Prediction of the result in race walking using regularized regression models

• Autorzy: Przednowek K. , Wiktorowicz K.
• Języki publikacji: EN

Abstrakty

EN

The following paper presents the use of regularized linear models as tools to optimize training process. The models were calculated by using data collected from race-walkers’ training events. The models used predict the outcomes over a 3 km race and following a prescribed training plan. The material included a total of 122 training patterns made by 21 players. The methods of analysis include: classical model of OLS regression, ridge regression, LASSO regression and elastic net regression. In order to compare and choose the best method a cross-validation of the leave-one-out was used. All models were calculated using R language with additional packages. The best model was determined by the LASSO method which generates an error of about 26 seconds. The methodhas simplified the structure of the model by eliminating 5 out of 18 predictors.

Słowa kluczowe

EN

regularized regression; shrinkage methods; variable selection; prediction of sport result; race walking; R language

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2013
• Tom: Vol. 7, nr 2
• Strony: 45--58
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Przednowek K.

• Faculty of Physical Education, University of Rzeszow, Poland

autor

Wiktorowicz K.

• Division of Informatics and Control, Rzeszow University of Technology, Poland

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