About this Course
This course introduces the scientific foundations of sports injury causation, workload modelling, and the emerging role of artificial intelligence in injury prevention. Learners will explore how injury mechanisms have evolved from simple monocausal views to multifactorial, dynamic, and systems-based approaches that reflect real athlete complexity. Through clear explanations and practical football examples, the course covers essential concepts such as intrinsic and extrinsic risk factors, the dynamic recursive model, the workload–injury relationship, and modern systems thinking used in elite performance environments. The second part of the course examines precision workload metrics, including acute: chronic workload ratios, high-speed running thresholds, acceleration load, sleep quality monitoring, and contextual load management. Finally, learners discover how AI and machine learning are transforming injury prediction, from multi-modal data integrations to SVM models, decision trees, Bayesian networks, individualised risk profiles, and early-warning systems used in elite clubs. By the end of this course, learners will understand the biological, physiological, and data-driven mechanisms behind injuries, and how modern AI-enhanced monitoring strategies reduce injury risk, optimize performance, and enhance player availability
What You'll Learn
- Understand modern injury causation models and systems-based frameworks in elite sport
- Apply precise workload metrics to manage injury risk and player availability
- Explore how AI and ML predict injuries using multi-modal performance data
Prerequisites
- No deep prior experience is required, but basic computer and internet skills are helpful
- Ability to read course instructions in English and complete short practice activities
Instructors
Marisa Sáenz