Authors:
Krzysztof Przednowek
1
;
Janusz Iskra
2
and
Karolina H. Przednowek
1
Affiliations:
1
University of Rzeszow, Poland
;
2
Academy of Physical Education in Katowice, Poland
Keyword(s):
Hurdling, Shrinkage Regression, Artificial Neural Networks, Sport Prediction.
Related
Ontology
Subjects/Areas/Topics:
Coaching
;
Computer Systems in Sports
;
Health, Sports Performance and Support Technology
;
Simulation and Mathematical Modeling
;
Sport Science Research and Technology
;
Sport Statistics and Analyses
Abstract:
The paper presents the use of linear and nonlinear multivariable models as tools to predict the results of
400-metres hurdles races in two different time frames. The constructed models predict the results obtained
by a competitor with suggested training loads for a selected training phase or for an annual training cycle.
All the models were constructed using the training data of 21 athletes from the Polish National Team. The
athletes were characterized by a high level of performance (score for 400 metre hurdles: 51.26±1.24 s). The
linear methods of analysis include: classical model of ordinary least squares (OLS) regression and
regularized methods such as ridge regression, LASSO regression. The nonlinear methods include: artificial
neural networks as multilayer perceptron (MLP) and radial basis function (RBF) network. In order to
compare and choose the best model leave-one-out cross-validation (LOOCV) is used. The outcome of the
studies shows that Lasso shrinkage regression
is the best linear model for predicting the results in both
analysed time frames. The prediction error for a training period was at the level of 0.69 s, whereas for the
annual training cycle was at the level of 0.39 s. Application of artificial neural network methods failed to
correct the prediction error. The best neural network predicted the result with an error of 0.72 s for training
periods and 0.74 for annual training cycle. Additionally, for both training frames the optimal set of
predictors was calculated.
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