Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review

José E Teixeira ^1,2,3,4,5,6, Eduardo Maio ^7,8,9, Pedro Afonso ^7,8

¹Department of Sports Sciences, Polytechnic of Guarda, Guarda, Portugal.
²Department of Sports Sciences, Polytechnic of Cávado and Ave, Guimarães, Portugal.
³SPRINT-Sport Physical Activity and Health Research & Innovation Center, Guarda, Portugal.
⁴Research Center in Sports, Health and Human Development, Covilhã, Portugal.
⁵Research Center for Active Living and Wellbeing (LiveWell), Polytechnic Institute of Bragança, Bragança, Portugal.
⁶CI-ISCE, Instituto Superior de Ciências Educativas do Douro (ISCE Douro), Penafiel, Portugal.
⁷Biosciences Higher School of Elvas, Polytechnic Institute of Portalegre, Portalegre, Portugal.
⁸Life Quality Research Center (LQRC-CIEQV), Santarém, Portugal.
⁹Department of Sport Sciences, University of Beira Interior, Covilhã, Portugal.
¹⁰Department of Sports Sciences, Polytechnic Institute of Bragança, Bragança, Portugal.
¹¹Department of Physical Education, Sport and Human Movement, Universidad Autónoma de Madrid (UAM), Ciudad Universitaria de Cantoblanco, Madrid, Spain.
¹²Centre of Research and Studies in Soccer (NUPEF), Universidade Federal de Viçosa, Viçosa, Brazil.
¹³Scientific Department and Department of Athletes' Integration and Development, Paulista Football Federation (FPF), São Paulo, Brazil.
¹⁴School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom.

⁰Department of Sports Sciences, Polytechnic of Guarda, Guarda, Portugal.

Frontiers in Sports and Active Living +

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June 16, 2025

View abstract on PubMed

Summary

Artificial intelligence (AI) enhances football tactical analysis by examining player movement and team dynamics. AI models effectively translate spatiotemporal data for improved training and real-time decision-making in the sport.

Area Of Science

Sports Science
Data Analytics
Artificial Intelligence

Background

Football performance relies heavily on tactical understanding and complex player interactions.
Artificial intelligence (AI) offers advanced capabilities for analyzing vast datasets in sports.
Understanding tactical behaviors and collective dynamics is crucial for optimizing football performance.

Purpose Of The Study

To systematically review and map research on AI-based tactical behavior analysis in football.
To identify AI techniques used for analyzing collective dynamics and player movement patterns.
To synthesize findings on AI applications for performance enhancement in football.

Main Methods

Systematic review of 2,548 articles following PRISMA guidelines.
Synthesis of findings from 32 selected studies.
Analysis of AI techniques including artificial neural networks, deep learning, machine learning, and time-series analysis.

Main Results

AI techniques like convolutional neural networks and recurrent neural networks analyze spatiotemporal data for tactical behavior.
Graph metrics, expected possession value, and computer vision map collective dynamics and patterns.
AI models effectively translate data for training, performance prediction, and match management.

Conclusions

AI significantly enhances football tactical analysis, providing real-time insights for decision-making and training.
Challenges remain in practical AI application, ethical regulation, and interdisciplinary expertise.
AI integration promises to reshape football strategy, performance prediction, and training methodologies.

Keywords:

AI deep learning machine learning neural networks performance tactical analysis

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