Player skill estimation for soccer match prediction Online publication date: Thu, 26-Dec-2019
by Juan Pablo Maldonado López; Vojtěch Jindra
International Journal of Applied Pattern Recognition (IJAPR), Vol. 6, No. 1, 2019
Abstract: In this paper we propose two algorithms for assessing skill of soccer players. We introduce an adaptation of the Elo rating algorithm, which is widely used in chess, to handle teams. A different approach is proposed to assess player skill as a function of the proportion of matches won. Since it is hard to measure skill directly, to estimate the performance of our approach we propose as a proxy the number of correctly predicted match outcomes of a classifier that takes as input our algorithms' output. Numerical experiments suggest that our approach is competitive with book-keeper's estimates, even though we rely only on historical game outcome data.
Online publication date: Thu, 26-Dec-2019
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