Big Data and Artificial Intelligence Applications for Injury Prevention in Football Players

Abstract

This study examines the key factors that increase football players’ risk of injury, considering physical, biomechanical, psychological, and contextual variables. Identified risk factors include prior injury history, neuromuscular fatigue, biomechanical asymmetries, inadequate training load, age, playing position, psychological stress, and adverse environmental conditions. Advanced tools using Artificial Intelligence (AI) and Big Data are reviewed for their role in integrating these variables for injury prevention. Techniques include supervised algorithms (Random Forest, SVM, k-NN), deep neural networks (CNN, RNN), wearable sensors (IoT), integrated Big Data platforms, clustering methods, and explainable AI (XAI) models. These approaches outperform traditional methods by enabling real-time monitoring, data integration, dynamic adaptation, and individualized planning—achieving over 90% accuracy in injury prediction and reducing injury incidence by up to 30%. The study follows a systematic review methodology based on the PICO model and PRISMA protocol. It includes bibliometric and content analyses and offers a critical discussion on evidence gaps and practical implementation. Conclusions highlight main findings, acknowledge limitations, and provide recommendations for future research.