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 …

Anti-Doping

The small volume blood transfusion study

This study Time Trial Performance Is Sensitive to Low-Volume Autologous Blood Transfusion. by Beldjer et al has drawn some attention by a shocking finding of “ABT of only ~135 ml of RBCs is sufficient to increase mean power in a 650 kcal cycling time trial by ~5% in highly trained men.” But you have to dive in …

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 …

Anti-Doping

A Bayesian approach to boost individual anti-doping classification accuracy by transverse monitoring of the athlete network

Purpose: To demonstrate a Bayesian approach utilizing the athlete network to boost the positive predictive value of individual doping classifications. Study Design and Methods:  Five data-sets of 10,000 individuals and their networks were simulated and statistically analyzed in R. Background prevalence (BP) of 10%, 20%, 30%, 40%, and 50% was chosen to cover the range …