Individual role classification for players defending corners in football (soccer)

Author:

Bauer Pascal12ORCID,Anzer Gabriel23ORCID,Smith Joshua Wyatt4ORCID

Affiliation:

1. DFB-Campus , Schwarzwaldstraße 121, Frankfurt am Main , Hessen 60528 , Germany

2. Eberhard Karls Universität Tübingen, Wirtschafts- und Sozialwissenschaftliche Fakultät, Institut für Sportwissenschaft, Arbeitsbereich Sportpsychologie & Methodenlehre , Wilhelmstraße 124, 72074 Tübingen , Germany

3. Hertha BSC Berlin, Hanns-Braun-Straße , Friesenhaus 2, 14053 Berlin , Germany

4. Department of Mathematics and Statistics , Concordia University , 1455 De Maisonneuve Blvd. W. , Montreal , QC , H3G 1M8 , Canada

Abstract

Abstract Choosing the right defensive corner-strategy is a crucial task for each coach in professional football (soccer). Although corners are repeatable and static situations, due to their low conversion rates, several studies in literature failed to find useable insights about the efficiency of various corner strategies. Our work aims to fill this gap. We hand-label the role of each defensive player from 213 corners in 33 matches, where we then employ an augmentation strategy to increase the number of data points. By combining a convolutional neural network with a long short-term memory neural network, we are able to detect the defensive strategy of each player based on positional data. We identify which of seven well-established roles a defensive player conducted (player-marking, zonal-marking, placed for counterattack, back-space, short defender, near-post, and far-post). The model achieves an overall weighted accuracy of 89.3%, and in the case of player-marking, we are able to accurately detect which offensive player the defender is marking 80.8% of the time. The performance of the model is evaluated against a rule-based baseline model, as well as by an inter-labeller accuracy. We demonstrate that rules can also be used to support the labelling process and serve as a baseline for weak supervision approaches. We show three concrete use-cases on how this approach can support a more informed and fact-based decision making process.

Publisher

Walter de Gruyter GmbH

Subject

Decision Sciences (miscellaneous),Social Sciences (miscellaneous)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Putting team formations in association football into context;Journal of Sports Analytics;2023-03-23

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