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

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.