Rating Player Actions in Soccer
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Chronological data
Date of first publication2021-07-15
Date of publication in PubData 2024-11-19
Language of the resource
English
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Abstract
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.
Keywords
Sports Analytics; Soccer; Graph Networks; Trajectory Prediction; Trajectory Data
Faculty / department
Notes
This publication was funded by the Open Access Publication Fund of Leuphana University Lüneburg.