A study of forecasting tennis matches via the Glicko model

• Published in: PloS one Vol. 17; no. 4; p. e0266838
• Main Authors: Yue, Jack C, Chou, Elizabeth P, Hsieh, Ming-Hui, Hsiao, Li-Chen
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.04.2022; Public Library of Science (PLoS)
• Subjects: Accuracy; Bayes Theorem; Bayesian analysis; Big Data; Biology and Life Sciences; Chess; Classification; Competitions; Computer and Information Sciences; Data analysis; Empirical analysis; Female; Forecasting; Forecasts and trends; Humans; Logistic Models; Machine Learning; Male; Mathematical models; Modelling; Neural networks; Physical Sciences; Physiological aspects; Random variables; Ratings & rankings; Regression analysis; Research and Analysis Methods; Social Sciences; Statistical analysis; Support vector machines; Tennis; Tournaments & championships; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0266838

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.