Lasso Ridge based XGBoost and Deep_LSTM Help Tennis Players Perform better
<|reference_start|>Lasso Ridge based XGBoost and Deep_LSTM Help Tennis Players Perform better: 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.<|reference_end|>
@article{zhai2024lasso,
    title={Lasso Ridge based XGBoost and Deep_LSTM Help Tennis Players Perform
  better},
    author={Wankang Zhai, Yuhan Wang},
    journal={arXiv preprint arXiv:2405.07030},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07030},
    primaryClass={cs.LG}
}