Sparta Science’s Injury Risk is a machine-learning derived value assigned to an individual based on the results of their most recent Jump Scan leveraging our 12+ years of aggregate data.
Sparta's Injury Risk is a representation of an individual’s relative risk of suffering a musculoskeletal injury compared to their peers.
How does the scale work?
Injury Risk is a categorical metric, with 5 unique values of risk on a scale: Low, Below Average, Average, Above Average, and High. As Injury Risk ranges from one value to another (eg Low to Below Avg), the relative change or increase in injury risk can be interpreted as approximately 20%. Average risk can be thought of as average or typical risk for our database, with the two values on either side being interpreted as risk greater than (Above Avg, High) or less than (Below Avg, Low) average.
How do we know this?
Sparta has been consistently collecting ground reaction force data from numerous assessments for over a decade from 80k+ unique individuals and a variety of populations. Along with that data, we’ve been collecting injury outcome metrics. With this depth of data, Sparta has dynamically built finely-tuned Machine Learning models into our product to analyze the relative likelihood of an individual’s injury risk with amazing accuracy. Our Force Plate Machine Learning (FPML) engine instantly analyzes each individual’s Movement Signature and compares this data to our historical data of force, injury, exposure, and assigned sex to assign an Injury Risk value for each individual.
What goes into determining Injury Risk
The current factors that contribute to the model are listed below. These factors will evolve and grow over time to produce more accurate models for a variety of populations ranging from sport athletes to tactical athletes to industrial athletes.
- Assigned Sex at Birth
- Load (Average Eccentric Rate of Force)
- Explode (Average Relative Concentric Force)
- Drive (Relative Concentric Impulse)
Future injury-specific (eg ACL) and population-specific (eg Basic Military Trainee) models are currently in development, though sufficient data needs to be shared/aggregated to enable to development of these models.
The current Injury Risk model is a general Human-based model (aggregating multiple populations/injuries) which has shown predictive ability and a significant level of predictive accuracy.
Retrospective analyses of injury prediction accuracy are available for customers to encourage transparency and improvements in modeling outcomes.
In addition to the development of new injury prediction models, our general model will continue to evolve and improve over time.
Near term and future additions
- Additional biomechanical metrics from Jump Scan
- Data-driven (eg machine learning) metrics from Jump Scan
- Biomechanical/data-driven metrics from Balance Scan
- Additional individual characteristics (eg Age, Height, Sport, MOS, etc)
- Previous injury history
- Additional (continuous stream) outcome data (injury, performance)