2024/05/11 by Wankang Zhai, Yuhan Wang, Zhai, Wankang +1
Computer Science · Economics, Econometrics and Finance · #FOS: Computer and information sciences #I.2 #Machine Learning (cs.LG) #Sports Analytics and Performance #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2405.07030
openalex publication_date 2024/05/11 · openalex created_date 2024/05/15 · openalex updated_date 2026/07/28
Understanding the dynamics of momentum and game fluctuation in tennis matches is cru-cial for predicting match outcomes and enhancing player performance. In this study, we present a comprehensive analysis of these factors using a dataset from the 2023 Wimbledon final. Ini-tially, we develop a sliding-window-based scoring model to assess player performance, ac-counting for the influence of serving dominance through a serve decay factor. Additionally, we introduce a novel approach, Lasso-Ridge-based XGBoost, to quantify momentum effects, lev-eraging the predictive power of XGBoost while mitigating overfitting through regularization. Through experimentation, we achieve an accuracy of 94% in predicting match outcomes, iden-tifying key factors influencing winning rates. Subsequently, we propose a Derivative of the winning rate algorithm to quantify game fluctuation, employing an LSTMDeep model to pre-dict fluctuation scores. Our model effectively captures temporal correlations in momentum fea-tures, yielding mean squared errors ranging from 0.036 to 0.064. Furthermore, we explore me-ta-learning using MAML to transfer our model to predict outcomes in ping-pong matches, though results indicate a comparative performance decline. Our findings provide valuable in-sights into momentum dynamics and game fluctuation, offering implications for sports analytics and player training strategies.