Optimizing Sporting Actions Effectiveness: A Machine Learning Approach to Uncover Key Variables in the Men’s Professional Doubles Tennis Serve

Abstract

This study used a novel machine learning approach to uncover key serve variables that maximize effectiveness in men’s professional doubles tennis. A large dataset of 14,146 serves from 97 Davis Cup doubles matches played between 2010 and 2019 was analyzed using explainable AI techniques. The angle and distance from the bounce to the sidelines of the serves were found to best distinguish the points won with aces from rallies lasting more than three strokes. Optimal serve angle ranges of 5.7–8.7° substantially increased the probability of serving an ace by over 80%, compared to around 30% when serving used more central angles. Lateral bounce distances of 0–28 cm from the sidelines also boosted the ace probability by over 50%. The serve speed was shown to have less influence on serve effectiveness as compared to singles tennis, with velocities above 187 km h−1 only increasing the probability of serving an ace by 10%. These findings have important practical implications for the tactical decision-making and technical training of serves in men’s professional doubles tennis. The data highlight that the angle and placement of serves are more important than velocity for attaining effective serves in doubles. Coaches and players can use this knowledge to pay special attention to the most important variables in the effectiveness of serves, such as the line distance and angle, in order to maximize the performance of the doubles serve. The novel methodology used in this study provides a valid and reliable way to calculate the efficiency of actions in various sport disciplines using tracking data and machine learning approaches.