Biomechanics and Medicine in Swimming XI

BiomechanicsandmedicineinswimmingXi
Talent Prognosis in Young Swimmers
hohmann, A. 1 , seidel, I. 2
1 Institute of Sport Science, University of Bayreuth, Bayreuth, Germany
2 Karlsruhe Institute of Technology, Karlsruhe, Germany
In talent selection a high level of competitive performance, and also of
performance prerequisites are of interest. Furthermore, the trainability
and utilization of the performance prerequisites, and also psychological
factors have to be included. As the different components of the early
talent make-up not only change over time, but also mutually suppress
or enhance each other, talent development is a complex non-linear process.
Linear models like discriminate analysis can only predict the talent
development within a small range of the future performance outcome.
Therefore, non-linear neural network methods turned out to be appropriate
tools for talent detection purposes. By recognizing distinct patterns
in the individual dispositions, the Self-organizing Kohonen Feature
Map allowed for better predictions of the future success of talented
swimmers.
Key words: talent prognosis, swimming, discriminate analyses, neural
network
IntroductIon
Recent theoretical contributions to the theory of talent in sport have
clearly shown that a complex and longitudinal framework is necessary to
successfully address talent identification and the talent promotion issue
in most sports (Gagnè, 1985; Abbot & Collins, 2002; 2004). Consequently,
the early diagnosis of juvenile competition performances and
performance prerequisites has to be complimented by a final follow-up
search for the individual best performance of each adult athlete at the
end of his/her career (Willimczik, 1982; Schneider, et al., 1993).
As talent development is a complex non-linear process, and the different
components of early talent make-up not only change over time,
but can also mutually suppress or enhance each other, linear models like
discriminate analysis can only approximate the non-linear talent development
within a very small range of the future performance output.
Because of this, neural networks also seem to be appropriate tools for
talent detection purposes (Philippaerts, et al., 2008). Due to their pattern
detection ability, such methods as e.g. the Self-organizing Kohonen
Feature Map may allow to predict the future success of talents by revealing
distinct patterns in the individual sets of sport specific dispositions.
The purpose of this paper is to compare the quality of linear and
non-linear talent predictions, which are both based on prognostic valid
talent criteria in swimming. Specifically, it shall be demonstrated that
the talent development outcome can be better modeled by means of the
non-linear Self-organizing Kohonen Feature Map (SOFM) network.
Methods
The Magdeburg Talent study on Elite Sport Schools (MATASS;
Hohmann, 2009) is a six year longitudinal study on the development
of talented children and adolescents. It was conducted at the two Elite
Sport Schools in Magdeburg, Germany, and was based on a sequential
sliding populations design (Regnier, et al., 1993) with three test waves in
the years 1997, 1999 and 2001. In each test wave all pupils of the swimming
classes 5 to 12 were included. They were added by pre-selected
young athletes from class 4 of the elementary schools and the graduates
of the last year before the tests (see Fig.1).
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Cohort 3
Cohort 4
Cohort 5
Cohort 6
Cohort 7
Cohort 2
Cohort 1
1997 1999 2001
Wave 1 Wave 2 Wave 3
Classes 4 & 5
Classes 6 & 7
Classes 8 & 9
Classes 10 &11
Classes 12 & 13
Figure 1. The longitudinal study design of the MATASS
The data for these analyses of swimming were collected from 1997 to
2001 from a total of N = 290 male (n = 172, Min = 128 months, Max
= 276 months, M = 171.24 months, SD = 42.52) and female swimmers
(n = 118, Min = 116 months, Max = 282 months, M = 159.25 months,
SD = 39.02). The final competition performance data was recorded in
the year 2006 for all male swimmers (n = 130, M = 254.51 months,
SD = 37.99) and female swimmers (n = 113, M = 236.47 months, SD
= 36.29) that were at least 16 years old. Thus, the diagnosis of the adult
competition results took place about seven years after their personal best
test results (males: M = 81.67 months, SD = 19.68; females: M = 76.98
months, SD = 17.95).
Twenty one elementary physical and technical performance components
of the swimming performance were measured. Elementary speed:
(1) Reactive fall into the wall (wall contact time), (2) Reactive drop jump
(ground contact time), (3) Foot tapping speed, (4) Accoustic reaction
time, (5) Arm cranking speed. Complex speed: (6) Isokinetic arm pull
(speed level 1), (7) Isokinetic arm pull (speed level 9), (8) Maximum
rate of force development (isokinetic arm pull, level 1), (9) Standing
high jump, (10) Standing long jump, (11) 7.5-m-start (into the water),
(12) 5-m-flying sprint (in the water). Technique and coordination:
(13) Crawl swimming technique, (14) Complex coordination (swimming
with obstacles in the water), (15) Maximum pulling frequency
(in the water). Strength: (16) Isometric maximum strength of the arms
(bench press), (17) Maximum rate of force development of the arms
(bench press). Anthropometric variables (body structure): (18) Arm
span width, (19) Hand size, (20) Shoulder flexibility, (21) Broca index.
These variables were complemented by four psychological (achievement
motivation, volition, stress stability, concentration) and four sociological
(school support, family support, training environment, training load)
performance components, which were collected via questionnaire. The
best competition performance of each athlete in each of the three survey
periods (1997, 1999, 2001) was also assessed. Finally, at the end of the
study in the year 2006, the adult (peak) performance up to then of all
athletes aged 16 years and older was recorded.
In a first step, the 21 physical and technical performance components
were analysed by factor analysis (SPSS 14.0, SPSS Inc., Chicago,
Ill.). Using this procedure, six complex and one-dimensional performance
prerequisites were extracted by orthogonal factor analysis. In
swimming, these factors were (1) body structure, (2) maximum strength,
(3) general and (4) swim specific speed strength, (5) technique and coordination,
and (6) elementary speed (Hohmann, 1999).
In a second step, the (1) juvenile competition performance and (2)
the factor values of the six complex performance prerequisites served
as well as talent predictors as the (3) speed of performance development,
(4) speed of development of the performance prerequisites, (5)
utilization (Kupper, 1980; Hohmann & Seidel, 2003), and (6) psychological
stress stability. This set of six complex predictors was included
in the talent prediction model that was used to determine the final tal-