Yes, sort of, and no depending on several factors:
- The model has to be good
- The model has to have high quality, recent, and relevant data
- The individual can’t vary significantly from the data set that the model was trained on
- The quality of the prescription will be directly related to the quality of the data points near the specific energy system that you are looking to target
For example, here is what different power durations looks like from a variability perspective when compared to FTP
You’ll notice a massive variation in the short duration part of the graph that gets increasingly less so when out towards 3600s. This variation can also change within the course of a training cycle (i.e. during one cycle my 20min:FTP ratio may fluctuate inside the range of 92-97% depending on the type of work I’m doing).
It’s difficult to predict longer power durations from short power durations (and vice versa) and I’m sure you’ve found that a 3-5% variation in power depending on the zone can mean the difference between getting through a workout or failing it.
I’m not anti-models; quite the opposite. I’ve spent significant time working with Xert, Golden Cheetah, WKO4, TrainerRoad, and the Coggan/Friel/CTS tests in my own personal trial and error to see how they all match up. In my own experience, whenever I’ve relied on the models over a consistent set of benchmark testing, my training suffered and I wasn’t able to complete workouts where I should have and I got slower. When I use consistent testing and use that to set zones, I get faster.