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THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE
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António
José Silva1,2 ,
Aldo Manuel Costa1, Paulo Moura Oliveira2,3, Victor Machado Reis1,
José Saavedra4, Jurgen Perl5, Abel Rouboa2,3 and Daniel Almeida Marinho1 |
1Sports Science Department of University of Trás-os-Montes and
Alto Douro, Vila Real, Portugal, 2CETAV, Research Centre, Vila
Real, Portugal, 3Engineering Department of University of Trás-os-Montes
and Alto Douro, Vila Real, Portugal, 4Sports Science Department
of University of Extremadura, Spain, 5Institute of Computer Science,
University of Maiz, Germany.
| Received |
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20 September 2006 |
| Accepted |
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24
January 2007 |
| Published |
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01
March 2007 |
©
Journal of Sports Science and Medicine (2007) 6, 117 - 125
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| ABSTRACT |
| The aims of the present study were: to identify the factors which
are able to explain the performance in the 200 meters individual medley
and 400 meters front crawl events in young swimmers, to model the
performance in those events using non-linear mathematic methods through
artificial neural networks (multi-layer perceptrons) and to assess
the neural network models precision to predict the performance. A
sample of 138 young swimmers (65 males and 73 females) of national
level was submitted to a test battery comprising four different domains:
kinanthropometric evaluation, dry land functional evaluation (strength
and flexibility), swimming functional evaluation (hydrodynamics, hydrostatic
and bioenergetics characteristics) and swimming technique evaluation.
To establish a profile of the young swimmer non-linear combinations
between preponderant variables for each gender and swim performance
in the 200 meters medley and 400 meters font crawl events were developed.
For this purpose a feed forward neural network was used (Multilayer
Perceptron) with three neurons in a single hidden layer. The prognosis
precision of the model (error lower than 0.8% between true and estimated
performances) is supported by recent evidence. Therefore, we consider
that the neural network tool can be a good approach in the resolution
of complex problems such as performance modeling and the talent identification
in swimming and, possibly, in a wide variety of sports.
KEY
WORDS: Evaluation, age group swimmers, individual medley, front
crawl.
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