Mapping football tactical behavior and collective dynamics with artificial intelligence

A systematic review

Bibliographic Data

ID16969031
AuthorsJosé E Teixeira (0000-0003-4612-3623), Eduardo Maio, Pedro Afonso (0000-0003-0959-3538), Samuel Encarnação (0000-0003-2965-2777), Guilherme F Machado, Ryland Morgans (0000-0003-2007-4827), Tiago M Barbosa, António M Monteiro (0000-0003-4467-1722), Pedro Forte (0000-0002-5985-8875), Ricardo Ferraz (0000-0001-9885-8811), Luís Branquinho (0000-0001-9000-5419)
Year2025
Volume7
Publication date2025-05-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Sports and Active Living (JOURNAL)
Journal identifiersISSN: 2624-9367 • E-ISSN: 2624-9367
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fspor.2025.1569155
LanguageEN
Citations received2
References cited65

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

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