Prediction of the result in race walking using regularized regression models

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 method has simplified the structure of the model by eliminating 5 out of 18 predictors.