Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review
Football, as a dynamic and complex sport, demands an understanding of tactical behaviors to excel in training and competition. Artificial intelligence (AI) has revolutionized the tactical performance analysis in football, offering unprecedented data analytics insights for players, coaches, and analy...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-05-01
|
| Series: | Frontiers in Sports and Active Living |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fspor.2025.1569155/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850239730121179136 |
|---|---|
| author | José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira Eduardo Maio Eduardo Maio Eduardo Maio Pedro Afonso Pedro Afonso Samuel Encarnação Samuel Encarnação Samuel Encarnação Guilherme F. Machado Guilherme F. Machado Ryland Morgans Tiago M. Barbosa Tiago M. Barbosa António M. Monteiro António M. Monteiro Pedro Forte Pedro Forte Pedro Forte Ricardo Ferraz Ricardo Ferraz Luís Branquinho Luís Branquinho Luís Branquinho Luís Branquinho |
| author_facet | José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira Eduardo Maio Eduardo Maio Eduardo Maio Pedro Afonso Pedro Afonso Samuel Encarnação Samuel Encarnação Samuel Encarnação Guilherme F. Machado Guilherme F. Machado Ryland Morgans Tiago M. Barbosa Tiago M. Barbosa António M. Monteiro António M. Monteiro Pedro Forte Pedro Forte Pedro Forte Ricardo Ferraz Ricardo Ferraz Luís Branquinho Luís Branquinho Luís Branquinho Luís Branquinho |
| author_sort | José E. Teixeira |
| collection | DOAJ |
| description | Football, as a dynamic and complex sport, demands an understanding of tactical behaviors to excel in training and competition. Artificial intelligence (AI) has revolutionized the tactical performance analysis in football, offering unprecedented data analytics insights for players, coaches, and analysts. This systematic review aims to examine and map out the current state of research on AI-based tactical behavior, collective dynamics, and movement patterns in football. A total of 2,548 articles were identified following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Population-Intervention-Comparators-Outcomes framework. By synthesizing findings from 32 studies, this review elucidates the available AI-based techniques to analyze tactical behavior and identify the collective dynamic based on artificial neural networks, deep learning, machine learning, and time-series techniques. Concretely, the tactical behavior was expressed by spatiotemporal tracking data using convolutional neural networks, recurrent neural networks, variational recurrent neural networks, and variational autoencoders, Delaunay method, player rank, hierarchical clustering, logistic regression, XGBoost, random forest classifier, repeated incremental pruning produce error reduction, principal component analysis, and T-distributed stochastic neighbor embedding. Furthermore, collective dynamics and patterns were mapped by graph metrics such as betweenness centrality, eccentricity, efficiency, vulnerability, clustering coefficient, and page rank, expected possession value, pitch control map classifier, computer vision techniques, expected goals, 3D ball trajectories, dangerousity assessment, pass probability model, and total passes attempted. The performance of technical-tactical key indicators was expressed by team possession, team formation, team strategy, team-space control efficiency, determining team formations, coordination patterns, analyzing player interactions, ball trajectories, and pass effectiveness. In conclusion, the AI-based models can effectively reshape the landscape of spatiotemporal tracking data into training and practice routines with real-time decision-making support, performance prediction, match management, tactical-strategic thinking, and training task design. Nevertheless, there are still challenges for the real practical application of AI-based techniques, as well as ethical regulation and the formation of professional profiles that combine sports science, data analytics, computer science, and coaching expertise. |
| format | Article |
| id | doaj-art-10b3c7d8131c4ba9b97d6a202cf5cee3 |
| institution | OA Journals |
| issn | 2624-9367 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sports and Active Living |
| spelling | doaj-art-10b3c7d8131c4ba9b97d6a202cf5cee32025-08-20T02:01:04ZengFrontiers Media S.A.Frontiers in Sports and Active Living2624-93672025-05-01710.3389/fspor.2025.15691551569155Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic reviewJosé E. Teixeira0José E. Teixeira1José E. Teixeira2José E. Teixeira3José E. Teixeira4José E. Teixeira5Eduardo Maio6Eduardo Maio7Eduardo Maio8Pedro Afonso9Pedro Afonso10Samuel Encarnação11Samuel Encarnação12Samuel Encarnação13Guilherme F. Machado14Guilherme F. Machado15Ryland Morgans16Tiago M. Barbosa17Tiago M. Barbosa18António M. Monteiro19António M. Monteiro20Pedro Forte21Pedro Forte22Pedro Forte23Ricardo Ferraz24Ricardo Ferraz25Luís Branquinho26Luís Branquinho27Luís Branquinho28Luís Branquinho29Department of Sports Sciences, Polytechnic of Guarda, Guarda, PortugalDepartment of Sports Sciences, Polytechnic of Cávado and Ave, Guimarães, PortugalSPRINT—Sport Physical Activity and Health Research & Innovation Center, Guarda, PortugalResearch Center in Sports, Health and Human Development, Covilhã, PortugalResearch Center for Active Living and Wellbeing (LiveWell), Polytechnic Institute of Bragança, Bragança, PortugalCI-ISCE, Instituto Superior de Ciências Educativas do Douro (ISCE Douro), Penafiel, PortugalBiosciences Higher School of Elvas, Polytechnic Institute of Portalegre, Portalegre, PortugalLife Quality Research Center (LQRC-CIEQV), Santarém, PortugalDepartment of Sport Sciences, University of Beira Interior, Covilhã, PortugalBiosciences Higher School of Elvas, Polytechnic Institute of Portalegre, Portalegre, PortugalLife Quality Research Center (LQRC-CIEQV), Santarém, PortugalResearch Center for Active Living and Wellbeing (LiveWell), Polytechnic Institute of Bragança, Bragança, Portugal0Department of Sports Sciences, Polytechnic Institute of Bragança, Bragança, Portugal1Department of Physical Education, Sport and Human Movement, Universidad Autónoma de Madrid (UAM), Ciudad Universitaria de Cantoblanco, Madrid, Spain2Centre of Research and Studies in Soccer (NUPEF), Universidade Federal de Viçosa, Viçosa, Brazil3Scientific Department and Department of Athletes' Integration and Development, Paulista Football Federation (FPF), São Paulo, Brazil4School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United KingdomResearch Center for Active Living and Wellbeing (LiveWell), Polytechnic Institute of Bragança, Bragança, Portugal0Department of Sports Sciences, Polytechnic Institute of Bragança, Bragança, PortugalResearch Center for Active Living and Wellbeing (LiveWell), Polytechnic Institute of Bragança, Bragança, Portugal0Department of Sports Sciences, Polytechnic Institute of Bragança, Bragança, PortugalResearch Center for Active Living and Wellbeing (LiveWell), Polytechnic Institute of Bragança, Bragança, PortugalCI-ISCE, Instituto Superior de Ciências Educativas do Douro (ISCE Douro), Penafiel, Portugal0Department of Sports Sciences, Polytechnic Institute of Bragança, Bragança, PortugalResearch Center in Sports, Health and Human Development, Covilhã, PortugalDepartment of Sport Sciences, University of Beira Interior, Covilhã, PortugalResearch Center in Sports, Health and Human Development, Covilhã, PortugalBiosciences Higher School of Elvas, Polytechnic Institute of Portalegre, Portalegre, PortugalCI-ISCE, Instituto Superior de Ciências Educativas do Douro (ISCE Douro), Penafiel, PortugalLife Quality Research Center (LQRC-CIEQV), Santarém, PortugalFootball, as a dynamic and complex sport, demands an understanding of tactical behaviors to excel in training and competition. Artificial intelligence (AI) has revolutionized the tactical performance analysis in football, offering unprecedented data analytics insights for players, coaches, and analysts. This systematic review aims to examine and map out the current state of research on AI-based tactical behavior, collective dynamics, and movement patterns in football. A total of 2,548 articles were identified following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and the Population-Intervention-Comparators-Outcomes framework. By synthesizing findings from 32 studies, this review elucidates the available AI-based techniques to analyze tactical behavior and identify the collective dynamic based on artificial neural networks, deep learning, machine learning, and time-series techniques. Concretely, the tactical behavior was expressed by spatiotemporal tracking data using convolutional neural networks, recurrent neural networks, variational recurrent neural networks, and variational autoencoders, Delaunay method, player rank, hierarchical clustering, logistic regression, XGBoost, random forest classifier, repeated incremental pruning produce error reduction, principal component analysis, and T-distributed stochastic neighbor embedding. Furthermore, collective dynamics and patterns were mapped by graph metrics such as betweenness centrality, eccentricity, efficiency, vulnerability, clustering coefficient, and page rank, expected possession value, pitch control map classifier, computer vision techniques, expected goals, 3D ball trajectories, dangerousity assessment, pass probability model, and total passes attempted. The performance of technical-tactical key indicators was expressed by team possession, team formation, team strategy, team-space control efficiency, determining team formations, coordination patterns, analyzing player interactions, ball trajectories, and pass effectiveness. In conclusion, the AI-based models can effectively reshape the landscape of spatiotemporal tracking data into training and practice routines with real-time decision-making support, performance prediction, match management, tactical-strategic thinking, and training task design. Nevertheless, there are still challenges for the real practical application of AI-based techniques, as well as ethical regulation and the formation of professional profiles that combine sports science, data analytics, computer science, and coaching expertise.https://www.frontiersin.org/articles/10.3389/fspor.2025.1569155/fullperformancetactical analysismachine learningneural networksdeep learningAI |
| spellingShingle | José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira José E. Teixeira Eduardo Maio Eduardo Maio Eduardo Maio Pedro Afonso Pedro Afonso Samuel Encarnação Samuel Encarnação Samuel Encarnação Guilherme F. Machado Guilherme F. Machado Ryland Morgans Tiago M. Barbosa Tiago M. Barbosa António M. Monteiro António M. Monteiro Pedro Forte Pedro Forte Pedro Forte Ricardo Ferraz Ricardo Ferraz Luís Branquinho Luís Branquinho Luís Branquinho Luís Branquinho Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review Frontiers in Sports and Active Living performance tactical analysis machine learning neural networks deep learning AI |
| title | Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review |
| title_full | Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review |
| title_fullStr | Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review |
| title_full_unstemmed | Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review |
| title_short | Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review |
| title_sort | mapping football tactical behavior and collective dynamics with artificial intelligence a systematic review |
| topic | performance tactical analysis machine learning neural networks deep learning AI |
| url | https://www.frontiersin.org/articles/10.3389/fspor.2025.1569155/full |
| work_keys_str_mv | AT joseeteixeira mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT joseeteixeira mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT joseeteixeira mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT joseeteixeira mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT joseeteixeira mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT joseeteixeira mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT eduardomaio mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT eduardomaio mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT eduardomaio mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT pedroafonso mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT pedroafonso mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT samuelencarnacao mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT samuelencarnacao mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT samuelencarnacao mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT guilhermefmachado mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT guilhermefmachado mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT rylandmorgans mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT tiagombarbosa mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT tiagombarbosa mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT antoniommonteiro mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT antoniommonteiro mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT pedroforte mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT pedroforte mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT pedroforte mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT ricardoferraz mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT ricardoferraz mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT luisbranquinho mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT luisbranquinho mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT luisbranquinho mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview AT luisbranquinho mappingfootballtacticalbehaviorandcollectivedynamicswithartificialintelligenceasystematicreview |