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Using machine learning to draw inferences from pass location data in soccer
Author(s) -
Brooks Joel,
Kerr Matthew,
Guttag John
Publication year - 2016
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11318
Subject(s) - computer science , artificial intelligence , task (project management) , rank (graph theory) , machine learning , mathematics , engineering , systems engineering , combinatorics
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