Clustering algorithm for formations in football games

TL;DR

This paper presents a hierarchical clustering algorithm that classifies football team formations into main types and sub-patterns using Delaunay triangulation, aiding tactical analysis.

Contribution

It introduces a novel clustering approach combining hierarchical clustering and Delaunay triangulation to analyze football formations at multiple levels.

Findings

Successfully classified main football formations like 442, 4141, 433, 541, and 343.

Enabled detailed sub-pattern recognition within each formation.

Provides a structured way to analyze team tactics based on formation data.

Abstract

This paper develops a clustering algorithm for formations in team sports, with a focus on football games. Our method first clusters formations into several average formations: `442,' `4141,' `433,' `541,' and `343.' Then, each average formation is further divided into more specific patterns in which the configurations of players are slightly different. The latter step is based on hierarchical clustering and the Delaunay method, which defines the formation of a team as an adjacency matrix of Delaunay triangulation. A formation clustered using our method is expressed in a form such as `442-C1'.