Predicting upper body power of cross-country skiers using machine learning methods combined with feature selection

Upper body power (UBP) is one of the most important determinants of cross-country ski race performance. In this study, General Regression Neural Networks (GRNN), Radial Basis Function Neural Network (RBF), Decision Tree Forest (DTF) combined with a feature selection algorithm have been used to developed prediction models for estimating 10-second UBP (UBP₁₀) and 60-second UBP (UBP₆₀) of cross-country skiers. By using the Relief-F attribute selection algorithm, the score of each attribute has been calculated. Seven different UBP₁₀ and UBP₆₀ prediction models have been developed by removing the attribute with the lowest score at a time. By using 10-fold cross-validation on the data set, the performance of the prediction models has been evaluated by calculating their multiple correlation coefficients (R) and standard error of estimate (SEE). The results show that gender and VO₂max are the most effective variables for prediction of UBP₁₀ and UBP₆₀.