Zone7 feeds a team's existing training load, fitness tests, and injury records into a model to compute each player's injury risk over an upcoming period, and advises whether to reduce load or whether more training is fine. The core proposition is straightforward: rather than rehab after the fact, avoid it beforehand.
Features and use scenarios
The system connects the GPS vests, fitness tests, and medical records the team already uses, not requiring replacing existing equipment. The output is each player's risk level and trigger reasons, giving the fitness and medical teams a basis for discussion. The company has publicly announced cases of collaborating with professional soccer clubs to reduce injury days, but individual teams' results vary greatly, so look at your own data quality when adopting.
It suits professional teams' sports-medicine and fitness teams. Taiwan's professional-sports injury management still mostly relies on trainers' experience and subjective reporting, and for this kind of model-based approach to work, the prerequisite is stably accumulating training and injury data all along — without data, even the best model computes nothing.
Main features
- Injury-risk prediction model
- Connects existing GPS and fitness-test data
- Risk-trigger reason explanation
- Training-load adjustment suggestions
- Team overall health-status dashboard
Common uses
- Professional-team injury prevention
- Training-load adjustment decisions
- Player return-timing assessment
- Season-long health management
Key Features
- Injury-risk prediction model
- Connects existing GPS and fitness-test data
- Risk-trigger reason explanation
- Training-load adjustment suggestions
- Team overall health-status dashboard
Pros
- Shifts injury management from experience-based judgment to data-backed
- No need to replace existing wearable equipment
- Risk reasons are traceable, easing team discussion
Cons
- Prediction accuracy highly depends on data completeness
- Model suggestions still need medical-professional interpretation
- Enterprise-grade pricing, hard for grassroots teams to afford
Use Cases
- Professional-team injury prevention
- Training-load adjustment decisions
- Player return-timing assessment
- Season-long health management
Editor's Note
The cost of a key player being out for six weeks far exceeds any analytics software's annual fee. Pro teams do this math clearly; grassroots teams can't afford it — that's the real problem.
FAQ
Is the prediction accurate?
Injury is a multifactorial event, and any model can only give a probability, not an answer. In practice its value is 'reminding you to give a certain player a second look,' not making decisions for the medical team.
How long must data accumulate to be useful?
You need at least a season or more of training and injury records for the model to have enough baseline. The first few months of adoption are usually about filling in data, not getting suggestions.