Development of new upper body power prediction models for cross-country skiers by using different machine learning methods

Upper Body Power (UBP) is one of the most important determinants that directly affects the performance of cross-country skiers during races. In this study, new models have been developed to predict the 10-second UBP (UBP₁₀) and 60-second UBP (UBP₆₀) of cross-country skiers by using different machine learning methods including Cascade Correlation Network (CCN), Radial Basis Function Neural Network (RBF) and Decision Tree Forest (DTF). The predictor variables used to develop prediction models are age, gender, body mass index (BMI), heart rate (HR), maximal oxygen uptake (VO₂max) and exercise time. By using 10-fold cross-validation on the dataset, the performance of the prediction models has been evaluated by calculating their multiple correlation coefficient (R) and standard error of estimate (SEE). The results show that the CCN-based model including the predictor variables age, gender, BMI and VO₂max yields the lowest SEE both for the prediction of UBP₁₀ and UBP₆₀.