Open AccessA study of forecasting tennis matches via the Glicko model
Author(s) -
Jack C. Yue,
Elizabeth P. Chou,
Ming-Hui Hsieh,
Li-Chen Hsiao
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0266838
Subject(s) - machine learning , artificial intelligence , computer science , logistic regression , gradient boosting , boosting (machine learning) , support vector machine , statistical model , artificial neural network , ranking (information retrieval) , variables , predictive modelling , variable (mathematics) , naive bayes classifier , random forest , mathematics , mathematical analysis
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.