The origins of goals in the German Bundesliga
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In: Journal of Sports Sciences, Vol. 39, No. 22, 17.11.2021, p. 2525-2544.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - The origins of goals in the German Bundesliga
AU - Anzer, Gabriel
AU - Bauer, Pascal
AU - Brefeld, Ulf
N1 - Publisher Copyright: © 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2021/11/17
Y1 - 2021/11/17
N2 - 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.
AB - 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.
KW - Sports analytics
KW - Professional football (Soccer)
KW - Hierarchical Clustering
KW - Tactical Analysis
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85111431422&partnerID=8YFLogxK
U2 - 10.1080/02640414.2021.1943981
DO - 10.1080/02640414.2021.1943981
M3 - Journal articles
C2 - 34308758
VL - 39
SP - 2525
EP - 2544
JO - Journal of Sports Sciences
JF - Journal of Sports Sciences
SN - 0264-0414
IS - 22
ER -