Translating player monitoring into training prescriptions: Real world soccer scenario and practical proposals - Pillitteri, G.; Clemente, F. M.; Sarmento, H.; Figuereido, A.; Rossi, A.; Bongiovanni, T.; Puleo, G.; Petrucci, M.; Foster, C.; Battaglia, G.; Bianco, A. (International Journal of Sports Science & Coaching, 2025)
Data-driven training prescription based on previous training or match data is thought to be associated with better training outcome, compared to prescription without considering any monitoring data. Understanding the complex relationship between training load, physical performance, fitness status, fatigue and injury risk represents a challenge for health and performance practitioners and researchers. Although studies have revealed a positive correlation between training load and injury risk, this cause-effect relation cannot be determined given the multifactorial nature of injuries. Additionally, conflicting findings have been published explaining the relationship between training load and injuries, underlining the importance of training load management, prescription, and communication within the multidisciplinary team to improve physical performance and reduce injury risk. In this sense, practitioners may benefit from practical examples based on training load data to make informed decisions for prescribing training. This narrative review provides real-world examples of training decisions based on training load data in soccer, including training prescription, drill design and multidisciplinary team communication. Finally, a framework was provided to make informed training prescription from a physiological standpoint and elucidate the relationship between training load and injury risk.