Sports Prediction and Betting Models in the Machine Learning Age: The Case of Tennis

Journal of Sports Analytics, Vol. 7, 2021, pp. 99-117.

Posted: 9 Jan 2020 Last revised: 8 Sep 2021

Date Written: September 1, 2021

Abstract

Machine learning and its numerous variants have meanwhile become established tools in many areas of society. Several attempts have been made to apply machine learning to the prediction of the outcome of professional sports events and to exploit "inefficiencies" in the corresponding betting markets. On the example of tennis, this paper extends previous research by conducting one of the most extensive studies of its kind and applying a wide range of machine learning techniques to male and female professional singles matches. The paper shows that the average prediction accuracy cannot be increased to more than about 70%. Irrespective of the used model, most of the relevant information is embedded in the betting markets, and adding other match- and player-specific data does not lead to any significant improvement. Returns from applying predictions to the sports betting market are subject to high volatility and mainly negative over the longer term. This conclusion holds across most tested models, various money management strategies, and for backing the match favorites or outsiders. The use of model ensembles that combine the predictions from multiple approaches proves to be the most promising choice.

Keywords: Machine Learning, Model Ensembles, Classification, Sports Betting, Tennis

JEL Classification: C10, C53, Z20

Suggested Citation

Wilkens, Sascha, Sports Prediction and Betting Models in the Machine Learning Age: The Case of Tennis (September 1, 2021). Journal of Sports Analytics, Vol. 7, 2021, pp. 99-117., Available at SSRN: https://ssrn.com/abstract=3506302 or http://dx.doi.org/10.2139/ssrn.3506302

Sascha Wilkens (Contact Author)

Independent

No Address Available

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