Regression shrinkage and neural models in predicting the results of 400-metres hurdles races

K Przednowek; J Iskra; A Maszczyk; M Nawrocka

doi:10.5604/20831862.1224463

eISSN: 2083-1862
ISSN: 0860-021X

Biology of Sport

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4/2016
vol. 33

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abstract:

Original paper

Regression shrinkage and neural models in predicting the results of 400-metres hurdles races

K Przednowek

¹

,

J Iskra

²

,

A Maszczyk

³

,

M Nawrocka

³

Faculty of Physical Education, University of Rzeszow, Poland
Faculty of Physical Education and Physiotherapy, Opole University of Technology, Opole, Poland
Faculty of Physical Education, Academy of Physical Education in Katowice, Poland

Biol. Sport 2016;33:415-421

DOI: https://doi.org/10.5604/20831862.1224463

Online publish date: 2016/11/24

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This study presents the application of regression shrinkage and artificial neural networks in predicting the results of 400-metres hurdles races. The regression models predict the results for suggested training loads in the selected three-month training period. The material of the research was based on training data of 21 Polish hurdlers from the Polish National Athletics Team Association. The athletes were characterized by a high level of performance. To assess the predictive ability of the constructed models a method of leave-one-out cross-validation was used. The analysis showed that the method generating the smallest prediction error was the LASSO regression extended by quadratic terms. The optimal model generated the prediction error of 0.59 s. Otherwise the optimal set of input variables (by reducing 8 of the 27 predictors) was defined. The results obtained justify the use of regression shrinkage in predicting sports outcomes. The resulting model can be used as a tool to assist the coach in planning training loads in a selected training period.

keywords:

Regression shrinkage and neural models in predicting the results of 400-metres hurdles races

K Przednowek 1 , J Iskra 2 , A Maszczyk 3 , M Nawrocka 3

Predicting in sport, 400-metres hurdles, Regression shrinkage, Neural modelling

K Przednowek

¹

,

J Iskra

²

,

A Maszczyk

³

,

M Nawrocka

³