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 …

This is the third post on applying functional principal component analysis to the open GC project power duration data. Part 2 explains the approach and the resulting model. The purpose of this post is now to make the model and data useful. The model itself outputs a score for each principal component. That score is hard to …

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 …

With 2,445 athlete seasons (inclusion criteria: at least 100 power files per season and at least PD data out past 2 hours), it makes sense to let the data speak without a predetermined narrative. One tool that works without a priori assumptions is principal component analysis. The basic idea is to start with the data …

Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Click the link for our paper on implementing the critical power model for doping detection published in …

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 …

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 …

DerivationPart 1 The Equations Part 2 Example Fits Also see the Derivation. Below are the equations since they are a little hard to see in the video. #2 secret exp top linear bottom fo <- y~ w1/x*(1-exp(-x/tau1))*((1-exp(-x/10))^alpha) + pow2/(1+x/tau2) rpowb <- nls2(fo, start=list(w1 = rw1, tau1 = rtau1b, pow2 = rpow2, tau2 = rtau2, alpha …