WKO5 Power Insights.
Understanding and Utilising the PDC with Error Corrections in WKO5
(Lesson One)
To make the most out of WKO's features, it's essential to maintain a clean model. One of the key charts I use to achieve this is the Power Duration Curve (PDC) with error corrections. This model leverages all power data to generate a PDC, which provides the following metrics, Pmax, FRC, and mFTP. These metrics are similar to sprint capacity, W prime, and critical power.
First and foremost, it's crucial to examine the fit of the model. How well do the Power Duration Curve and the Mean Max Power (MMP) curves align? In the above image, you can see that they generally fit well, though there are some odd areas. What I appreciate most about the error corrections feature is the mathematical insight it provides through metrics like the Standard Error of Estimate (SEE) and Coefficient of Variation (CV).
Personally, I consider anything under 2% CV to be a clean fit. If the CV exceeds 2%, it indicates that I need to gather new data or conduct some additional testing. Typically, if the error exceeds 10%, the PDC model and all associated metrics vanish, signaling a need for a thorough investigation into power spikes.
Another key aspect of my analysis is the Gap Analysis. I developed this expression to measure the wattage gap between the PDC and the MMP curve. This helps ensure that the model accurately reflects the cyclist's performance, plus it enhances my work flow.
For both new and current cyclists, I use this chart to confirm a clean fit. In my next article, I will detail my baseline testing protocol and my ongoing testing methods.
If you're interested in the Gap Analysis Expression and how to set up this channel, the expression and settings are provided below.
Personally, I consider anything under 2% CV to be a clean fit. If the CV exceeds 2%, it indicates that I need to gather new data or conduct some additional testing. Typically, if the error exceeds 10%, the PDC model and all associated metrics vanish, signaling a need for a thorough investigation into power spikes.
Another key aspect of my analysis is the Gap Analysis. I developed this expression to measure the wattage gap between the PDC and the MMP curve. This helps ensure that the model accurately reflects the cyclist's performance, plus it enhances my work flow.
For both new and current cyclists, I use this chart to confirm a clean fit. In my next article, I will detail my baseline testing protocol and my ongoing testing methods.
If you're interested in the Gap Analysis Expression and how to set up this channel, the expression and settings are provided below.
Copy and paste this expression
athleterange(today-89,today,meanmax(bikepower)-pdcurve(meanmax(bikepower)))
Thanks for taking the time to read. This was a very basic lesson, but a very important one.
Jody
athleterange(today-89,today,meanmax(bikepower)-pdcurve(meanmax(bikepower)))
Thanks for taking the time to read. This was a very basic lesson, but a very important one.
Jody
