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Computational Intelligence and Neuroscience
Volume 2015, Article ID 735060, 9 pages
http://dx.doi.org/10.1155/2015/735060
Research Article

Predictive Modeling in Race Walking

1Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland
2Faculty of Physical Education, University of Rzeszów, 35-959 Rzeszów, Poland

Received 15 December 2014; Accepted 18 June 2015

Academic Editor: Okyay Kaynak

Copyright © 2015 Krzysztof Wiktorowicz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.