Rating Player Actions in Soccer

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We present a data-driven model that rates actions of the player in soccer with respect to their contribution to ball possession phases. This study approach consists of two interconnected parts: (i) a trajectory prediction model that is learned from real tracking data and predicts movements of players and (ii) a prediction model for the outcome of a ball possession phase. Interactions between players and a ball are captured by a graph recurrent neural network (GRNN) and we show empirically that the network reliably predicts both, player trajectories as well as outcomes of ball possession phases. We derive a set of aggregated performance indicators to compare players with respect to. to their contribution to the success of their team.

Original languageEnglish
Article number682986
JournalFrontiers in Sports and Active Living
Volume3
Number of pages14
ISSN2642-9367
DOIs
Publication statusPublished - 15.07.2021

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