Prediction of upper body power and maximal oxygen uptake of cross-country skiers using different regression methods

Upper body power (UBP) and maximal oxygen uptake (VO2max) are the two most important determinants of cross-country ski race performance. Although numerous studies exist to measure UBP of cross-country skiers, to date, no study has attempted to predict UBP of cross-country skiers. The purpose of this thesis is to develop prediction models for estimating 10-second UBP (UBP10), 60-second UBP (UBP60) and VO2max of cross-country skiers using different regression methods, namely support vector machines using the radial-basis function (SVM-RBF), linear SVM (SVM-Linear), multi layer perceptron (MLP) and multiple linear regression (MLP). Several UBP and VO2max prediction models have been developed using different data sets and combination of the predictor variables such as protocol, age, gender, height, weight, body mass index (BMI), heart rate (HR), heart rate at lactate threshold (HRLT) and exercise time. By using 10-fold cross-validation on the data sets, the performance of the models has been evaluated by calculating their standard error of estimates (SEE's) and multiple correlation coefficients (R's). The results show that SVM-RBF-based UBP and VO2max prediction models perform better (i.e. yield lower SEE's and higher R's) than the prediction models developed by other regression methods.