Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 96-103 |
| Number of pages | 8 |
| Journal | CEUR Workshop Proceedings |
| Volume | 1970 |
| Publication status | Published - 2015 |
| Event | 2nd Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2015, co-located with 2015 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 - Porto, Portugal Duration: 11 Sept 2015 → 11 Sept 2015 |
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