Lasso Ridge based XGBoost and Deep_LSTM Help Tennis Players Perform better
This provides insights for sports analytics and player training in tennis, though it appears incremental with hybrid methods.
The study tackled predicting tennis match outcomes and quantifying game fluctuations using data from the 2023 Wimbledon final, achieving 94% accuracy in match prediction and mean squared errors of 0.036-0.064 for fluctuation scores.
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 LSTM_Deep 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.