Dynamic research of tennis match based on machine learning

Abstract

The study employed the Adaboost algorithm to identify optimal indicators of momentum, demonstrating that momentum could significantly predict the likelihood of a player's subsequent wins or losses. This study offers a novel exploration into the dynamics of tennis matches, focusing on the concept of "momentum" and its impact on match outcomes. Through an analysis of the 2023 Wimbledon Men’s Singles final, the research demonstrates how momentum significantly influences the likelihood of a player's success, challenging traditional perceptions of match progression as merely a sequence of random events. By employing advanced statistical methods, including the Adaboost algorithm for identifying key indicators of momentum and the Wald-Wolfowitz Runs Test to examine the randomness of winning streaks, the findings reveal a substantial effect of momentum on the game's flow. The analysis further utilizes the CatBoost algorithm to pinpoint factors critical in shifting momentum between players, such as set scores, serving dynamics, and return depths. These indicators provide concrete evidence against the randomness of match dynamics, suggesting that strategic adjustments based on these factors can potentially alter the course of a match. The implications of this research extend beyond academic interest, offering practical insights for players and coaches in strategizing to leverage momentum for competitive advantage. By debunking the myth of randomness in tennis match outcomes, this study underscores the importance of psychological and strategic elements in sports performance, providing a foundation for further research and practical applications in the field of sports science and coaching.