Finding Factors Influencing Tennis Match Trends Based on AHP-TOPSIS and LightGBM

Abstract

The paper conducts a comprehensive analysis of the 2023 Wimbledon men's singles tournament in an attempt to quantify momentum to look at swings in play and runs of success, and to make predictions about flows of momentum. The paper introduces an innovative approach to analyzing tennis match dynamics by integrating the Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) with the LightGBM algorithm. The AHP-TOPSIS model quantifies player momentum, identifying key factors such as serving ability, strength, mentality, and in-game state that influence match outcomes. A novel aspect of this study is the application of the CUSUM algorithm to detect significant turning points within matches, providing a granular insight into the flow of play. Subsequently, the LightGBM model, known for its efficiency and accuracy, is employed to predict these turning points, offering a comprehensive predictive framework that enhances the understanding of match dynamics. The paper's findings underscore the predictive power of the serving factor and the state factor in determining match swings. This research contributes to the field by offering a more nuanced and data-driven perspective on tennis match analysis, with practical implications for both players and coaches in strategizing for competitive advantage.