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In today's data-driven world of sports science, in-depth analysis of tennis match data is especially critical for improving athlete performance, refining training strategies, and enhancing the viewing experience. This study starts from all aspects of data and key features of the players during the game, using Stacking integrated machine learning methods, a kind of integrated algorithms through XGBoost, GBDT, CatBoost, and ExtraTrees to build prediction models, aiming to always keep an eye on the dynamics of the game, using the data to reveal the changes in the player's strength and to quantify the impact of the momentum on the player as well as the game's trend. The experimental results show that we have identified factors that influence changes in players' momentum, which can provide real-time feedback to coaches and players so that they can make data-based decisions during matches, opening up new avenues for improving tennis players' scoring ability and maximizing help for players to improve their winning percentage in matches.
Gao et al. (Thu,) studied this question.