The purpose of this study was to assess the possibilities of predicting playing successfulness in competitive tennis by using machine learning methods applied to young players` motor abilities and morphological test results. The classification of players according to their competitive successfulness was performed using several methods: the naive Bayes classification method, decision tree, the C4.5 algorithm, the k-nearest neighbour, support vector machine (SVM), and logistic regression. After discretising the players' successfulness into quality classes, the possibility of automatically identifying the most promising attributes was tested using the ReliefF method and the wrapper approach. Both the naive Bayes method with ReliefF and logistic regression with the wrapper approach proved to be accurate predictors of competitive performance in the age group under 12 years and in the age group between 12 and 16 years. The most promising attribute was racquet ball handling. Predictions of the competitive performance of tennis players proved to be a highly complex issue because the accuracy of the prediction models in our study, based on morphological and motor factors, was relatively poor.
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