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Individual ball possession in soccer.

Daniel Link1, Martin Hoernig2

  • 1Department of Exercise Science and Sport Informatics, Technical University of Munich, Munich. Germany.

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|July 11, 2017
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Summary

This study introduces machine learning models to detect individual and team ball possession in soccer using player tracking data. These models accurately classify ball control and possession, enhancing soccer performance analysis and tactical insights.

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Area of Science:

  • Sports Science
  • Data Science
  • Machine Learning

Background:

  • Accurate detection of individual and team ball possession is crucial for soccer performance analysis.
  • Existing methods may lack the granularity to differentiate types of ball control and possession phases.

Purpose of the Study:

  • To develop and evaluate machine learning models for detecting and classifying individual and team ball possession in soccer.
  • To analyze the spatio-temporal characteristics of ball possession and control.

Main Methods:

  • Utilized player position data from 60 German Bundesliga matches.
  • Developed machine learning models incorporating player-ball distance, motion, speed, and acceleration.
  • Employed Bayesian networks to classify the degree of ball control based on player-ball-opponent configurations.

Main Results:

  • Achieved high identification rates for Individual Ball Action (F=.88) and Individual Ball Possession (F=.83).
  • Classified Individual Ball Control with a rate of κ=.67.
  • Quantified Team Ball Possession (56:04 min), Team Playmaking (50:01 min), and Team Ball Control (17:49 min) per match.
  • Identified significant differences in Individual Ball Control times across player positions.

Conclusions:

  • The developed models provide a robust method for analyzing ball possession and control in soccer.
  • Results can improve automated match event detection and the identification of tactical structures.
  • Player-level possession data offers valuable insights into individual player roles and team strategies.