When it comes to monitoring athlete health & well-being, it can feel like it rarely helps to even have a plan based off how often that plan must be upended. With a quality process for monitoring athlete fatigue and readiness though, this job becomes easier. In my current role, I meet with lots of teams that are trying to definitively say “this athlete is ready” or “we need to limit this athletes’ exposure”. The path to either statement is not always clear though.
Any one data point will not tell you how ready an athlete is. Multiple data points can get even more confusing; that is, without proper context. Below are a few ways to look at data to get a better reference for what the data is telling you:
Max Score (within a given time range)
This tells us what the athlete is capable of. When an athlete falls too far beyond their max (or minimum, depending on the variable) score, it’s good practice to ask more questions and dive deeper into why that might be. If testing is supposed to be “maximal,” fatigue, pain, soreness, psychological apprehension might all play a part in limiting what an athlete can currently do compared to what they are capable of when they are at 100% health.
Mean Score (within a given time range)
Depending on how often you take measurements, deviations from the mean scores can be quite telling. If you only test 2x per month on Nordics, and an athlete is now down 20% of their max force, it could feel normal to become alarmed. The mean tells us something different than the max though. The max tells us that at their VERY BEST, an athlete can perform in this way (whether that be in preseason, coming off a break, or the stars just aligned one day). The mean tells us that Athlete A typically performs like this. It pushes the abnormalities aside and focuses on what the athlete can do during a typical stretch of games/trainings. Whereas we might be alarmed to see a 20% decrease from their max score, if they typically test between -10% and -15% of their max, this is no longer alarming because of the added context.
Last Score
Lastly, comparing an athletes’ score on anything should always be compared to what they previously did. If there is a sharp increase or decrease, does this make sense given the amount of game-time vs training-time they’ve been exposed too? Oftentimes we can take an educated guess on why these scores deviate. It’s usually when we have a tough time explaining why the deviation occurred that we need to pay the most attention.
Below are some examples of various data types and the types of contexts you can add by looking at very simple calculations. This is not an exhaustive list but is meant as a general guide to how I personally look at various types of data in different date ranges. Keep in mind, this is how you frame someone’s CURRENT score and what to compare that CURRENT score to:
Peak Performance Platform 