Rating Player Actions in Soccer - Dick, Uwe; Tavakol, Maryam; Brefeld, Ulf (Frontiers in Sports and Active Living, 2021)
We present a data-driven model that rates player actions in soccer with respect to their contribution to ball possession phases. Our 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 ball are captured by a graph recurrent neural network 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 wrt. to their contribution to their team's success.