Even if a single team or organization has enough volumes of data (GPS, Accelerometery, HRV, force plate assessments), in order to make meaning out of this data we must have outcome metrics like injuries. Hopefully a single organization never suffers enough injuries to build their own injury risk models, but this is where the database can help! Having a depth of frequent, reliable, longitudinal data along with outcome metrics like injuries allows you to begin the pursuit of predictability.
The power of a database is truly demonstrated when the same standardized assessment is performed frequently across dozens of organizations with athletes of all shapes and sizes. The most important thing this allows us to do is identify “norms”. When you go to the doctor to get blood work done, the results are interpreted based on where the patient’s numbers lie compared to the gender norms. Is the white blood cell count high or low? What are the inherent risks with a low white blood cell count? What is the ideal range? Without this context, the tests don’t mean much.
For example: Running Back “A” has a concentric impulse of 5.39724N*s/kg. To most people, this number doesn’t mean anything. When you have context into norms, you can see this would be considered normal for a running back. But just because he is normal for his population, that doesn’t mean it is ideal. When we compare him to the athletic population he is in an extremely low range and we can see that he has increased odds of suffering a muscular strain. That meaningful insight only comes to light when he is compared to all of his athletic peers, not just people that play his sport or position.
Instead of comparing running backs to running backs, we analyze movement in context to the entire male or female athletic population. We get asked a lot why we haven’t created separate databases for each sport. It turns out much more meaningful findings are possible when utilizing only gender norms. Similar thoughts have occurred in creating norms for blood work. Should we use separate norms based on ethnicity? Age? Just like what they discovered in the blood work results, we found there is more similarity between positions across sports (Offensive Lineman and Front Row in rugby or a Catcher) than within sports (Offensive Lineman to Quarterback). Simply gender has proven the most reliable and valid population for creating actionable information.
While ethnicity, sport, age isn’t valuable separations to the database, the designation of “athlete” is. Our database only includes High School, College, and Professional athletes. Research done on speed improvements in untrained sedentary individuals with a mean age of 34.8 isn’t meaningful to the athletic population, so we exclude general population when analyzing injury risk and performance metrics. To create a database large enough and deep enough with an athlete specific population, data needs to be anonymized and aggregated across multiple organizations.
Join the thousands of individuals already utilizing the Sparta Platform to drive favorable results throughout their organizations. Request a demo to speak with a solutions specialist about how your organization can benefit from the actionable insights of our robust database.