Comparison of individual playing styles in football

Guan Tianyu, Sumit Sarkar and Swartz Tim B. ()
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Guan Tianyu: Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, Ontario M3J1P3, Canada
Swartz Tim B.: Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, Canada

Journal of Quantitative Analysis in Sports, 2024, vol. 20, issue 4, 351-364

Abstract: This paper attempts to identify football players who have a similar style to a player of interest. Playing style is not adequately quantified with traditional statistics, and therefore style statistics are created using tracking data. Tracking data allow us to monitor players throughout a match, and therefore include both “on-the-ball” and “off-the-ball” observations. Having developed style features, tractable discrepancy measures are introduced that are based on Kullback–Leibler divergence in the context of multivariate normal distributions. Examples are provided where a pool of players from the Chinese Super League are identified as having a playing style that is similar to players of interest.

Keywords: big data; Bayesian analyses; divergence measures; player tracking data; spatio-temporal analyses (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/jqas-2024-0041

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