Mythbusting Set-Pieces in Soccer

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Abstract: The gap in resources between the richest and poorest teams in world football is growing wider each season as demonstrated by Paris St German spending a world record 222 million Euro on Neymar who scored 15 goals in the 17/18 Ligue 1 season. The ability for a small market team to replicate the same goal output for the price of an effective set-piece strategy is a clear market inefficiency that can be exploited. Given the large amount of data now available, it should now be possible to quantitatively measure these things. In this paper, we employ a mythbuster’s approach by first stating the common-held belief and seeing if this is true or not. To do this we present an attribute-driven approach to set-piece analysis, which utilizes a hybrid of deep-learning methods to detect complex attributes such as defensive marking schemes, and hand-crafted features to enable interpretability. Specifically, we employ a Convolutional Neural Network (CNN), which adequately captures the defensive structure of a team around set-pieces. Additionally, we use expected metrics such as expected goal value (xG) to value the quality of chances that a team creates based on the location and quality of delivery in addition to the defensive attributes. Our research demonstrates which types of delivery are the most dangerous and how this varies by team. As a result, we are now able to provide a recommendation to a coach and analyst about how a team may play against them and how to prepare for and exploit the opposition’s strengths and weaknesses.

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