Analyzing Momentum Shifts in Tennis: A Machine-Learning Approach to Predicting Match Outcomes

This study examines the impact of momentum dynamics on tennis match outcomes, addressing the limitations of traditional performance prediction methods. Using data from the 2023 Wimbledon men’s singles matches, a data-driven framework was developed to analyze factors influencing player performance. Factor analysis identified key independent variables, while a decision-tree model optimized through random-forest and genetic algorithm techniques was employed to predict match outcomes. Momentum was quantified using the Kendall consistency test, which revealed a significant correlation between momentum shifts and match results. The analysis highlighted that variations in momentum difference between players are crucial predictors, achieving a success rate of 78%. Visualizations further demonstrated that momentum fluctuations closely align with critical match events, offering a detailed understanding of performance dynamics. At the same time, the model has also been applied to the prediction of other tennis games and even table tennis games. It can adaptively adjust the K value and has a good fitting effect.Compared to conventional statistical methods, which often overlook the role of momentum, this model provides deeper insights into the interplay between momentum and match outcomes. While the framework shows strong applicability to tennis, its extension to other sports, such as table tennis, requires further exploration. These findings underscore the importance of momentum in competitive sports, offering new perspectives for match analysis, player training, and strategic planning and paving the way for future research into momentum dynamics across various sports disciplines.