Bayesian statistics meets sports: a comprehensive review

Santos-Fernandez Edgar (), Wu Paul () and Mengersen Kerrie L. ()
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Santos-Fernandez Edgar: Queensland University of Technology, Faculty of Science and Engineering, School of Mathematical Sciences, Y Block, Floor 8, Gardens Point Campus Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, Australia, e-mail: santosfe@qut.edu.au
Wu Paul: Queensland University of Technology, Faculty of Science and Engineering, School of Mathematical Sciences, Brisbane, Queensland, Australia
Mengersen Kerrie L.: Queensland University of Technology, Faculty of Science and Engineering, School of Mathematical Sciences, Brisbane, Queensland, Australia

Journal of Quantitative Analysis in Sports, 2019, vol. 15, issue 4, 289-312

Abstract: Bayesian methods are becoming increasingly popular in sports analytics. Identified advantages of the Bayesian approach include the ability to model complex problems, obtain probabilistic estimates and predictions that account for uncertainty, combine information sources and update learning as new data become available. The volume and variety of data produced in sports activities over recent years and the availability of software packages for Bayesian computation have contributed significantly to this growth. This comprehensive survey reviews and characterizes the latest advances in Bayesian statistics in sports, including methods and applications. We found that a large proportion of these articles focus on modeling/predicting the outcome of sports games and on the development of statistics that provides a better picture of athletes’ performance. We provide a description of some of the advances in basketball, football and baseball. We also summarise the sources of data used for the analysis and the most commonly used software for Bayesian computation. We found a similar number of publications between 2013 and 2018 as compared to those published in the three previous decades, which is an indication of the growing adoption rate of Bayesian methods in sports.

Keywords: Bayesian modelling; Bayesian regression; sports science; sports statistics (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1515/jqas-2018-0106

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