Quantification and Prediction of Tennis Match Momentum Based on a Data-Driven Approach

The purpose of this paper is to discuss the quantification and prediction of momentum in tennis, in order to provide a scientific basis for coaches and players to develop strategies. By analyzing the data from the 2023 Wimbledon men's singles tournament, we first defined five key metrics to measure momentum: the number of untouchable winning serves (UWS), the value of the difference in the number of successful games (SG), the number of consecutive successes (CS), the frequency of being a server (S), and the average distance run (AD). Then, the hierarchical analysis method (AHP) was used to determine the weights of each indicator and derive an effective formula for calculating momentum. In addition, the nonrandomness of momentum swing and successive successes in the race was verified by running tests. To predict momentum changes, a model based on the random forest algorithm was constructed, which showed high accuracy and stability. Further analysis showed that the serve (S) and the number of untouchable winning serves (UWS) were the key factors affecting momentum swing. Finally, the results of the model's generalization and sensitivity analysis in different types of matches are discussed. This study not only provides new perspectives for understanding the dynamics of tennis matches, but also provides strong support for practical applications, which can help to improve athletes' game performance. Future work will focus on expanding the indicator system, combining more dimensions of data and exploring the application of deep learning techniques, with a view to further improving the predictive ability and practicality of the model.