Customer description
A professional football organization with multiple teams in national competitions, supported by its own performance team and medical staff.
Challenge
The team regularly suffered from injuries to key players, often caused by overload or fatigue. The medical staff worked reactively to signals, with little predictive power.
Solution
An AI model was developed that combined historical injuries, training load, match minutes, HRV and GPS data to estimate the injury risk per player on a daily basis.
Approach
- Data collection
Integration of GPS tracking, heart rate data, recovery time, subjective fatigue, and medical reports. - Model development
A predictive model was trained on injury types, load type and recovery time, tailored to individual profiles. - Staff dashboarding
Coaches and performance experts received daily insight into risk profiles and adjustment proposals per player. - Integration into training and planning
Training intensity was adjusted at the individual level, with real-time feedback.
Results
- 32% fewer muscle injuries in 6 months
- Improved communication between performance, medical staff and coaches
- More trust among players in guidance
Learnings
By using AI for injury prevention, sports medical guidance became predictive and personal. Prevention moved from experience to data-driven decision making.
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