylion height were also measured

with the portable Martin anthropometer. Arm span

was measured as the distance between the tips of the

middle fingers of each hand when both arms are

extended laterally and maximally at shoulder level,

with the players standing against a wall. Hand length

was measured with a sliding calliper (Holtain, UK)

from the styloid process of the radius to the tip of

the middle finger. Hand span was measured with the

same sliding calliper as the distance between the

external points of thumb and the little finger when

the hand is maximally spread. All one-sided mea-

sures were taken on the left side of the body except

for hand length and hand span, where the dominant

side was measured. All measures were taken by the

same experienced researcher.

Handball-specific motor skills. Besides the EUROFIT

measures, explosive power was measured by means

of a vertical jump, following the procedures

described by Seminick (1994). In addition to the

106 5-m shuttle run from the Eurofit test battery,

running speed was also measured using the

56 10-m shuttle sprint (Verheijen, 1997). A hand-

ball agility test measured the speed and agility when

specific defensive sliding movements had to be

carried out. The player was instructed to run forward

in a straight line and touch a spot marked on the

floor 3 m from the starting position with one foot.

Then the player had to slide diagonally and back-

wards to the stand, which was positioned 2.5 m to

the right of the starting position. After touching the

stand with one hand, the player had to move back to

the starting position and repeat the same cycle on the

left side. When returning to the starting position, a

second cycle was to be performed immediately. The

time to finish the two cycles was recorded as the

player’s performance. This test showed moderate to

good reliability (test–retest period of one week) with

an intraclass correlation coefficient of 0.81

(P5 0.001) in a sample of 31 players within the

same age range. Figure 1 provides an overview of the

test procedure.

Maturation. To estimate the maturation status of

the handball players, a maturation index was calcu-

lated using Mirwald and colleagues’ equation (3)

(Mirwald, Baxter-Jones, Bailey, & Beunen, 2002).

This technique is a non-invasive and practical

method of predicting years from peak height velocity

(PHV) as a measure of maturity offset using anthro-

pometric variables. Negative values should be inter-

preted as pre-PHV and positive values as post-PHV.

The equation reported by Mirwald et al. (2002) is

as follows: maturity offset¼79.236þ 0.0002708 �(leg length � sitting height) 70.001663 � (age � leg

length)þ0.007216 � (age � sitting height)þ0.02292 �(weight/height), with R¼ 0.94, R2¼ 0.891, and the

standard error of estimate¼0.592. However, because

the Under-16 group covers 2 years of chronological age

(from 14.0 to 14.99 years and from 15.0 to 15.99

years), mean age at PHV was calculated for each

Talent identification in youth team handball 259

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one-year age band separately. A maturation index was

then calculated as the difference between the individual

estimated maturity offset and the mean age at PHV for

each age band. Negative values should be considered as

relatively early maturation and positive values as

relatively late maturation.

Analysis

PART ONE: TALENT DETECTION

To develop a potential detection model, data for

height, body mass, sum of five skinfolds, and the

EUROFIT measures from the Under-14 and Under-

16 handball players were compared with the means

from the Flemish reference populations for the same

age groups using one-sample t-tests. These t-tests

were used to compare the mean score of the handball

sample to the population mean (Flemish reference

values).

PART TWO: TALENT IDENTIFICATION

The Under-16 group with two levels of expertise

(elite and non-elite) was used for talent identification

purposes. A multivariate analysis of covariance

(MANCOVA) was applied with expertise (elite vs.

non-elite) as the fixed factor and the anthropometric

and performance measures as dependent variables.

The maturity index and chronological age were used

as covariates to take maturation and age differences

into account. Stepwise discriminant analysis with

expertise as the dependent variable (non-elite vs.

elite) was used to identify the most important

characteristics for the identification process in the

Under-16 group.

Normality of distributions was checked. In all

subsamples, sum of skinfolds and plate tapping were

logarithmically transformed (log10) because of their

positively skewed distribution, as shown by the

Shapiro-Wilk test for normality. All analyses were

performed using SPSS Version 15.0. Statistical

significance was set at P5 0.05.

Results

PART ONE: TALENT DETECTION

The results of the anthropometric measurements

and fitness parameters measured by the EUROFIT

test battery for the Flemish reference population

and Under-14 handball players are shown in

Table I. The means for the handball group for

age, height, sum of skinfolds, flexibility, plate

tapping, and hand grip were not significantly

different from those of the Flemish reference

population. However, for flamingo balance, stand-

ing long jump, sit-ups, bent arm hang, 106 5-m

shuttle run, and endurance shuttle run, the mean

values differed significantly with the better scores

for the handball players. Also, the mean body mass

of the Under-14 handball players was significantly

different from the reference value.

Figure 1. Schematic representation of the handball sliding test.

Table I. Comparison of the Under-14 male handball players

(mean+ s) with the Flemish reference population (mean) for

anthropometric and fitness parameters using the one-sample t-test.

Variable

Flemish

reference

(n¼430)

Handball

players

(n¼ 34) t (d.f.) P

Age 13.1 13.1+ 0.5 70.980 (33) N.S.

Body mass (kg) 48.4 45.1+ 8.3 72.297 (33) *

Height (m) 1.59 1.57+ 0.09 71.390 (33) N.S.

Sum of five

skinfold (mm)

49.4 42.2+ 19.0 71.487 (33) N.S.

Flamingo

balance (n)

16.7 12.1+ 4.6 75.626 (32) ***

Sit-and-reach (cm) 16.2 17.6+ 6.9 1.192 (33) N.S.

Plate tapping (s) 12.1 12.5+ 1.4 1.832 (33) N.S.

Standing long

jump (cm)

168.7 178.1+ 21.2 2.588 (33) *

Hand grip (kg) 24.3 25.1+ 7.5 0.674 (33) N.S.

Sit-ups (n) 22.3 25.3+ 3.8 4.587 (33) ***

Bent arm hang (s) 15.3 23.9+ 15.1 3.338 (33) **

Shuttle run (s) 22.2 20.3+ 1.2 78.814 (33) ***

Endurance shuttle

run (min)

6.8 8.0+ 1.7 4.124 (32) ***

Notes: *P5 0.05; **P5 0.01; ***P5 0.001; N.S.¼not significant.

260 H. Mohamed et al.

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Table II shows the same comparison for the

Under-16 age groups. No significant differences in

mean values were found for age, body mass, sum

of skinfolds, flexibility or plate tapping. The

Under-16 handball players were, however, signifi-

cantly taller and outperformed Flemish youth on

all fitness tests except for plate tapping and

flexibility.

PART TWO: TALENT IDENTIFICATION

Results of MANCOVA comparing elite and non-

elite Under-16 handball players are shown in

Table III. Chronological age was not different

between the elite (14.9+ 0.6 years) and the non-

elite players (15.0+ 0.6 years). However, to elim-

inate possible differences in performance outcome

caused by real differences in chronological age (boy

of 14.1 years vs. boy of 15.8 years), both matura-

tional status and chronological age were used as a

covariate.

Of the anthropometric variables, chronological age

appeared to have a confounding effect on height,

sitting height, arm length, and arm span. The

maturation index showed a significant effect on all

anthropometric measures except for sum of skinfolds

and relative arm length. Elite handball players were

heavier and had significantly higher values for

absolute and relative arm length and arm span.

Moreover, in general, elite players had greater upper

Table II. Comparison of the Under-16 male handball players

(mean+ s) with the Flemish reference population (mean) for

anthropometric and fitness parameters using the one-sample t-test.

Variable

Flemish

reference

(n¼ 570)

Handball

players

(n¼ 47) t (d.f.) P

Age 15.0 15.0+ 0.6 0.162 (46) N.S.

Body mass (kg) 59.1 61.3+ 9.4 1.593 (46) N.S.

Height (m) 1.71 1.74+ 0.08 2.311 (46) *

Sum of five

skinfold (mm)

44.2 44.2+ 15.8 1.573 (46) N.S.

Flamingo

balance (n)

15.3 11.8+ 4.0 75.880 (45) ***

Sit-and-reach (cm) 18.2 19.9+ 8.5 1.362 (46) N.S.

Plate tapping (s) 11.0 10.9+ 1.1 70.528 (46) N.S.

Standing long

jump (cm)

188.8 203.6+ 21.7 4.663 (46) ***

Hand grip (kg) 33.8 39.9+ 10.8 3.842 (46) ***

Sit-ups (n) 24.8 26.6+ 3.7 3.268 (46) **

Bent arm hang (s) 23.3 33.4+ 15.5 4.981 (46) ***

Shuttle run (s) 21.4 18.7+ 1.4 713.319 (46) ***

Endurance shuttle

run (min)

8.2 9.6+ 1.4 6.878 (46) ***

Notes: *P50.05; **P50.01; ***P50.001; N.S.¼not significant.

Table III. Results of the MANCOVA with chronological age (age) and maturation as covariates: differences by expertise (elite vs. non-elite)

in Under-16 male handball players.

Variable Non-elite (n¼29) Elite (n¼18)

Covariates

MANCOVAAge Maturation

Body mass (kg) 57.5+ 9.3 67.1+ 6.4 N.S. *** *

Height (m) 1.71+ 0.08 1.79+ 0.04 * *** N.S.

Sum of five skinfolds (mm) 43.3+ 16.1 45.6+ 16.0 N.S. N.S. N.S.

Sitting height (cm) 87.8+ 4.7 92.0+ 3.2 *** *** ns

Leg length (cm) 82.8+ 4.5 86.6+ 2.5 N.S. ** ns

Arm length (cm) 74.5+ 4.5 79.2+ 2.7 * *** *

Arm length to height (%) 43.6+ 1.1 44.3+ 1.2 N.S. ns *

Arm span (cm) 171.6+ 10.0 182.6+ 4.8 * *** **

Hand span, dominant hand (cm) 20.8+ 1.8 22.2+ 0.8 N.S. ** N.S.

Hand length, dominant hand (cm) 17.8+ 1.1 18.7+ 0.8 N.S. *** N.S.

Upper arm circumference, stretched (cm) 24.6+ 2.3 27.3+ 2.1 N.S. * **

Upper arm circumference, flexed (cm) 27.0+ 2.6 30.5+ 2.1 N.S. * ***

Thigh circumference (cm) 51.1+ 4.2 53.0+ 5.2 N.S. ** N.S.

Calf circumference (cm) 33.8+ 2.8 37.3+ 4.9 N.S. *** N.S.

Flamingo balance (n) 12.1+ 4.3 11.2+ 3.4 N.S. N.S. N.S.

Plate tapping (s) 11.0+ 1.1 10.6+ 0.9 N.S. N.S. N.S.

Sit-and-reach (cm) 18.3+ 8.2 21.6+ 8.4 N.S. N.S. N.S.

Standing long jump (cm) 194.2+ 21.2 218.7+ 12.3 N.S. N.S. **

Hand grip (kg) 35.6+ 11.0 46.4+ 6.6 N.S. ** *

Sit-ups (n) 25.5+ 2.7 28.5+ 4.2 N.S. N.S. **

Bent arm hang (s) 31.3+ 17.5 36.8+ 11.6 N.S. N.S. N.S.

Shuttle run 1065 m (s) 19.4+ 1.2 17.5+ 0.6 N.S. N.S. ***

Endurance shuttle run (min) 9.2+ 1.4 10.3+ 1.2 N.S. N.S. **

Vertical jump height (cm) 43.5+ 7.0 51.3+ 5.2 N.S. N.S. **

Shuttle sprint 5610 m (s) 14.8+ 0.9 13.7+ 0.7 N.S. N.S. **

Handball-specific shuttle run (s) 15.6+ 1.5 13.7+ 1.0 N.S. N.S. ***

Notes: *P5 0.05; **P50.01; ***P50.001; N.S.¼not significant.

Talent identification in youth team handball 261

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body muscle circumferences than their non-elite

counterparts.

Regarding the performance measures, the matura-

tion index was a significant covariate for hand grip.

Elite players were significantly better than their non-

elite counterparts on standing long jump, hand grip,

sit-ups, 106 5-m shuttle run, endurance shuttle run,

vertical jump, 56 10-m shuttle sprint, and handball-

specific shuttle run.

The discriminant analysis classified 87.2% of the

elite and non-elite players in the correct group,

revealing 106 5-m shuttle run and height as the

most important discriminators (Table IV).

Discussion

The aim of the present study was to identify the

anthropometric measures and fitness tests that best

describe the specific characteristics of male youth

handball players between 12 and 16 years of age for

the development of a model for talent detection and

identification.

A limited number of studies have assessed some of

the anthropometric, physical fitness, and handball-

specific characteristics of youth and adult male

handball players (Bergemann, 1999; Gorostiaga,

Granados, Ibanez, & Izquierdo, 2005; Ibnziaten

et al., 2002; Lidor et al., 2005). However, compar-

isons are difficult to make because of different age

ranges and test procedures. Regarding height, body

mass, and explosive power (measured by standing

long jump), handball players appear to be positioned

between volleyball players on the one hand and

soccer and field hockey players on the other. In a

study by Lidor and colleagues (Lidor, Hershiko,

Bilkevitz, Arnon, & Falk, 2007), the height, body

mass, and standing long jump of elite volleyball

players aged 16.4 years was 1.89+ 0.03 m, 75.6+5.9 kg, and 231+ 11 cm respectively. The volleyball

players were somewhat older than our Under-16

group and this could explain the higher values for the

former. Our elite Under-16 players and the selected

14-year-olds from Lidor et al. (2005) have compar-

able values, including standing long jump (218.7+12.3 vs. 215.8+ 17.0 cm). On the other hand,

Under-16 elite youth soccer and field hockey players

show lower values for height, body mass, and

standing long jump (Elferink-Gemser, Visscher,

Lemmink, & Mulder, 2007; Gil, Ruiz, Irazusta,

Gil, & Irazusta, 2007; Malina et al., 2000; Vaeyens

et al., 2006).

To the best of our knowledge, no study has

evaluated all of these parameters in youth players of

varying standards taking into account the players’

maturation. In the present study, we used a non-

invasive indicator of maturation, applicable to both

boys and girls, using anthropometric measures and

chronological age as input variables. This cross-

validated technique permits calculation of the

individual distance (expressed in years) to the age

at PHV. The outcome variable can be used in a

categorical way, but also in a continuous assess-

ment (Mirwald et al., 2002). For statistical reasons,

we used this technique in a continuous manner.

Dichotomizing into early and late maturation

reduces the statistical power of the analysis.

Moreover, maturation is a continuous process and

therefore we preferred to use the maturation

index as a continuous variable in the present

analysis.

No significant differences in height or sum of

skinfolds were observed between the Under-14

handball players and their Flemish reference coun-

terparts. In contrast, the Under-16 handball players

were significantly taller than their Flemish reference

counterparts, although the difference was only

2.6 cm. Although height is an important variable in

team handball (Lidor et al., 2005), it would appear

that adolescent height is not important for talent

detection purposes. Adolescent height shows a large

variation in growth potential just before, at, and just

after puberty, mainly because of altered hormone

activity (Pearson, Naughton, & Torode, 2006). The

change in hormone activity is also responsible for the

gain in body mass and the advantage in muscular

development (Roemmich & Rogol, 1995). Because

of the large inter-individual variation in growth

during puberty, estimation of adult height should

complement the measurement of height at a given

age (Pearson et al., 2006).

Table IV. Summary of the stepwise discriminant analysis in the Under-16 group: variables entered/removed*.

Wilks’ lambda Exact F

Step Entered Statistic df1 df2 df3 Statistic df1 df2 Significance

1 Shuttle run 0.544 1 1 44.0 36.935 1 44.0 0.000

2 Height 0.466 2 1 44.0 24.660 2 43.0 0.000

Notes: At each step, the variable that minimizes the overall Wilks’ lambda is entered. Maximum number of steps is 34. *Maximum

significance of F to enter is 0.05; minimum significance of F to remove is 0.10. F-level, tolerance, or VIN insufficient for further

computation.

262 H. Mohamed et al.

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For physical fitness (balance, explosive power,

upper body and abdominal muscle strength

and endurance, running speed with agility, and

cardiorespiratory endurance), the Under-14 and

Under-16 handball players outperformed their

Flemish age-matched peers. These performance

characteristics correspond well with the general

physical prerequisites of handball players

(Hatzimanouil & Oxizoglou, 2004). However, flexi-

bility and speed of the upper limbs were the only

fitness measures in which no significant difference

was found between the handball players and their

age-matched reference groups. Note that the

106 5-m shuttle run was performed using different

protocols (the handball players performed the test

wearing shoes for reasons of sports specificity,

which was not the case for the reference group).

Even if the same procedure had been used, the

handball players would still have performed signifi-

cantly better. For talent detection purposes, clubs

and federations should look for children showing

good strength, explosive power, speed, agility, and

cardiorespiratory endurance together with the po-

tential to become tall. However, no information

about the maturity status from the reference group

was available, and therefore this could not be used in

the first part of the analysis.

For identification purposes, a comprehensive set

of anthropometric and performance measures was

included for the Under-16 group. Of the anthropo-

metric variables, significant differences between the

Under-16 elite and non-elite players were observed

for body mass, arm length, relative arm length, arm

span, and both upper limb muscle circumferences

(stretched and flexed). For the other anthropometric

measures, the elite players showed slightly, but not

significant, higher values than the non-elite group

(the elite players were 8 cm taller). However, an

important finding was the significant confounding

influence of chronological age and maturation. An

influence of maturity during puberty has been

observed in many game sports (Gil et al., 2007;

Malina, Ribeiro, Aroso, & Cumming, 2007; Vaeyens

et al., 2006). [For an extensive overview, the reader

is referred to Malina, Bouchard, & Bar-Or (2004).]

However, Lidor et al. (2005) did not observe

differences in height or body mass between selected

and non-selected handball players aged 12–14 years,

possibly because data on maturation were not

available for this younger age range. At odds with

their younger age, Israeli youth players were taller

and heavier than the Flemish elite youth players. It is

clear that absolute size of the body and body

segments is important in youth and adult handball.

This is in line with the findings of Hatzimanouil and

Oxizoglou (2004) and Skoufas and colleagues

(Skoufas, Kotzamanidis, Hatzikotoylas, Bebetsos, &

Patikas, 2003), who reported that anthropometry

and the size of the hand in particular should be taken

into account when selection criteria for handball

players are set. Similarly, Visnapuu and Jurimae

(2007) stated that hand size and finger length is

important for throwing accuracy. However, for the

long-term development of talented players, a careful

follow-up of the maturation process, including

growth of the most important body segments, is

necessary.

Regarding the performance measures, elite players

outperformed their non-elite counterparts on all tests

except for balance (flamingo balance), speed of

upper limbs (plate tapping), flexibility (sit-and-

reach), and upper body muscular endurance (bent

arm hang). However, in contrast to the anthropo-

metry resultss, chronological age and maturation did

not show significant covariate effects, with the

exception of the maturation effect on the hand grip

test. This is in line with the view that maturation is

generally more strongly related to anthropometric

characteristics than to measures of physical perfor-

mance (Beunen et al., 1978). Moreover, maturation

has been reported to explain a greater percentage of

the variability in muscle (maximal and static)

strength than that in several motor performance tests

that reflect neuromuscular maturation in children

and adolescents (Beunen, Ostyn, Simons, Renson, &

Van Gerven, 1981; Katzmarzyk, Malina, & Beunen,

1997). However, Vaeyens et al. (2006) found that

maturation had a marked effect on performance on

almost the same physical tests when comparing

Belgian elite, sub-elite, and non-elite youth soccer

players aged 12–16 years, but the observed differ-

ences in height, body mass, and adiposity were much

smaller. However, since chronological age, matura-

tion, height, and body mass are interrelated during

puberty and adolescence, it is difficult to show their

specific effects on performance on strength, speed,

and other physical tests (Malina et al., 2004).

Even in the talent detection analysis, no differ-

ences were observed between the reference group

and the handball players for speed of upper limbs or

flexibility. Although speed of upper limbs and

flexibility are likely to be important for team hand-

ball, the test procedures probably do not reflect the

appropriate property. It could be argued that upper

body flexibility (trunk and shoulder flexibility) and

strength are more related to specific properties

required in handball, even for injury prevention

(Baltaci & Tunay, 2004; Hahn, Foldspang,

Vestergaard, & Ingemann-Hansen, 1999; Jaric,

Ugarkovic & Kukolj, 2001; Zakas et al., 2003). In

contrast with the results of Lidor et al. (2005), we

found differences in favour of the elite players. The

gap between elite and sub-elite or non-elite in youth

handball is presumably smaller in Israel than in

Talent identification in youth team handball 263

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Flanders and may therefore explain the differences in

physical performance between the elite and non-elite

players. Moreover, these differences cannot be

explained by a higher training load. Elite and non-

elite players play one game per week and train on

average 2 h twice a week. It would appear that size is

an important factor in the Federation’s selection

criteria, as confirmed by the stepwise discriminant

analysis. The parameters highlighted by the discri-

minant analysis correspond well with characteristics

suggested as essential in youth and adult team

handball (Gorostiaga et al., 2005; Gorostiaga,

Granados, Ibanez, Gonzalez-Badillo, & Izquierdo,

2006; Hatzimanouil & Oxizoglou, 2004; Izquierdo,

Hakkinen, Gonzalez-Badillo, Ibanez, & Gorostiaga,

2002; Lidor et al., 2005; Vicente-Rodriguez,

Dorado, Perez-Gomez, Gonzalez-Henriquez, &

Calbet, 2004). Again, from this point of view, team

handball is characterized by a combination of size,

speed, and agility.

Some aspects of the present study need to be put

into perspective. Talent identification requires a

multidisciplinary approach, including morphologi-

cal, physical, technical, tactical, and psychological

aspects. This study was restricted to anthropometric

and fitness measures and did not include technical

and psychological measures. The use of individual

scores on a single technical measure can be proble-

matic within a talent identification process. Techni-

que is a complex characteristic in ball games and

consequently the use of a composite technique score

based on individual scores on several reliable and

relevant skills is would be useful (Malina et al.,

2007). Finally, the implementation of perceptual-

cognitive skills (e.g. decision-making), measures of

anxiety management, and task and ego orientation is

perceived as a more promising avenue for future

work in talent identification (Vaeyens, Lenoir,

Williams, & Philippaerts, 2007; Williams & Franks,

1998). Moreover, this was a cross-sectional study,

although two age groups were included in the data

analysis. Development of mixed-longitudinal identi-

fication programmes, within the structure of the

Handball Federation, is recommended for further

follow-up of talented youth players.

In summary, the present results demonstrate that

Under-14 and Under-16 youth handball players

outperformed Flemish reference boys in almost all

physical tests. However, the Under-14 handball

players were slightly lighter than, and the Under-16

players taller than, their Flemish reference counter-

parts. Elite and non-elite handball players differed in

morphological parameters and motor performance

measures, but maturation should be considered as an

important covariate. Specific anthropometric mea-

sures as well as some motor performance tests are

useful for talent identification in youth handball. The

use of a non-invasive measure of maturation is

recommended for use in talent identification

programmes.

Acknowledgements

We thank Linde Panis (Top Sport Coordinator) and

Ken De Nil (Technical Director) of the Flemish

Handball Association (VHV) for their cooperation in

this study.

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