Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights
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adf0793-brooksA.pdf
Description
Accepted version
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306.82 KB
Format
Adobe PDF
Checksum (MD5)
2985eae1551abd0ded0ae83519014ed0
Author(s)
Brooks, Joel David
Kerr, Matthew
Guttag, John
Date Issued
August 2016
Journal
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher
Association for Computing Machinery (ACM)
Citation
Brooks, Joel, et al. “Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16, 13-17 August, 2017, San Francisco, California, USA, ACM Press, 2016, pp. 49–55. © 2016 the Authors
Version
Author's final manuscript
Abstract
Quantitative evaluation of the ability of soccer players to contribute to team offensive performance is typically based on goals scored, assists made, and shots taken. In this paper, we describe a novel player ranking system based entirely on the value of passes completed. This value is derived based on the relationship of pass locations in a possession and shot opportunities generated. This relationship is learned by applying a supervised machine learning model to pass locations in event data from the 2012-2013 La Liga season. Interestingly, though this metric is based entirely on passes, the derived player rankings are largely consistent with general perceptions of offensive ability, e.g., Messi and Ronaldo are near the top. Additionally, when used to rank midfielders, it separates the more offensively-minded players from others.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2939672.2939695
