Putting team formations in association football into context

Abstract

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 trained a convolutional neural network to decompose each game into non-overlapping segments and classify these segments into phases with an average F 1-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 teams 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.