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<p>Predicting game outcomes has significantly garnered the interest of researchers in recent years. The role of player performance is integral in-game analytics, impacting the interpretation and results of the analysis. Our work presents an AI for Science (AI4Sci) method to use real-time data from each game point to determine essential feature values, formulate and assess the impact of psychological momentum, and employ machine learning methodology on mid-match data for predicting the game’s victor. The data source is from Wimbledon and US Open games from 2017 to 2022, a total of 1592 games, and utilize 363 games of 2023 to evaluate their forecasting ability. We first obtained weights through information entropy and defined psychological momentum, and then 3 best classifiers, random forest, CatBoost, and Logistic Regression, were detected to assess the features. Additionally, we implemented a soft voting ensemble method integrating the Random Forest and CatBoost classifiers. All four models achieve over 90% accuracy and F1-score, with the soft voting classifier performing the best (accuracy: 97.5%, F1 score: 97.4%). These models achieve predictive accuracies above 70% using the first 25% data of a game.</p>

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Predicting tennis match outcomes mid-game using machine learning on psychological and physical data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月8日

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  • Boyuan Li,
  • Zihui Deng,
  • Gaurav Gupta,
  • Jinger Li,
  • Yixuan Miao

摘要

Predicting game outcomes has significantly garnered the interest of researchers in recent years. The role of player performance is integral in-game analytics, impacting the interpretation and results of the analysis. Our work presents an AI for Science (AI4Sci) method to use real-time data from each game point to determine essential feature values, formulate and assess the impact of psychological momentum, and employ machine learning methodology on mid-match data for predicting the game’s victor. The data source is from Wimbledon and US Open games from 2017 to 2022, a total of 1592 games, and utilize 363 games of 2023 to evaluate their forecasting ability. We first obtained weights through information entropy and defined psychological momentum, and then 3 best classifiers, random forest, CatBoost, and Logistic Regression, were detected to assess the features. Additionally, we implemented a soft voting ensemble method integrating the Random Forest and CatBoost classifiers. All four models achieve over 90% accuracy and F1-score, with the soft voting classifier performing the best (accuracy: 97.5%, F1 score: 97.4%). These models achieve predictive accuracies above 70% using the first 25% data of a game.