A study of forecasting tennis matches via the Glicko model

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 outco...

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Bibliographic Details
Published in:PloS one 2022-04, Vol.17 (4), p.e0266838-e0266838
Main Authors: Yue, Jack C, Chou, Elizabeth P, Hsieh, Ming-Hui, Hsiao, Li-Chen
Format: Article
Language:English
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0266838