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Clustering algorithm for formations in football games.

Takuma Narizuka1, Yoshihiro Yamazaki2

  • 1Department of Physics, Faculty of Science and Engineering, Chuo University, Bunkyo, Tokyo, 112-8551, Japan. pararel@gmail.com.

Scientific Reports
|September 13, 2019
PubMed
Summary

This study introduces a novel clustering algorithm for analyzing football (soccer) formations. The method quantifies team styles by examining player positions and transitions within game formations.

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Area of Science:

  • Sports Analytics
  • Computational Geometry
  • Data Mining

Background:

  • Effective team formations are crucial for success in competitive team sports like football (soccer).
  • Quantitative analysis of dynamic team formations is needed for objective assessment of team styles.
  • Existing methods lack a generalized framework for dynamic formation characterization.

Purpose of the Study:

  • To develop a general framework for quantitatively analyzing and assessing team styles through dynamic formation structures in football (soccer).
  • To create a clustering algorithm based on the Delaunay method for characterizing team formations.
  • To enable visualization, quantitative comparison, and time-series analysis of formations.

Main Methods:

  • Development of a clustering algorithm for football formations using the Delaunay method.
  • Defining team formations as adjacency matrices of Delaunay triangulations.
  • Application of hierarchical clustering to subdivide average formations into specific patterns.

Main Results:

  • Successfully clustered football game heat maps into average formations: "442", "4141", "433", "541", and "343".
  • Hierarchical clustering revealed more specific formation patterns based on player configurations.
  • The algorithm facilitates visualization and quantitative comparison of formations across different time scales.

Conclusions:

  • The developed algorithm effectively characterizes dynamic team formations in football (soccer).
  • It enables the extraction of team styles by analyzing player positional exchange and formation transitions.
  • The method provides insights into typical formation transition patterns for specific teams.