Effective Serve Strategies in Table Tennis: Combining CNN and Random Forest Techniques

This study examines the novel approach of integrating CNNs with RFs to enhance table tennis serve strategies to improve players' performance. Thanks to the footage and motion capture data of professional matches, the model of categorizing and predicting successful serve methods is designed. Hence, the CNN-RF model performance was evaluated with a test accuracy [converting it into 100% for more accessible analysis] of 91%, precision of 91%, recall of 91%, and an F1-score of 91%, implying good classification for the various server types. The research carried out by the authors identified all parameters that are closely related to the effectiveness of serves, including, for example, the speed and the spin of the serve. The learning and validation displays learning curves with minimal over-learning, proving the model's reliability. Comparative analysis of other models, such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), showed the superior performance of the Cnn-Rf model developed to apply this work. Concerning these elements, focusing on the heatmap and correlation graphs that demonstrated strong positive correlations between service attributes and success is crucial. They found the confusion matrix emphasizing 91% of the classification accuracy and the ROC curve that depicted an AUC of 0. 95 were shown to have very high discrimination capability. This paper offers valuable findings for players and coaches to make computational decisions to enhance the serve skills to play well and efficiently. It also advances the area of sports analysis as the powerful effect of contemporary machine learning methods has been illustrated in table tennis.