[2402.12149] MLFEF: Machine Learning Fusion Model with Empirical Formula to Explore the Momentum in Competitive Sports

Computer Science > Machine Learning arXiv:2402.12149 (cs) [Submitted on 19 Feb 2024 (v1), last revised 24 Mar 2026 (this version, v2)] Title:MLFEF: Machine Learning Fusion Model with Empirical Formula to Explore the Momentum in Competitive Sports Authors:Ruixin Peng, Ziqing Li View a PDF of the paper titled MLFEF: Machine Learning Fusion Model with Empirical Formula to Explore the Momentum in Competitive Sports, by Ruixin Peng and 1 other authors View PDF Abstract: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 momentu...