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

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.

Presented at:
Machine Learning and Data Mining for Sports Analytics 2016, Riva del Garda, Italy, September 19, 2016

 Record created 2016-09-06, last modified 2018-03-17

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