When "Sport Science" Goes Wrong A few years back, I tried an...
Pat Basil@pbasilstrength
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Oct 03, 2025
~2 min read
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1
When "Sport Science" Goes Wrong
A few years back, I tried an experiment with our men's basketball team.
I adjusted in-season training based on daily vertical jump tests.
The idea was simple:
If a player's jump performance was down, I'd adjust their workout accordingly.
Little did I know, this strategy would teach me a crucial lesson in balancing data-driven decisions with the actual needs of my players.
Here's the breakdown:
- If a player was within 5% of their baseline jump, business as usual.
- A 5-10% drop meant a 30% volume reduction
- Anything beyond 10% led to cutting top sets and a 30-50% volume decrease.
Seemed logical, right?
It ended up backfiring.
High-minute players saw continuous volume cuts, leading some to actually get weaker as the season progressed.
I realized we did too much "load management" to a fault.
The key lesson?
Sometimes, you have to ignore the data and give your players what they need.
Even if the numbers suggest fatigue, there comes a point where they need ample load to maintain strength during the season.
The experiment wasn't a failure. It taught me to balance data-driven decisions with the human element of coaching.
Now I know what you're thinking - did the players lost trust in me?
No, the opposite happened.
They appreciated that I had their best interests at heart, acknowledged the learning process, and how it adapted our training.
We used it to improve our whole operation together.
And went on the win a conference championship not long after.
The takeaway?
Adjust training when necessary, but don't let data overshadow the human aspect of coaching.
Remember, you coach the team, not the computer.
A few years back, I tried an experiment with our men's basketball team.
I adjusted in-season training based on daily vertical jump tests.
The idea was simple:
If a player's jump performance was down, I'd adjust their workout accordingly.
Little did I know, this strategy would teach me a crucial lesson in balancing data-driven decisions with the actual needs of my players.
Here's the breakdown:
- If a player was within 5% of their baseline jump, business as usual.
- A 5-10% drop meant a 30% volume reduction
- Anything beyond 10% led to cutting top sets and a 30-50% volume decrease.
Seemed logical, right?
It ended up backfiring.
High-minute players saw continuous volume cuts, leading some to actually get weaker as the season progressed.
I realized we did too much "load management" to a fault.
The key lesson?
Sometimes, you have to ignore the data and give your players what they need.
Even if the numbers suggest fatigue, there comes a point where they need ample load to maintain strength during the season.
The experiment wasn't a failure. It taught me to balance data-driven decisions with the human element of coaching.
Now I know what you're thinking - did the players lost trust in me?
No, the opposite happened.
They appreciated that I had their best interests at heart, acknowledged the learning process, and how it adapted our training.
We used it to improve our whole operation together.
And went on the win a conference championship not long after.
The takeaway?
Adjust training when necessary, but don't let data overshadow the human aspect of coaching.
Remember, you coach the team, not the computer.
2
Coaches!
Need help with your training program design?
I have a Large Group Training Guide for you that explains how to structure your programs to flow seamlessly in your space.
Want it for free?
Click this link below
patbasil.com/get/
Need help with your training program design?
I have a Large Group Training Guide for you that explains how to structure your programs to flow seamlessly in your space.
Want it for free?
Click this link below
patbasil.com/get/