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- The use of neural network technology to model swimming performancePublication . Silva, António; Costa, Aldo M.; Oliveira, Paulo Moura; Reis, VM; Saavedra, Jose M; Perl, Jurgen; Rouboa, Abel I; Marinho, Danielto 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 pointsThe 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).
- Tracking young talented swimmers: follow-up of performance and its biomechanical determinant factorsPublication . Morais, Jorge; Saavedra, Jose M; Costa, Mário Jorge; Silva, António; Marinho, Daniel; Barbosa, Tiago M.The aim of the study was to follow-up the stability of young talented swimmers' performance and its biomechanical determinant factors (i.e., anthropometrics, kinematics, hydrodynamics and efficiency) during a competitive season. Thirty three (15 boys and 18 girls) young swimmers (overall: 11.81 ± 0.75 years old and Tanner stages 1-2 by self-evaluation) were evaluated. Performance, anthropometrics, hydrodynamics, kinematics and efficiency variables were assessed at three moments during a competitive season. Performance had a significant improvement (with minimum effect size) and a moderate-very high stability throughout the season. In the anthropometrics domain all variables increased significantly (ranging from without to minimum effect size) between moments and had a moderate-very high stability. Hydrodynamics presented no variations between all moments and had a low-very high stability throughout the season. In the kinematics domain, there were no variations between moment one and three, except for an increase in stroke frequency (without size effect). Speed fluctuation remained constant, with no significant variations. All kinematic variables had a low-very high stability. Efficiency variables did not present variations between moment one and three and had a low-moderate stability. Overall, young swimmers showed a minimum improvement in performance and in anthropometric factors; and a moderate stability of performance and its determinant factors (i.e., anthropometrics, hydrodynamics, kinematics and efficiency) during the competitive season.