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- W2963549583 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." @default.
- W2963549583 created "2019-07-30" @default.
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- W2963549583 date "2016-01-01" @default.
- W2963549583 modified "2023-09-25" @default.
- W2963549583 title "The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions" @default.
- W2963549583 hasPublicationYear "2016" @default.
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