Using machine learning to draw inferences from pass location data in soccer. (20th June 2016)

• Record Type: Journal Article
• Authors: Brooks, Joel; Kerr, Matthew; Guttag, John
• Is Part Of: Statistical analysis and data mining. Volume 9:Number 5(2016)
• Page Start: 338
• Page End: 349
• Publication Date: 2016-06-20
• Subjects: machine learning -- sports analytics -- soccer analytics; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11318
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract : In this paper, we present two approaches to analyzing pass event data to uncover sometimes‐nonobvious insights into the game of soccer. We illustrate the utility of our methods by applying them to data from the 2012–2013 La Liga season. We first show that teams are characterized by where on the pitch they attempt passes, and can be identified by their passing styles. Using heatmaps of pass locations as features, we achieved a mean accuracy of 87% in a 20‐team classification task. We also investigated using pass locations over the course of a possession to predict shots. For this task, we achieved an area under the receiver operating characteristic (AUROC) of 0.785. Finally, we used the weights of the predictive model to rank players by the value of their passes. Shockingly, Cristiano Ronaldo and Lionel Messi topped the rankings. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016