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Data-Driven Visual Performance Analysis in Soccer: An Exploratory Prototype.

Alejandro Benito Santos1, Roberto Theron1, Antonio Losada1

  • 1Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain.

Frontiers in Psychology
|December 21, 2018
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Summary
This summary is machine-generated.

This study introduces a software prototype for analyzing soccer team tactics using visualization and computer vision. It aids analysts in understanding collective behavior from geospatial data, enhancing performance analysis.

Keywords:
collective tactical behaviorfootball associationinformation visualizationperformance analysisvisual analytics

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Area of Science:

  • Sports Science
  • Data Analysis
  • Computer Vision

Background:

  • Elite soccer analysis increasingly relies on large, specific datasets.
  • Technological advancements enable cost-effective data collection for sports activities.
  • Growing data complexity necessitates advanced tools for performance analysis.

Purpose of the Study:

  • To present a software prototype for analyzing collective tactical behavior in soccer.
  • To assist researchers and performance analysts in studying group dynamics during games and training.
  • To demonstrate the utility of visualization and computer vision in sports data analysis.

Main Methods:

  • Utilized geospatial data from a professional soccer match.
  • Developed a software prototype incorporating visualization and computer-vision techniques.
  • Applied the prototype in two distinct case studies to showcase its capabilities.

Main Results:

  • Demonstrated the prototype's effectiveness in analyzing collective behavior from geospatial data.
  • Validated the efficiency of implemented visualization techniques.
  • Showcased how visual analysis enhances fundamental tasks for sports experts.

Conclusions:

  • The developed software prototype effectively aids in the study of collective tactical behavior in soccer.
  • Visualization techniques integrated into the prototype improve the understanding of sports data.
  • This approach complements traditional methods, offering enhanced insights for performance analysts.