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A study of forecasting tennis matches via the Glicko model

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  • Jack C Yue
  • Elizabeth P Chou
  • Ming-Hui Hsieh
  • Li-Chen Hsiao

Abstract

Tennis is a popular sport, and professional tennis matches are probably the most watched games globally. Many studies consider statistical or machine learning models to predict the results of professional tennis matches. In this study, we propose a statistical approach for predicting the match outcomes of Grand Slam tournaments, in addition to applying exploratory data analysis (EDA) to explore variables related to match results. The proposed approach introduces new variables via the Glicko rating model, a Bayesian method commonly used in professional chess. We use EDA tools to determine important variables and apply classification models (e.g., logistic regression, support vector machine, neural network and light gradient boosting machine) to evaluate the classification results through cross-validation. The empirical study is based on men’s and women’s single matches of Grand Slam tournaments (2000–2019). Our analysis results show that professional tennis ranking is the most important variable and that the accuracy of the proposed Glicko model is slightly higher than that of other models.

Suggested Citation

  • Jack C Yue & Elizabeth P Chou & Ming-Hui Hsieh & Li-Chen Hsiao, 2022. "A study of forecasting tennis matches via the Glicko model," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-12, April.
    • Handle: RePEc:plo:pone00:0266838
    DOI: 10.1371/journal.pone.0266838
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    References listed on IDEAS

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    1. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
    2. P. Gorgi & S. J. Koopman & R. Lit, 2019. "The analysis and forecasting of tennis matches by using a high dimensional dynamic model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1393-1409, October.
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