Excellent physical performance of soccer players is inevitable for the success of a team. Despite of this, a large-scale, quantitative analysis of the maximal speed of the players is missing due to the sensitive nature of trajectory datasets. We propose a novel method to derive the in-game speed profile of soccer players from event-based datasets, which are widely accessible. We show that eight games are enough to derive an accurate speed profile. We also reveal team level discrepancies: to estimate the maximal speed of the players of some teams 50% more games may be necessary. The speed characteristics of the players provide valuable insights for domains such as player scouting.
© Copyright 2015 Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2015 workshop. Published by Department of Computer Science, KU Leuven. All rights reserved.