Teixeira José E, Maio Eduardo, Afonso Pedro, Encarnação Samuel, Machado Guilherme F, Morgans Ryland, Barbosa Tiago M, Monteiro António M, Forte Pedro, Ferraz Ricardo, Branquinho Luís
Department of Sports Sciences, Polytechnic of Guarda, Guarda, Portugal.
Department of Sports Sciences, Polytechnic of Cávado and Ave, Guimarães, Portugal.
Front Sports Act Living. 2025 May 30;7:1569155. doi: 10.3389/fspor.2025.1569155. eCollection 2025.
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.
足球作为一项充满活力且复杂的运动，要想在训练和比赛中表现出色，就需要理解战术行为。人工智能（AI）彻底改变了足球战术表现分析，为球员、教练和分析师提供了前所未有的数据分析见解。本系统综述旨在审视并梳理当前关于足球中基于人工智能的战术行为、集体动态和运动模式的研究现状。按照系统综述和Meta分析的首选报告项目指南以及人群-干预-对照-结局框架，共识别出2548篇文章。通过综合32项研究的结果，本综述阐明了基于人工智能的可用技术，以分析战术行为并基于人工神经网络、深度学习、机器学习和时间序列技术识别集体动态。具体而言，战术行为通过使用卷积神经网络、循环神经网络、变分循环神经网络和变分自编码器、德劳内方法、球员排名、层次聚类、逻辑回归、XGBoost、随机森林分类器、重复增量剪枝降低误差、主成分分析和T分布随机邻域嵌入的时空跟踪数据来表达。此外，集体动态和模式通过诸如介数中心性、离心率、效率、脆弱性、聚类系数和页面排名等图指标、预期控球值、球场控制图分类器、计算机视觉技术、预期进球数、3D球轨迹、危险性评估、传球概率模型和尝试的总传球数来描绘。技术-战术关键指标的表现通过球队控球率、球队阵型、球队策略、球队空间控制效率、确定球队阵型、协调模式、分析球员互动、球轨迹和传球效率来体现。总之，基于人工智能的模型可以有效地将时空跟踪数据的格局重塑为具有实时决策支持、性能预测、比赛管理、战术战略思维和训练任务设计的训练和实践常规。然而，基于人工智能的技术在实际应用中仍然存在挑战，以及伦理规范和结合体育科学、数据分析、计算机科学和教练专业知识的专业形象的形成。
