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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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