Biomechanics and Medicine in Swimming XI

ent outcome at the adult age of the participants. To define a criterion
variable, all athletes were categorized into three different talent groups
according to their personal best adult competition performance up to
the year 2006. So, the best adult swimmers taking part in international
championships were assigned to the category of extremely high talented
athletes, the national calibre athletes were assigned to the category of
highly talented swimmers, and the regional starters were labelled as normal
talents.
In a third step, the six juvenile talent criteria described above (of the
earlier time period 1997-2001) were used to predict the three final adult
talent groups (of the year 2006). For the prediction of the final adult talent
group two methods were used: firstly, a discriminate analysis (DA),
and secondly, the neural network of a Self-organizing Kohonen Feature
Map (SOFM; DataEngine, MIT Inc., Aachen, Germany).
To obtain a “true” prognosis, the talent forecasts on the basis of the
discriminate analyses and on the basis of the neural network method
have to follow a cross-validation procedure. In the case of the discriminate
analyses, 50 percent of the total number of cases was used to compute
the discriminate functions that were then used to determine the
talent outcome of the remaining 50 percent of cases.
In the two specific SOFM models for the male and the female
swimmers, the talent forecasts were validated by the “leave-one-out”procedure.
Therefore, one less than the total number of the data sets
of the talents (n-1) were used to train the network (consisting of a 5x5
neuron layer) with 5 000 training steps. The remaining data set of the
one single athlete was then presented to the neural network to calculate
a prognosis of his personal adult peak performance category. After
that, the predicted future talent category was compared with the real,
already known adult performance category. This procedure was repeated
for each single data set, so that the total number of correctly predicted
cases represents the quality of the talent prediction models for the male
and the female swimmers.
results
For talent identification purposes it is essential to know the early performance
prerequisites that form the structure of the current, and also the
future adult peak performance (e.g. for the female swimmers see Fig. 2).
Juvenile performance prerequisites in 16-20 years old female swimmers (1997-2001)
R = .74; R2 = .55; R2adj = .41; F (22;72) = 4.02; p = 0.001
Psychological V.
Technique &
Coordination
Condition
Anthropometric
variables
Extrinsic
motivation
Action orientation
(after failure)
Psychological
stress stability
Arm movement
frequency
Sprinting power
Stroking power
Shoulder
flexibility
Hand size
.77 .001
.42
Sprinting speed
(71 %)
.071 .74 .001
.014
.40
.37 .099
.77 .021
.61 .049
.51 .045
.33 .055
Swimming
coordination
(64 %)
.79 .071
.60 .039
.44
Adult
Competition
Performance
(2006)
50-m-Crawl
(41 %)
.005
.43 .089
Figure 2. Path analysis on the prognostic relevance of different performance
prerequisites of young female swimmers for the future competition
performance in 50 m sprint swimming (Hohmann, 2009)
The comparison of the real adult performance groups with the predicted
future groups of the talented swimmers at adult age led to far better
predictions, when the neural network method was applied. The percentages
of correctly predicted cases by discriminate analysis (females: 69.0
percent; males: 50.0 percent) are much lower than those delivered by
SOFM (females: 87.9 percent; males: 68.3 percent; Fig. 3).
Extreme
Talents
100.0 %
High
Talents
77.3 %
Normal
Talents
88.9 %
chaPter4.trainingandPerformance
Extreme
Talents
100.0 %
High
Talents
74.2 %
Normal
Talents
33.3 %
Figure 3. Results of the talent prognosis in female (left) and male (right)
swimmers on the basis of the nonlinear model of the Self-organizing
Kohonen Feature Map (single and double arrows symbolize light resp.
severe classification errors). Extreme talents: Participants in Olympic
Games, World Championships, European Championships, and German
Championship finals. High talents: European Youth Championships.
Normal talents: German Youth Championships
dIscussIon
This paper aimed at finding out whether the talent development outcome
can be better modeled by means of the nonlinear mathematical
method of artificial neural networks or by linear methods such as discriminate
analyses. The percentages of correctly modeled performances
in the linear discriminate analysis were comparably lower than in the
non-linear neural network procedure. Thus, the results support the assumption
of Philippaerts et al. (2008) that neural networks are excellent
tools to model and to predict future competitive performance categories
on the basis of juvenile talent make-up data.
Since there is no guarantee that such modeling and talent prediction
will lead to similar results in other groups, the validation procedure has
to be applied to data sets of other swimmers. Based on the results of
this procedure it must be decided whether the neural network is a good
or poor model of talent development in swimming. To obtain a good
model, it may require changes in some of the training parameters.
conclusIon
The better results of the neural network analysis compared with the
poorer results of the discriminate analysis support the interpretation
that the adaptive behaviour of the athlete is a non-linear complex problem.
This supports a dynamic systems approach to talent development in
which the young athlete unfolds a performance development process in
a self-organized way, which is influenced by various personal and contextual
moderator variables (Gagnè, 1985; Heller & Hany, 1986; Heller,
et al., 2005; Cote, et al., 2003).
As neural networks are able to recognize global patterns of different
talent make-ups, they are a worthwhile tool in the detection of talents
under the condition of the non-linear talent development. Hence, from
a dynamical systems point of view, a successful neural network modeling
may be interpreted as a representation of deviations of the different
states of the system from equi-probability, in our case the identification
of different patterns of juvenile athletic performance. This is a very interesting
aspect of the modeling of competitive performances, because
the non-linear dynamic systems perspective is rapidly emerging as one
of the dominant meta-theories in the natural sciences, and there is also
reason to believe that in the future it will eventually provide a more
general integrative understanding in training science, as well.
reFerences
Abbot, A. & Collins, D (2002). A theoretical and empirical analysis of
a `State of the Art` talent identification model. High Ability Studies,
13(2), 157-78.
Abbot, A. & Collins, D (2004) Eliminating the dichotomy between
theory and practice in talent identification and development: considering
the role of psychology. Journal of Sport Sciences, 22(5), 395-408.
Cotè, J., Baker, J. & Abernethy, B. (2003). From Play to Practice. A
Developmental Framework for the Acquisition of Expertise in Team
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