Journal of Sports Science and Medicine
Journal of Sports Science and Medicine
ISSN: 1303 - 2968   
Ios-APP Journal of Sports Science and Medicine
Androit-APP Journal of Sports Science and Medicine
from September 2014
©Journal of Sports Science and Medicine (2007) 06, 117 - 125

Research article
The Use of Neural Network Technology to Model Swimming Performance
António José Silva,1,2 , Aldo Manuel Costa1, Paulo Moura Oliveira2,3, Victor Machado Reis1, José Saavedra4, Jurgen Perl5, Abel Rouboa2,3, Daniel Almeida Marinho1
Author Information
1 Sports Science Department of University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
2 CETAV, Research Centre, Vila Real, Portugal
3 Engineering Department of University of Trás-os-Montes and Alto Douro, Vila Real, Portugal
4 Sports Science Department of University of Extremadura, Spain
5 Institute of Computer Science, University of Maiz, Germany

António José Silva
✉ Universidade de Trás-os-Montes e Alto Douro, Departamento de Ciências do Desporto, CIFOP, R. Dr. Manuel Cardona, 5000 Vila Real, Portugal
Publish Date
Received: 20-09-2006
Accepted: 24-01-2007
Published (online): 01-03-2007
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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.

           Key Points
  • The non-linear analysis resulting from the use of feed forward neural network allowed us the development of four performance models.
  • The mean difference between the true and estimated results performed by each one of the four neural network models constructed was low.
  • The neural network tool can be a good approach in the resolution of the performance modeling as an alternative to the standard statistical models that presume well-defined distributions and independence among all inputs.
  • The use of neural networks for sports sciences application allowed us to create very realistic models for swimming performance prediction based on previous selected criterions that were related with the dependent variable (performance).
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