Developing a Data-Driven Player Ranking in Soccer using Predictive Model Weights
Joel Brooks*, Massachusetts Institute of Tec; Matthew Kerr, Massachusetts Institute of Technology; John Guttag, MIT
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.
Filed under: Classification