Predictive Modeling in Race Walking.
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.
Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland.
Krzysztof
K
Wiktorowicz
Faculty of Physical Education, University of Rzeszów, 35-959 Rzeszów, Poland.
Krzysztof
K
Przednowek
Faculty of Physical Education, University of Rzeszów, 35-959 Rzeszów, Poland.
Lesław
L
Lassota
Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, 35-959 Rzeszów, Poland.
Tomasz
T
Krzeszowski
D000465:Algorithms; D056352:Athletes; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D016138:Walking / Q000706:statistics & numerical data*