10/08/2026
There was a time when I was brand new to data collection and processing. It didnāt take me long to realize one major fact:
Looking at a playerās wellness questionnaire data is impressively meaningless.
See, we all have our biases. We might intensify as a low-stress individual, or maybe someone that never quite gets a restful sleep. These biases are going to sway the way we answer something like āhowād you sleep on a scale of 1-10?ā.
Youāll see people input 9ās or 10ās almost every day. A ābadā sleep for them comes out as an 8/10. And yet, they might have the highest sleep score on the team, despite feeling under-slept and a little tired.
So if thereās no mathematical processing of the inputs you collect, you end up missing a lot of red flags. Or applying false red flags where issues do not exist.
This normalization method takes into account the playerās average and their standard deviation (how much does their score vary?) for each metric.
Of course, chronically low inputs are cause for question. There may very well be an underlying problem, or low hanging fruit for recovery/performance.
I think a good solution is to look at and display the normalized scores primarily, and then opt to look under the hood every once in a while at the specific inputs.
If youāre tuned in, you should notice a trend here. Data needs relevant context to be useful. This software was designed specifically to paint useful context around your data and provide insights on a platter.
If you think this software and these insights can help your organization, follow the link in my bio to learn more.
Stay tuned while I continue to drop clips breaking down key sport science principles š§Ŗš
