02/09/2026
THIS ATHLETE SHOULD BE FASTER.
Rugby player:
120.1kg bodyweight
50.3cm CMJ
40.6 W/kg
4.61s 30m
Look at those numbers independently and you might miss the most important thing.
Look at them as a relationship:
120.1KG ATHLETE ➡️ 50.3CM CMJ ✓✓ ➡️ 40.6 W/KG ? ➡️ 4.61S 30M ↓↓
Now it gets interesting.
Moving 120kg through a 50.3cm countermovement jump demonstrates significant physical capability.
Yet his 30m sprint is poor.
So the question ISN’T:
“How do we make him more powerful?”
It’s:
“Why isn’t this physical capability being expressed locomotively?”
That completely changes the programme.
Now we’re investigating the gap.
Is it acceleration?
Force orientation?
Projection?
Relative horizontal force?
Sprint mechanics?
Elastic qualities?
Body mass?
The ability to convert force at the velocities and contact times required during sprinting?
This is transfer-led programming.
For years, I made the mistake of looking at testing data in isolation.
Strength = good/bad.
Power = good/bad.
Speed = good/bad.
Then I started looking at the relationships:
CAPACITY ➡️ CONVERSION ➡️ EXPRESSION ➡️ TRANSFER
And once you see athlete data like this, you can’t unsee it.
You don’t necessarily need another 15 tests.
You need enough data to identify where the chain breaks.
Because testing isn’t about creating prettier dashboards or more green boxes.
It’s about making better coaching decisions.
One caveat with this athlete: I’d verify the W/kg calculation because it doesn’t sit completely comfortably alongside the 50.3cm CMJ.
Good data analysis means questioning the numbers too.
But assuming the jump is valid, the jump → sprint transfer gap is difficult to ignore.
If you’ve got athlete testing data and you’re not sure what it’s actually telling you…
Comment DATA and I’ll show you the process I’d use to start identifying the transfer gaps.
You might not need more data.
You might just need to look at the data you already have differently.