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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.

journal of quantitative analysis in sports

Individual role classification for players defending corners in football (soccer): Categorisation of the defensive role for each player in a corner kick using positional data