A Graphical Representation of Training Levels

OK - a break from talking about my rehab progress and back to matters concerning training and racing with a power meter.

Above is a graphical representation of training levels and associated physiological adaptations to help explain the relativities between using Coggan training levels (based on FTP - Functional Threshold Power) and Ric Stern Training zones (based on MAP - Maximal Aerobic Power).

Click on the image to see a larger hi-res version.

The left hand side of the graphic shows the training levels/zones and how they relate to each other. I also show what is commonly referred to as the "Sweet Spot" for training.

I always took Sweet Spot to be upper-L2 to L4 (Coggan) although I put a limit in the graphic at FTP since the idea of sweet spot training is that it's good trade off between duration and intensity that gives Chronic Training Load (CTL/fitness) a great boost in the "bang for buck" sense. Once you go over FTP, the sustainable volume starts to decline somewhat.
The nice thing with RST zones is that there are three pre-defined (overlapping) levels that fall neatly within the sweet spot.
At the end of the day, it's the nature of the training and not what we call it that matters. But it sure helps to have a common language. I thought this might help translate between two commonly used training levels/zones.
It should be noted that the FTP:MAP ratio can vary a bit and I have taken a typical mid-point of 75% (typically 72%-77%). It wouldn't change the graphic much either way since levels/zones sort of blend into one another.

The right hand side of the graphic is my interpretation based on the Coggan table showing adaptations and number of ticks per level indicating impact of training at that level.

All I did was to represent that in shades of grey, with the darker areas indicating the relative power level where the adaptation most strongly occurs and fading out to white for power levels with little to no impact. In the same way the training zone/level colours blend into each other to show a continuum, it was an attempt to graphically demonstrate the non-discrete nature of the training impacts.

I also re-ordered the adaptations to show those primarily occuring from lower to higher relative power outputs.