The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions

2016/09/05 by Lucas Maystre, Maystre, Lucas, Victor Kristof +5
Computer Science · Economics, Econometrics and Finance · Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Sports Analytics and Performance #Statistics Education and Methodologies

paper · pdf · doi:10.48550/arxiv.1609.01176

openalex publication_date 2016/09/05 · openalex created_date 2016/09/16 · openalex updated_date 2026/07/28

Abstract

In this work, we draw attention to a connection between skill-based models of game outcomes and Gaussian process classification models. The Gaussian process perspective enables a) a principled way of dealing with uncertainty and b) rich models, specified through kernel functions. Using this connection, we tackle the problem of predicting outcomes of football matches between national teams. We develop a player kernel that relates any two football matches through the players lined up on the field. This makes it possible to share knowledge gained from observing matches between clubs (available in large quantities) and matches between national teams (available only in limited quantities). We evaluate our approach on the Euro 2008, 2012 and 2016 final tournaments.

Citations

• Ties in Paired-Comparison Experiments: A Generalization of the Bradley-Terry Model

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