Seeing in to the future: using self-propelled particle models to aid player decision-making in soccer

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Abstract

Francisco Peralta Alguacil: Football Data Scientist, Hammarby IF
Pablo Piñones Arce, Hammarby IF
David Sumpter, Professor of Applied Mathematics, Uppsala University
Javier Fernandez, Head of Sports Analytics, FC Barcelona

Abstract

Soccer has some of the most complex team movement patterns of any team sport. Recently, several measurements have been proposed for evaluating the value of dribbles, passes or shots. The next step is to automatically identify the alternative actions available to players both on and off the ball.

We address this challenge by building a ‘self-propelled player’ model, simulating attacking roles by maximizing three criteria: pass probability, pitch Impact and pitch control. The model assumes that players can anticipate the movement of the other players on the pitch a few seconds in to the future and maximize the future value of their position. We compared these simulations to player decisions during matches by top-flight men’s teams of Hammarby IF and FC Barcelona. In simulations, we found that the two or three players nearest to the ball tended to optimize the product of pass probability and pitch impact.

In a first-team coaching intervention at Hammarby, players re-watched attacking situations in which they had been involved, and were asked to discuss their own actions in comparison with the model. The players often agreed that the model captured complex game patterns, including off-ball actions. The model also recommended runs that the players hadn’t taken, which the players also found realistic and aided discussions. Despite the novelty of these discussions, the players showed a high willingness to engage with them. We further explored how these techniques can be used to provide automated feedback to players within the match cycle.

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