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Identifying and developing rugby talent among10-year-old boys: A practical modelAnita E. Pienaar , Manie J. Spamer & Hendrik S. Steyn JrPublished online: 01 Dec 2010.

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0264 ± 0414 /98 Ó 1998 E & FN Spon

Journal of Sports Sciences, 1998, 16, 691± 699

Identifying and developing rugby talent among10-year-old boys: A practical model

ANITA E. PIENAAR,1* MANIE J. SPAMER3 and HENDRIK S. STEYN Jr2

1Department of Human M ovement Science and

2Statistical Consultation Service, Potchefstroom University for

Christian Higher Education, Potchefstroom 2520 and 3Department of Physical Education, Potchefstroom

College of Education, Potchefstroom 2520, South Afr ica

Accepted 22 August 1997

The re-entry of South Africa into the international sporting arena and the resultant need for the identiW cation

and development of talent, especially among formerly deprived groups of people, provided the incentive for this

study. Its aim was to identify the physical, motor and anthropometric variables that will enable coaches to

identify 10-year-old boys, based on their abilities, who could become successful rugby players. Altogether, 173

ten-year-old boys with no rugby experience from a cross-section of the population were selected at random

and subjected to 14 physical and motor tests and 14 anthropometric measurements. From 22 schools which

participated in the Western Transvaal primary schools under-11 rugby league, the three top teams (n = 45

individuals) were selected and also tested. The results from these three teams were used as the criteria for rugby

talent among 10-year-old boys. To establish the best predictors of talent, a stepwise discriminant analysis was

conducted: this indicated eight variables (four motor and four anthropometric) that discriminated maximally

between the talented and the rest of the players of this age. With classiW cation functions based on these eight

variables, 93.8% of all the subjects were classiW ed correctly, indicating good validity. A canonical analysis, based

on the selected variables, was then conducted on all the under-11 teams that played in the league in the region

(n = 330), and they were ranked according to the scores of the W rst canonical variable from the most to the least

talented. By comparing these results with the players who were chosen for the region’ s primary schools team, a

success rate of 88% in prediction of talent was established. We conclude that this is a successful and practical

method to aid the teacher and the coach in selecting and developing talent among 10-year-old rugby players in

South Africa.

Keywords: anthropometry, development, W tness, motor tests, talent identiW cation.

Introduction

Competitive sports participation has become an

established feature of the lives of children in the West,

resulting in an exercise explosion in the industrialized

countries (M alina and Bouchard, 1991). According to

MaV uli and Baxter-Jones (1995), 79% of all British

children aged 5± 15 years are likely to have participated

in youth sports; in the USA, it has been estimated that

50% of males and 25% of females aged 8± 16 years take

part in organized competitive sport (Arnot and Gaines,

1986). However, much controversy still exists regarding

competitive sports participation at a young age, includ-

ing the ethical and moral issues of early specialization in

one sport (Reilly and Stratton, 1995). The stigma of

* Author to whom all correspondence should be addressed.

talent identiW cation, as an undesirable practice, is fast

disappearing, especially when the research process and

the implementation of talent detection programmes are

focused on the well-being of athletes striving to realize

their full potential. Alabin et al. (1980) and Hahn

(1990) stated that talent identiW cation is important in

modern sport; they suggested that eY cient talent iden-

tiW cation procedures should play a major role in modern

sport, as international competition has become more

intense and involves ever younger participants. Arnot

and Gaines (1986) stated that sports talent should be

recognized and encouraged in children after the age of

10, since such talent is an important part of the child’ s

overall potential, and one that deserves recognition and

encouragement as much as any other. BloomW eld et al.

(1994) noted that such identiW cation programmes help

to direct children towards sports, or particular events,

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692 Pienaar et al.

for which they are physically and psychologically best

suited. This, in turn, means that they will probably

obtain good results and enjoy their training and par-

ticipation more. Another factor is injury, which is an

inherent risk in sports participation and, to a certain

extent, must be considered an inevitable cost of athletic

training and competition. Coaches and parents can,

however, minimize the risk of injury by ensuring proper

selection of sports events, using proper equipment,

enforcing rules, using safe playing conditions and pro-

viding adequate supervision (MaV uli and Baxter-Jones,

1995).

An intensive literature survey on talent identiW cation

by Du Randt (1992) indicated that the identiW cation of

talent is a well-established practice, especially in what

were the Eastern-bloc countries. Reilly and Stratton

(1995), however, noted that there are few, if any, models

of talent identiW cation and talent nurturing that are

at present globally acceptable. In South Africa, talent

identiW cation is, according to Du Randt (1992),

uncoordinated and under-researched, although there

is a deW nite need for it, especially for identiW cation in

accordance with scientiW c methods. Talent identiW -

cation in South Africa should not only concern so-called

`deprived groups’ , but also help already talented

children to realize their potential. Du Randt (1992)

suggested that the W rst stage of identiW cation should

take place at the age of 8 ± 10 years in the form of mass

screening (this age can vary depending on the kind of

sport involved), and this should be followed up 18± 24

months later. Final talent identiW cation should take

place at around 14 years of age.

In rugby, very little research has been done in the

W eld of talent identiW cation, particularly among young

children (International Rugby Information Centre,

1994). Williams (1979) and Rutherford (1983) long ago

expressed the need for more scientiW cally grounded

methods in rugby talent identiW cation.

In this study, an attempt was made to establish,

through scientiW c methods, a set of tests that could be

used for talent identiW cation and development among

10-year-old rugby players.

Methods

Several researchers (Woodman, 1985; Hahn, 1990;

Malina and Bouchard, 1991; BloomW eld et al., 1994)

have indicated that aspects of somatotype, body com-

position, proportionality, strength and power, Xexibility,

speed, posture, coordination, balance and agility should

be considered for inclusion in tests used for selection

purposes. Du Randt (1992) further suggested that

initial selection on the basis of physiological and

motor abilities should be performed in conjunction with

sport-speciW c skills. An analysis of the demands that

rugby makes on young players has revealed that the

basic skills and abilities a player needs are handling

(catching and passing), running, kicking, a good sprint

time and endurance (Guy et al., 1991; Hazeldine and

McNab, 1991). The only set of tests to measure most of

these components that we could W nd in the literature

was the football skills test (American Alliance for

Health, Physical Education and Recreation, 1996). This

consists of three handling skills (passing for distance,

passing for accuracy over a distance of 7 m and running

and catching a pass), two kicking skills (kicking for dis-

tance and kick-oV for distance) and two motor ability

tests (50-yard dash to measure sprint time and the ball-

changing zigzag run to measure agility). All these tests

were executed according to the instructions of the

original test. The execution of the throwing pattern in

the three handling skills had, however, to be adapted to

the game of rugby football because of the diV erence in

throwing technique. The throwing pattern was therefore

changed from an overhead pass to a two-handed lateral

Table 1 Pearson’ s correlation coeY cient (r) and 95% agreement limits for diV erent tests (n = 36)

Test Mean r

95% agreement

limits

Passing for distance

Passing for accuracy, 7 m (marks out of 30)

Passing for accuracy, 4 m (marks out of 10)

Running and catching (marks out of 20)

Test = 8.83

Retest = 9.44

Test = 4.08

Retest = 4.42

Test = 2.22

Retest = 3.05

Test = 1.82

Retest = 2.97

0.74

0.66

0.39

0.53

- 0.091 to 1.313

- 0.236 to 0.922

- 0.637 to 0.144

- 0.687 to 1.599

Note: 95% agreement limits = mean diV erence between test and retest ± the standard deviation of diV erences

between test and retest scores multiplied by 1.96. The score in the W rst test was subtracted from that obtained in

the retest.

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Identifying and developing rugby talent among boys 693

pass. Although the ball used in American football does

not diV er much from the ball used to play rugby in

South African primary schools, a standard `number 4’

ball was used in the execution of the tests. Execution of

the speed and agility tests remained unchanged. The

adapted tests were assessed for test± retest reliability

using Pearson’ s correlation coeY cient and 95% agree-

ment limits according to the method of Altman (1991),

as outlined by Atkinson (1995). The results are reported

in Table 1. The 7-m passing for accuracy test was the

only test included in the developed prediction function.

Our results indicated that the adjusted test was reliable.

As the situational analysis also revealed that such

physical abilities as strength, endurance and Xexibility

are important for success in rugby, tests of this nature

were included, namely pull-ups (dynamic strength), a

modiW ed sit-and-reach test (Xexibility), the vertical

jump (explosive strength) and the 500-m endurance

test (Johnson and Nelson, 1984). A speed endurance

test (the `fatigue index’ ; Hazeldine and McNab, 1991)

and a self-designed test for accuracy of passing over

a distance of 4 m were also included. Before imple-

menting the accuracy-of-passing test, its reliability

(test± retest correlation, r) and 95% agreement limits

were established in thirty-six 10-year-old boys with

(n = 18) and without (n = 18) rugby experience (Table

1). In this test, a player had to run with the ball in his

hands for 3 m up to a mark in line with the target (a

round circle with a diameter of 50 cm, mounted on a

stand 50 cm high). This target was 4 m away, and the

player had to throw the ball through the circle with a

lateral pass. The test had to be performed W ve times

from the left side and W ve times from the right side. For

each correct throw (through the circle) a score of 1 was

obtained (maximum = 10 points).

According to the protocol recommended by the Inter-

national Society for the Advancement of Kinanthro-

pometry (Eston and Reilly, 1995), 14 anthropometric

measurements were made on each subject: height, body

mass, two skeletal diameters (humerus and femur),

two muscle circumferences (tensed upper arm and calf

girths) and eight skinfolds (triceps, subscapular,

supraspinale, midaxillary, pectoralis, abdominal, front

thigh and calf ). Somatotype, percentage body fat

(Boileau et al., 1985), correction for the arm (tensed

upper arm girth corrected for fat by subtracting triceps

skinfold) and correction for the calf (calf girth corrected

for fat by subtracting calf skinfold), as well as the ratio of

length to the cube root of weight were also calculated.

Discriminant analysis can be used to assign indi-

viduals to groups (Thomas and Nelson, 1985) and was

therefore chosen as the statistical procedure for this

study. Salmela and RŠgnier (1983) suggested that a

control group and a target (talented) group should

be selected when a discriminant analysis is being

conducted for talent identiW cation purposes. From

seven local schools, 173 ten-year-old boys who did not

play rugby or were not considered to have the talent

to play rugby were selected at random to form the

control group. Their mean age was 10.11 years. They

were subjected to a set of tests, which consisted of

14 motor and physical variables and 14 anthropometric

variables (described above). The target group was

selected from 22 primary schools (n = 330 individuals)

that participated in the Western Transvaal region

under-11 rugby league. The three top teams (n = 45

individuals) were selected for the administration of

the tests, and the results were used as the criteria for

rugby talent among 10-year-old boys (mean age = 10.85

years).

The BMDP-1D (descriptive statistics), BMDP-3D

(t-test for independent samples) and BM D-7M (step-

wise discriminant analysis) statistical computer pro-

grams were used to analyse the data (Dixon, 1990). A

probability value of 0.01 was used to test for signiW cant

diV erences between groups. Practical signiW cance was

also tested using omega-square (v2) as the criterion

(Thomas and Nelson, 1985). Omega-square is reported

as percentages. Percentages of 14% and higher were

used to indicate practical signiW cance (Cohen, 1977).

Using a forward stepwise discriminant analysis

(Thomas and Nelson, 1985), we extracted a subset of

variables that discriminate maximally between the two

groups. An F-value of 4 was used as the cut-oV point

to stop the extraction procedure of the discriminant

analysis. ClassiW cation functions were then established,

which enabled us to classify a child as a `potential-

player’ or a `non-potential-player’ . The discriminatory

power of the classiW cations was established by using

the jack-knifed classiW cation matrix method (Dixon,

1990).

The children classiW ed as potential players were then

ranked from the least to the most talented by using the

values of the W rst canonical variable (Marriott, 1974).

This canonical analysis was performed based on the

selected variables from the discriminant analysis using

the SAS procedure (PRINCOMP), from which the W rst

canonical variable was calculated. The larger this value,

the higher the player was ranked. To test the practical

value of this established ranking, the coaches of the

three teams tested were asked to rank each player in

their team from 1 to 15, according to what each coach

thought their talent levels to be. This ranking included

such aspects as courage and good decision-making

abilities, which were not measured by the set of tests.

The association between each coach’ s ranking and that

of the top 15 players ranked by the canonical analysis

was established using rank correlation. As subjectivity

could play a role in the ranking by the coaches of the

three teams, regional selectors were also asked to select

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694 Pienaar et al.

a hypothetical 15-man A-team (1) and a B-team (2)

from all the players in their region according to their

rugby-playing abilities (six teams per region). It could

be that the coach of one of the three teams tested ranked

a speciW c player as second best in his team, but the six

regional coaches did not rank that particular player as

good enough for the A- or B-team. This helped to

further establish the practical value of the developed

classiW cation and ranking procedures.

After completion of the above phase, all rugby players

in the Western Transvaal region playing in the under-11

league (n = 330) were subjected to the selected sub-

set of tests and placed into a rank order according to

their W rst canonical variables. The top-ranked 40 players

were then engaged in a scientiW cally developed rugby

development programme. It is acknowledged that indi-

cators such as biological maturity, sports participation

and physical activity could confound the results;

these variables must be carefully considered when

interpreting the results, as they were not addressed. Two

years later, when the players reached the age of 12,

they competed in the under-13 league, from which the

regional primary schools A- and B-teams were selected.

These selected team s then competed against teams

from across the country at a `National Craven Rugby

Week’ . Our intention was to determine the number

of `scientiW cally top-ranked 40 players’ who were

eventually selected for these teams, and to establish,

by calculating the percentage players included in

these teams, the predictive validity of the developed

equation.

Results

When the two groups were compared according to their

rugby skills, it appeared that the experienced group was

signiW cantly better (P < 0.01) in all the skills tested

(Table 2). The same results were found for the physical

and motor abilities tested (Table 3). The only variable

which did not indicate a statistically signiW cant dif-

ference between the groups was the sit-and-reach test.

Table 2 Descriptive statistics and comparison of mean scores for rugby skills between experienced and non-experienced

groups (mean ± s)

Non-experienced Experienced

Statistical

signiW cance Practical

signiW cance,

Rugby skills (n = 173) (n = 45) t P v2 (%)

Passing for distance (m)

Passing for accuracy, 7 m (score)

Passing for accuracy, 4 m (score)

Running and catching (n)

Kick for distance (m)

Kick-oV for distance (m)

9.16 ± 1.93

4.09 ± 4.79

3.74 ± 2.09

9.54 ± 5.31

16.1 ± 5.42

14.1 ± 4.80

12.0 ± 2.16

15.8 ± 7.04

4.86 ± 2.06

14.9 ± 3.98

23.0 ± 4.14

18.8 ± 4.93

- 10.28

- 15.01

- 5.17

- 8.20

- 9.59

- 6.02

0.0000*

0.0000*

0.0000*

0.0000*

0.0000*

0.0000*

32.4

50.7

10.6

23.3

29.4

13.9

* P < 0.01 (one-sided).

Table 3 Descriptive statistics and comparison of mean scores for motor and physical abilities between experienced and

non-experienced groups (mean ± s)

Non-experienced Experienced

Statistical

signiW cance Practical

signiW cance,

Motor/physical abilities (n = 173) (n = 45) t P v2 (%)

Sprint time (s)

Agility run (s)

500-m endurance (s)

Sit-and-reach (cm)

Flexed armhang (s)

Pull-ups (n)

Vertical jump (cm)

Speed endurance (% decrease)

8.40 ± 0.79

9.79 ± 1.04

136.4 ± 37.0

1.82 ± 6.84

13.6 ± 8.73

2.76 ± 2.78

25.4 ± 6.08

6.70 ± 3.40

7.45 ± 0.71

8.9 ± 0.7

110.6 ± 12.7

2.71 ± 3.69

35.6 ± 17.6

5.06 ± 4.40

30.4 ± 5.99

5.38 ± 2.50

7.05

5.96

5.52

- 1.40

- 13.14

- 4.02

- 3.99

3.04

0.0000*

0.0000*

0.0000*

0.1642

0.0000*

0.0001*

0.0001*

0.0026*

18.3

13.6

11.9

0.4

44.1

6.5

6.4

3.6

* P < 0.01 (one-sided).

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Identifying and developing rugby talent among boys 695

Table 4 Descriptive statistics and comparison of mean scores for anthropometric variables between experienced and

non-experienced groups (mean ± s)

Non-experienced Experienced

Statistical

signiW cance Practical

signiW cance,

Body composition (n = 173) (n = 45) t P v2 (%)

Body mass (kg)

Stature (cm)

Triceps skinfold (mm)

Subscapular skinfold (mm)

Midaxillary skinfold (mm)

Supraspinale skinfold (mm)

Pectoralis skinfold (mm)

Abdominal skinfold (mm)

Front thigh skinfold (mm)

Calf skinfold (mm)

Percent fat (%)

Flexed arm girth (cm)

Calf girth (cm)

Humerus diameter (cm)

Femur diameter (cm)

Endomorphy

Mesomorphy

Ectomorphy

Upper arm correction

Calf correction

Stature/3

Ö mass

31.9 ± 6.66

137.8 ± 7.35

9.74 ± 4.70

6.93 ± 4.71

6.29 ± 4.24

6.29 ± 4.24

5.84 ± 3.89

9.00 ± 7.21

15.5 ± 8.24

9.81 ± 4.12

14.9 ± 5.35

20.7 ± 2.23

26.5 ± 2.51

5.67 ± 0.48

8.13 ± 0.62

2.8 ± 1.3

4.0 ± 0.9

3.4 ± 1.3

19.7 ± 1.89

25.5 ± 2.32

43.7 ± 1.72

36.4 ± 5.56

146.6 ± 5.82

11.4 ± 5.12

7.25 ± 4.16

6.81 ± 4.75

6.36 ± 4.26

6.22 ± 3.86

8.66 ± 6.53

15.6 ± 5.97

11.4 ± 5.44

15.3 ± 6.30

23.5 ± 2.42

28.9 ± 2.38

5.91 ± 0.50

8.95 ± 0.50

2.6 ± 1.3

4.5 ± 1.0

3.6 ± 1.4

22.4 ± 2.52

27.2 ± 2.48

44.0 ± 1.96

- 4.75

- 7.39

- 1.19

- 0.77

- 0.23

- 0.27

- 0.46

0.19

- 0.86

- 0.89

- 0.56

- 0.08

- 4.90

- 3.90

- 9.01

0.67

- 3.74

- 1.21

- 9.00

- 5.12

- 1.21

0.0000*

0.0000*

0.2350

0.4398

0.8221

0.7871

0.6425

0.8511

0.3890

0.3741

0.5794

0.0000*

0.0000*

0.0001*

0.0001*

0.5061

0.0000*

0.2276

0.0000*

0.0000*

0.2272

9.02

19.70

0.19

0.18

0.02

0.42

0.36

0.44

0.11

0.09

0.32

0.75

0.50

6.15

26.89

0.25

5.62

0.21

26.84

10.37

0.21

Note: Somatotype of non-experienced group = 2.8± 4.0 ± 3.4; somatotype of experienced group = 2.6± 4.5 ± 3.6. * P < 0.01.

Practical signiW cance was established for passing for dis-

tance, passing for accuracy (7 m), running and catching,

kick for distance, sprint time and Xexed armhang.

Although the experienced group was signiW cantly taller

and heavier than the non-experienced group (Table 4),

practical signiW cance could only be established for

stature. None of the eight skinfolds or the calculated

body fat percentage (experienced group, 15.3%; non-

experience group, 14.9%) diV ered signiW cantly, either

statistically or practically. Statistically signiW cant

diV erences found among the upper arm and calf girths,

humerus and femur diameters and the mesomorphic

components of the somatotypes (experienced group,

2.6 ± 4.5 ± 3.6; non-experienced group, 2.8 ± 4.0 ± 3.4) of

the two groups, indicated that the body composition

of the experienced group consisted of more muscle

mass in relation to subcutaneous mass when compared

to the body composition of the non-experienced group.

Of all the above-mentioned variables, only femur

diameter and upper arm correction were practically

signiW cant.

To establish the best predictors of talent, a stepwise

discriminant analysis was conducted on the data to

W nd the subset of best `discriminators’ among all the

potential classiW ers. This multivariate analysis high-

lighted the classiW ers (or variables) that distinguish

potential 10-year-old rugby players from their peers.

These include sprint time, passing accuracy (suggesting

hand-to-eye coordination), static and dynamic strength.

Omega-square values for the stature-to-body mass ratio

(0.21%) and the vertical jump test (6.4%), however,

showed that these two tests did not have practical

signiW cance, indicating a non-signiW cant contribution

of these two variables to the total variance responsible

for the grouping. All other variables, apar t from calf

correction (10.37%), included in the classiW cation

functions, indicated practical signiW cance (Tables 2 ± 4).

Nevertheless, early-maturing 10-year-olds will still have

an advantage over their late-maturing peers, in that they

are taller, heavier and more powerful.

Functions for eight variables were established, en-

abling the researchers to classify each child into one of

two categories, namely potential and non-potential

players. This was done by calculating both functions

for each subject in the sample. The function with the

highest value then indicated in which group each

particular subject should be classiW ed. The functions

used to classify the subjects are as follows:

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696 Pienaar et al.

· Potential player = - 1093 - 0.5402(passing accuracy, 7 m) +22.77(sprint time) + 0.2550(Xexed armhang) - 0.1828

(vertical jump) + 23.82(femur width) + 17.05(arm correc-

tion) + 0.9681(calf correction) + 31.69(stature : body mass

ratio)

· Non-potential player = - 1042 - 0.7907(passing accuracy,

7 m) + 24.01(sprint time) + 0.0961(Xexed armhang) -0.0840(vertical jump) + 21.53(femur width) + 15.31(arm

correction) + 1.378(calf correction) + 31.18(stature : body

mass ratio)

Note that these functions are only used for classiW cation

and are not to be interpreted for the purpose of prediction.

The above reported sequence of the variables in the

established functions is the same as that resulting from

the stepwise discriminant analysis (passing accuracy

entered W rst, stature : body mass ratio last).

The discriminatory power of the developed classiW -

cation functions was established using the jack-knifed

classiW cation matrix. Table 5 indicates the number of

players classiW ed correctly using this procedure. Accord-

ing to the results, 93.8% of the subjects were classiW ed

correctly with the eight selected variables, indicating

good validity of the developed equation.

Although the ratings of the coaches and selectors can

be inXuenced by subjectivity, it is still a form of external

criterion for determining predictive validity. These

ratings were, however, used mainly to determine the

practical value of the developed prediction functions.

To determine this practical value, the W rst canonical

variable was calculated for each member of the top three

teams, after which the W rst 15 ranked players were

selected and their results compared to the rankings of

their coaches and those of the regional selectors.

A Spearman rank correlation of - 0.35 was found

between the values of the W rst canonical variable and the

ranking of the players by their coaches. However, these

results (Table 6) indicated that 11 of the top 15 players

were ranked among the top 5 by their coaches. Also, 14

of the 15 players had been selected by the regional

selectors for inclusion in a hypothetical A-team or B-

team which they selected from all the teams in their

region. These results indicated that the children who

Table 5 Percent subjects classiW ed correctly into each group

(jack-knifed classiW cation matrix)

ClassiW cation

Actual group

%

Correct

Non-potential

player

Potential

player

Non-potential player

Potential player

Total

96.5

91.1

93.8

167

6

173

4

41

45

had been selected as the best potential players with the

discriminant analysis were, according to their coaches

and the selectors, also the best players.

When the playing positions of these top 15 players

were analysed, all nine playing positions (combined)

were included (Table 6). It thus seems clear that, with

the aid of the discriminant analysis, it is also possible to

select players who will be representative of all playing

positions in a team. The low rank correlation, which was

established between the ranking of the coach and the

canonical value, probably results from the fact that each

coach ranked his own 15 players. A better correlation

would probably be obtained if all 45 players were ranked

together and not separately as teams.

Of the other four rugby players who were also ranked

in the top 15 with the discriminant analysis, and not

ranked in the top 5 by their coaches, only one was not

ranked by the selectors. Regarding these players, it can

be concluded that they do have the `genetic abilities’ to

be good rugby players, but might at this young age still

lack other important characteristics, like good decision-

making abilities. If the coach took more trouble to

develop these potentially talented children by concen-

trating on their weak spots, these players would become

more valuable assets to the teams for which they play.

The discriminant analysis can thus assist the coach in

identifying those children who may, for instance, be late

developers in terms of their decision-making abilities.

Our next step was to rank all 330 players in the under-

11 league. This was done according to the values of the

W rst canonical variable, based upon the eight selected

variables. Western Tansvaal A and B regional primary

schools rugby teams are selected annually on the basis

Table 6 Results of the top-ranked rugby players (n = 15)

Value of

W rst canonical

variable

Playing

position

Ranking by

coaches

Choice

of team

selectorsa

4.56

4.09

4.08

3.68

3.65

3.62

3.52

3.52

3.38

3.34

3.20

3.07

3.05

3.00

2.52

4

8

9

13

6

1

4

10

14

2

3

3

14

15

13

3

4

1

1

10

4

15

3

12

5

9

4

5

3

5

2

1

1

2

2

2

Ð

1

1

1

2

2

2

2

1

a 1 = A-team, 2 = B-team.

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Identifying and developing rugby talent among boys 697

of a number of trials. These two teams are selected

from all the under-13 players in the region, which at this

time were all the players who took part in this study.

The 40 scientiW cally top-ranked under-11 players

were compared to the players chosen for these two

teams 2 years later, and the following results were found.

Fifteen of the 40 top-ranked under-11 players, who

were initially selected according to the eight variables

included in the canonical analysis, were chosen for the

A-team (8 players) and B-team (7 players), respectively.

As far as back players of the two teams are concerned,

a 100% prediction rate was obtained. Ten of the 14

players selected for the two teams were in the top 40

ranked players. The remaining four back players in the

teams were not part of the research group because,

at the time when all teams were tested, the part of the

region where they live fell outside the boundaries of

the Western Transvaal. Five of seven forward players

who were tested and were in the top 40 were included in

the two teams. The two other players who were selected

for the A- or B-team were ranked 99th and 159th

respectively. An average success rate of 88% for pre-

diction of talent for forward and back players was there-

fore obtained.

The above results, together with the high percentage

of players included in the regional teams at the end of

the players’ primary school career, proved that the

developed classiW cation functions have practical value.

Discussion

Up-to-date research on talent identiW cation has been

published for the following sports: wrestling, weight-

lifting, diving, tennis, track-and-W eld, hockey, baseball,

swimming, fencing, gymnastics, rowing, kayaking,

cycling and sprinting (Woodman, 1985; St-Aubin

and Sidney, 1996). From this list it is clear that talent

identiW cation research on team sports is limited. Reilly

and Stratton (1995) indicated that such sports as W eld

hockey, volleyball, judo and the martial arts are less

specialized sports, and arguably do not require a high

degree of specialist conditioning from an early age. This

may be one reason for the lack of research on team

sports in the area of talent identiW cation. Salmela and

RŠgnier (1983) noted that the isolation of performance

criteria is more crucial for team sports, in that there is an

even greater variety of tasks; the problem becomes even

more complex in team sports where mini-performances

must be conceived of within a team context, considered

against the strengths and weaknesses of other team-

mates and the given demands of each position. In this

case, the elaboration of a detection tool would be much

more complex, since there is no longer a single perform-

ance criterion to predict, but many criteria based on

identiW ed sub-objectives. Whatever the reason for this

lack of research on team sports, it was not possible to

gain knowledge from research conducted in this area,

or to compare results. Research on individual sports

has used the same research method as in the current

study. Klika and Thorland (1994) used discriminant

analysis to identify variables that can contribute to the

classiW cation of faster and slower male swimmers at

the ages of 12 and 16 years, respectively. Their results

indicate that diV erent variables account for perform-

ance as a function of age, and therefore diV erent models

should be used to classify swimmers.

The research W ndings of St-Aubin and Sidney (1996)

on the methods to be used in the development of talent

identiW cation models, indicate the conceptual research

model (sliding populations approach) of RŠgnier (1987)

to be the most complete and reliable model to date.

This model has currently been used only for the sports

of gymnastics, baseball and fencing. The model and

suggestions of RŠgnier (Salmela and RŠgnier, 1983),

together with guidelines from the studies of Du Randt

(1992) and Woodman (1985), were used in the research

design for this study. The main purpose of our research,

therefore, was not to develop a model that could pre-

dict long-term rugby playing potential (talent identiW ca-

tion), but to develop a strategy with which individuals in

a large population who possess the identiW ed attributes

for success in rugby can be identiW ed and developed

accordingly in the short term (talent selection). Follow-

up models should therefore be developed. This was the

reason why the validity of our model was tested over a

period of 2 years, as the results were to give an indication

of the age at which a new model should be developed.

According to Salmela and RŠgnier (1983), variability

in fundamental attributes and capacities underlying

sports performance can be controlled up to a certain

point by setting shorter gaps between control and target

populations. Although the prediction value of the

developed talent-selection model was tested and found

to be reliable over a period of 2 years, this procedure will

in the main be reliable for 10-year-olds because of the

inXuence that growth and maturation have on children

of this age. The two players who were not in the top

40 ranked players support the above conclusion. It is,

therefore, recommended that another selection tool be

developed for use with children 12 years and older. The

sliding populations approach to research design of

RŠgnier proved to be a reliable means of conducting

research of this nature. Follow-up studies should there-

fore use the same method, but with another set of initial

performance criteria, and target and control populations.

One deW nite limitation of our study, which was high-

lighted by the results, was a lack of measures of maturity

status. Reilly and Stratton (1995) indicated that early-

maturing males are at an advantage in many sports

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698 Pienaar et al.

because of their signiW cant increase in muscle mass

during peak growth. Malina and Bouchard (1991)

reported that baseball players, footballers, swimmers

and track athletes tend to be, on average, advanced in

their skeletal and sexual maturation, as these sports

rely to a large extent on strength and power. These

authors reported W ndings conducted on 58 American

football players between 10 and 14 years of age, who

were on average only slightly taller than US reference

data for stature, but consistently above average in

terms of body mass. The results of our study indicate

signiW cant statistical diV erences in both stature and

body mass between experienced and non-experienced

groups (Table 4), although only stature indicated

practical signiW cance between the groups. Although

neither of these variables was entered into the multi-

variate analysis, the stature-to-body mass ratio was

entered, but failed to show practical signiW cance

(0.21%) in the developed multivariate analysis. This

result indicates that the contribution of this ratio to the

total variance responsible for the grouping of talented

and less-talented players was not signiW cant.

With older boys entering their adolescent growth

spurt phase (12± 14 years), status of maturity should be

assessed to ensure that late-maturing children with

talent are not overlooked in the talent identiW cation

process. In the development process at this young age,

a principle of over-inclusion of players to be developed

should always be implemented, which means that as

many players as possible should be directed to suitable

development programmes based on their abilities.

Literature W ndings also indicate that such criteria as

previous training background, physical activity level,

response to training and psychological factors should

be investigated by talent identiW cation models (Hahn

and Gross, 1990; Rowley, 1992). As the aim of this

research was to develop a talent identiW cation model

with which `raw’ rugby-playing talent could be identi-

W ed, these factors were not tested for, although they

might have provided better results. The model developed

here will therefore be diV erent from those that need to

be developed when players have gained more experience

in a particular sport and because of the changing

demands within the sport. Detection tools for speciW c

playing positions will, for instance, become more

important as players age, gain more experience and are

chosen for teams according to the physical and motor

demands of diV erent playing positions.

Conclusion

The research reported here should be viewed as pio-

neering work in the W eld of talent identiW cation of

young rugby players and may therefore have its limita-

tions. It is unrealistic to expect that a highly eV ective

talent identiW cation process will be developed by con-

ducting the research process only once, and therefore

reW nement of the model is required. We intend reW ning

the results of this study and incorporating them into a

research design on a newly selected sample of 12-year-

old players. The 40 top-ranked players in the present

study, together with selected players from the `Craven

Week’ primary schools team, will also be followed and

tested over a period of 5 years, at which point they will

reach the end of their high-school careers. A high-school

`Craven Week Rugby Team’ is also selected annually

from all under-19 players in the region. Using this

longitudinal approach, we hope to determine whether

these players will still be the most successful players in

their late adolescence.

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