Upper body power (UBP) is a major parameter in cross-country skiing that strongly influences the performance of skiers during races. Although the direct measurement of UBP in laboratory environment provides the most accurate assessment, practical difficulties such as the need of sophisticated and expensive testing equipment and trained stuff have given rise to the necessity of predicting and identifying the relevant predictors of UBP. This study aims to develop feature selection-based prediction models to predict 10-s UBP (UBP10) and 60-s UBP (UBP60) of cross-country skiers with the help of support vector machines (SVMs) using different kernel functions. For model testing, tenfold cross-validation has been carried out, and the performance of the models has been assessed by computing their standard error of estimates (SEEs) and multiple correlation coefficients (Rs). With respect to the obtained results, the lowest SEEs (27.81 and 18.73 W) and the highest Rs (0.92 and 0.94) for the most accurate UBP10 and UBP60 prediction models have been obtained by SVM using the radial basis function kernel (SVM-RBF), respectively. The predictor variables gender and age have been found to be the most important variables in predicting the UBP10 and UBP60 of cross-country skiers. Also, the results have been compared to those of multilayer perceptron and multiple linear regression-based prediction models. Prediction models based on SVM-RBF exhibit much lower SEEs and higher Rs than the prediction models developed by other regression methods and can be safely utilized in making UBP predictions within acceptable limits of accuracy.