A Tennis Momentum Analysis Method Based on Gaussian Dynamics and Machine Learning

Momentum analysis in tennis matches has predominantly focused on qualitative methods, lacking systematic quantitative approaches. This study proposes a novel method that integrates Gaussian dynamics models with machine learning techniques to quantitatively analyze and predict momentum changes in tennis matches. By selecting and defining key indicators such as serve success rate and scoring rate, we developed an algorithm to identify and predict momentum shifts. The proposed method employs ensemble learning techniques, specifically Stacking, to combine multiple machine learning models, enhancing prediction accuracy and execution efficiency. Experimental validation using data from major tennis tournaments, including the 2023 Wimbledon Championships, demonstrates the effectiveness of the proposed approach. The results show that the Stacking model outperforms individual models in accuracy and robustness, providing scientific decision support for coaches and players. This method has low computational requirements, is simple to implement, and considers a wide range of variables, making it highly efficient. Future research will focus on further optimizing the model and applying it to other sports and match types.