Power Duration Modelling

Do Cyclists Cluster into Phenotypes ?

This post is a quick one asking the question whether the GC Open Data Project cyclists cluster into distinct phenotypes? I used the 2nd and 3rd principal component scores from the 3 component model (see Part 2 and Part 3). The short answer is that it doesn’t look that way, to me anyway. I don’t see any …

Power Duration Modelling

Part 2: Functional Principal Component Analysis of the Golden Cheetah Power Duration Data

After the first post on FPCA of the Golden Cheetah Open Data Dan Connelly (@djconnel) pointed out that since the FPCA uses basis functions the fit would improve after taking the log of power. Going back through the initial attempt there was heteroskedasticity in the residuals with errors increasing at long durations. Sure enough after taking the …

Power Duration Modelling

Rethinking Intermittent Modelling

Previous attempts have have focused on either trying to normalize power to Critical Power/Functional Threshold Power: nP = %CP^4 or vessel approaches of trying to dynamically track W’/FRC balance W’b = W’ – (P-CP)*t + (CP-P)*t*(f[reconstitution]). The normalized power approach will typically work reasonably well for power outputs close to CP/FTP. Outside a fairly narrow …

Power Duration Modelling

Veloclinic Plot (W’ plot)

This post introduces a new way of plotting performance to better visualize W’ and Critical Power (CP). I haven’t seen anyone plot performance this way, so I’m going to take the liberty to name it the Veloclinic plot. In descriptive terms, it would be accurate to call it a W’ plot or a Critical Power …