PubData Leuphana Repository: Rating Player Actions in Soccer

Please use this identifier to cite or link to this item: https://doi.org/10.48548/pubdata-1507

Resource type	Journal Article
Title(s)	Rating Player Actions in Soccer
DOI	10.48548/pubdata-1507
Handle	20.500.14123/1581
Creator	Dick, Uwe 1128098636 Tavakol, Maryam Brefeld, Ulf 0000-0001-9600-6463
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.
Language	English
Keywords	Sports Analytics; Soccer; Graph Networks; Trajectory Prediction; Trajectory Data
Year of publication in PubData	2024
Publishing type	Parallel publication
Publication version	Published version
Date issued	2021-07-15
Creation context	Research
Notes	This publication was funded by the Open Access Publication Fund of Leuphana University Lüneburg.
Published by	Medien- und Informationszentrum, Leuphana Universität Lüneburg
Related resources	Relations of this publication Relations of this publication