Study on Momentum Assessment in Tennis Matches Based on Dynamic Weights and Machine Learning - Liu, Qinghua; Zhu, Boshen; Wang, Jiran; Sun, Tongtong (Transactions on Computer Science and Intelligent Systems Research, 2024)
In recent years, with the popularization and technological progress of tennis, momentum has been an important indicator of changes in the players’ state. Traditional evaluation methods cannot fully reflect their dynamic changes and multi-factor effects, so it is essential to establish a more accurate momentum evaluation model. In this study, seven key indicators, such as serve advantage, breakpoints, etc., were selected to measure momentum fluctuations in matches, and weight was assigned to each indicator to reflect their importance in the overall momentum. Among them, for serving advantage, the dynamic weight Wdy-serve is newly introduced in this study. Moreover, the momentum fluctuation of players in the tiebreaker between Carlos Alcaras and Novak Djokovic is further analyzed, and its effectiveness is verified using the match results. Further, to evaluate index influence on momentum, this study conducted an XGBoost and decision tree algorithm performance, based on analyzing the interpretability displayed shapes. Finally, the XGBoost model with better performance is selected for deep training and iteration in this study. To better understand the influence of each momentum index on the prediction results, this study uses the SHAP model to analyze the correlation of the training set data. Among them, serve advantage and breakpoints are the two most influential indicators, and they contribute the most to the prediction of momentum fluctuations. The dynamic weight Wdy-serve is particularly helpful in capturing the fluctuations in a player’s serve performance during a match. The analysis results of this study show that the change of momentum is not only closely related to the technical performance of the players but also significantly related to psychological factors.