Load-injury modeling and risk mitigation in team sports

Team-sport performance science increasingly focuses on understanding how training and competition workloads contribute to injury risk, athlete availability, and performance across different levels of play, from developing and sub-elite athletes to elite competitors. Advances in GPS tracking, wearable sensors, inertial measurement units, neuromuscular testing, strength assessment, and wellness monitoring have enabled practitioners to quantify external and internal loads with increasing precision. However, the relationship between workload and injury is complex, multifactorial, and highly individual, with risk influenced by factors such as prior injury, physical capacity, fatigue, recovery, match congestion, psychosocial stress, and environmental conditions. Although acute-to-chronic workload concepts and other predictive approaches have informed applied practice, their validity, transferability, and practical value remain debated. Current evidence supports moving beyond single metrics and universal thresholds toward longitudinal, individualized, and context-sensitive models. Important gaps remain in the integration of physical profiling, physiological variability, sex-specific factors, and exposure history into reliable risk-mitigation frameworks. In particular, women’s team sports remain underrepresented, limiting understanding of how menstrual cycle variability and other physiological factors may interact with workload, recovery, and injury risk. Further research is needed to develop, validate, and translate load-injury models that support practical decision-making across performance contexts without overstating their predictive certainty.This Research Topic aims to advance the development and application of individualized load-injury models for injury prevention and risk mitigation in team sports across performance levels. It will examine how external and internal workload, physical characteristics, physiological responses, recovery status, previous injury, and contextual factors can be integrated to identify changing risk profiles and guide training decisions. Contributions may evaluate established and emerging workload metrics, investigate dose-response relationships between training exposure and injury, and test statistical, machine-learning, or longitudinal approaches to risk estimation. The Research Topic also seeks to clarify how monitoring systems can support progressive load management, rehabilitation, return-to-play, and return-to-performance decisions. Particular attention will be given to sex-specific and female-athlete considerations, including menstrual cycle variability and physiological differences that may affect workload tolerance, recovery, and injury susceptibility. Studies in elite settings are welcome as one important context, alongside developing, sub-elite, youth, and recreational team-sport populations. Performance and tactical outcomes may also be considered where they demonstrate how risk mitigation influences athlete availability and team-sport effectiveness.To gather further insights into load-injury modeling and risk mitigation across team sports and performance contexts, this Research Topic welcomes articles addressing, but not limited to, the following themes:• Workload monitoring and the quantification of external and internal training load.• Acute-to-chronic workload approaches and alternative load-injury models.• Individualized injury-risk profiling and multifactorial risk assessment.• Statistical, machine-learning, and longitudinal methods for predicting or explaining injury risk.• The influence of previous injury, training history, fatigue, recovery, and match congestion on injury occurrence.• Physical profiling, including strength, power, speed, endurance, neuromuscular function, and movement capacity.• Physiological monitoring, readiness assessment, and adaptation to training and competition.• Menstrual cycle variability, hormonal factors, and sex-specific approaches to load management.• Injury prevention programs, risk-mitigation strategies, and monitoring-guided training modification.• Rehabilitation, return-to-play, and return-to-performance frameworks.• Wearable sensors, GPS and tracking technologies, and integrated data systems.• Translating load-injury research into practical decisions by coaches, medical teams, and performance staff.• The effects of injury-risk mitigation on athlete availability, performance, and tactical outcomes.Accepted article types include Original Research, Systematic Review, Review, Mini Review, Brief Research Report, Perspective, Methods, and Case Reports focused on applied practice. Topic editors: Jorge E Morais, Jaime Serra Olivares, Javier García-Rubio Source: https://www.frontiersin.org/research-topics/86485/load-injury-modeling-and-risk-mitigation-in-team-sports