MLFEF: Machine Learning Fusion Model with Empirical Formula to Explore the Momentum in Competitive Sports
<|reference_start|>MLFEF: Machine Learning Fusion Model with Empirical Formula to Explore the Momentum in Competitive Sports: Tennis is so popular that coaches and players are curious about factors other than skill, such as momentum. This article will try to define and quantify momentum, providing a basis for real-time analysis of tennis matches. Based on the tennis Grand Slam men's singles match data in recent years, we built two models, one is to build a model based on data-driven, and the other is to build a model based on empirical formulas. For the data-driven model, we first found a large amount of public data including public data on tennis matches in the past five years and personal information data of players. Then the data is preprocessed, and feature engineered, and a fusion model of SVM, Random Forrest algorithm and XGBoost was established. For the mechanism analysis model, important features were selected based on the suggestions of many tennis players and enthusiasts, the sliding window algorithm was used to calculate the weight, and different methods were used to visualize the momentum. For further analysis of the momentum fluctuation, it is based on the popular CUMSUM algorithm in the industry as well as the RUN Test, and the result shows the momentum is not random and the trend might be random. At last, the robustness of the fusion model is analyzed by Monte Carlo simulation.<|reference_end|>
@article{peng2024mlfef:,
    title={MLFEF: Machine Learning Fusion Model with Empirical Formula to Explore
  the Momentum in Competitive Sports},
    author={Ruixin Peng, Ziqing Li},
    journal={arXiv preprint arXiv:2402.12149},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12149},
    primaryClass={cs.LG}
}