Title: Predicting the potential of professional soccer players
Authors: Vroonen, Ruben
Decroos, Tom
Van Haaren, Jan
Davis, Jesse
Issue Date: 18-Sep-2017
Host Document: Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2017 workshop
Conference: ECML PKDD location:Skopje, Macedonia date:18-22 September 2017
Article number: 6
Abstract: Projecting how a player’s skill level will evolve in the future is a crucial problem faced by sports teams. Traditionally, player projections have been evaluated by human scouts, who are subjective and may suffer from biases. More recently, there has been interest in automated projection systems such as the PECOTA system for baseball and the CARMELO system for basketball. In this paper, we present a projection system for soccer players called APROPOS which is inspired by the CARMELO and PECOTA systems. APROPOS predicts the potential of a soccer player by searching a historical database to identify similar players of the same age. It then bases its prediction for the target player’s progression on how the similar previous players actually evolved. We evaluate APROPOS on players from the five biggest European soccer leagues and show that it clearly outperforms a more naive baseline.
Publication status: accepted
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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