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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. [ABSTRACT FROM AUTHOR]

Purpose: Monitoring players' training load allows practitioners to enhance physical performance while reducing injury risk. The aim of this study was to identify the key external load indicators in professional U19 soccer. Methods: Twenty-four-professional Italian young (U19) soccer players were monitored by using the rating of perceived exertion (CR-10 RPE scale) and a wearable inertial sensor during the competitive season. Three main components were detected by a Principal Component Analysis (PCA): i) volume metabolic related component, ii) intensity mechanical stimuli component, and iii) intensity metabolic/mechanical component. We hence computed two scores (i.e. Performance [PERF] and total workload [WORK]) permitting to investigate the weekly microcycle. Results: Correlation analysis showed that scores (i.e. PERF and WORK) are low correlated (r = -0.20) suggesting that they were independent. Autocorrelation analysis showed that a weekly microcycle is detectable in all the scores. Two-way ANOVA RM showed a statistical difference between match day (MD) and playing position for the three PCA components and PERF score. Conclusion: We proposed an innovative approach to assess both the players' physical performance and training load by using a machine learning approach allowing reducing a large dataset in an objective way. This approach may help practitioners to prescribe the training in the microcycle based on the two scores. [ABSTRACT FROM AUTHOR]