Abstract
We propose to analyse the origin of goals in professional football (soccer) in a purely data-driven approach. Based on positional and event data of 3,457 goals from two seasons German Bundesliga and 2nd Bundesliga (2018/20,219 and 2019/2020), we devise a rich set of 37 features that can be extracted automatically and propose a hierarchical clustering approach to identify group structures. The results consist of 50 interpretable clusters revealing insights into scoring patterns. The hierarchical clustering found 8 alone standing clusters (penalties, direct free kicks, kick and rush, one-two’s, assisted by header, assisted by throw-in) and nine categories (e.g., corners) combining more granular patterns (e.g., five subcategories of corner-goals). We provide a thorough discussion of the clustering and show its relevance for practical applications in opponent analysis, player scouting and for long-term investigations. All stages of this work have been supported by professional analysts from clubs and federation.
| Original language | English |
|---|---|
| Journal | Journal of Sports Sciences |
| Volume | 39 |
| Issue number | 22 |
| Pages (from-to) | 2525-2544 |
| Number of pages | 20 |
| ISSN | 0264-0414 |
| DOIs | |
| Publication status | Published - 17.11.2021 |
Bibliographical note
Publisher Copyright:© 2021 Informa UK Limited, trading as Taylor & Francis Group.
Research areas and keywords
- Sports analytics
- Professional football (Soccer)
- Hierarchical Clustering
- Tactical Analysis
- Business informatics
ASJC Scopus Subject Areas
- Orthopedics and Sports Medicine
- Physical Therapy, Sports Therapy and Rehabilitation
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