Several performance metrics for quantifying the in-game performances of individual football players have been proposed in recent years. Although the majority of the on-the-ball actions during games constitutes of passes, many of the currently available metrics focus on measuring the quality of shots only. To help bridge this gap, we propose a novel approach to measure players` on-the-ball contributions from passes during games. Our proposed approach measures the expected impact of each pass on the scoreline.
© Copyright 2019 Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330. Veröffentlicht von Springer. Alle Rechte vorbehalten.