Putting team formations in association football into context

Journal of Sports Analytics

article

Pascal Bauer¹  Gabriel Anzer¹  Laurie Shaw¹ 

[1] Institute of Sports Science, University of Tübingen

关键词: Association football;    soccer;    sports analytics;    human-in-the-loop machine learning;   

DOI  :  10.3233/JSA-220620

来源: IOS Press

【 摘 要 】

Choosing the right formation is one of the coach’s most important decisions in football. Teams change formation dynamically throughout matches to achieve their immediate objective: to retain possession, progress the ball up-field and create (or prevent) goal-scoring opportunities. In this work we identify the unique formations used by teams in distinct phases of play in a large sample of tracking data. This we achieve in two steps: first, we train a convolutional neural network to decompose each game into non-overlapping segments and classify these segments into phases with an average F1-score of 0.76. We then measure and contextualize unique formations used in each distinct phase of play. While conventional discussion tends to reduce team formations over an entire match to a single three-digit code (e.g. 4-4-2; 4 defender, 4 midfielder, 2 striker), we provide an objective representation of team formations per phase of play. Using the most frequently occurring phases of play, mid-block, we identify and contextualize six unique formations. A long-term analysis in the German Bundesliga allows us to quantify the efficiency of each formation, and to present a helpful scouting tool to identify how well a coach’s preferred playing style is suited to a potential club.

【 授权许可】

Unknown