Machine Learning-based Decision Model for Optimal Return Placement in Tennis

This study collected 1 808 sets data from 7 matches of Carlos Alcaraz who was the champion of 2022 US Open, the tennis ball landing spots were quantitatively abstracted based on regional distribution, and a framework of micro-scale automated tennis tactic assistance was provided. The machine learning models such as Support Vector Machines, Neural Networks, and Random Forests were used to construct an optimal ball landing decision model for multi-stroke rallies. The results showed that the random forest was the most effective method in constructing the decision model, and the peak accuracy can reach to 76.66% for ball landing decisions after 5 strokes. Based on machine learning, the intelligent quantitative analysis can provide data and technical support for the improvement of professional tennis tactics.