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 …
Author: Mike
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 …
Functional PCA of the Golden Cheetah Power Duration Data
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 …
“The Critical Power Model as a Potential Tool for Anti-doping”
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 …
Tour de France Performance Trends (2008-2015,2017)
With 10 years (almost as 2016 is missing) of Tour de France finishing climbs on the spread sheets, it seemed time to have a look back and see if there was an interesting trend. First to normalize the performance I used the Martin model assuming a Froome size rider of 67 kg and about 50% …
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 …
A Transverse Doping Probability Passport: A Conceptual Illustration
Athlete doping rarely happens in isolation. So why do ADAs try to tackle the problem that way? Consider this toy model of the social structure of a doping network. We may have individuals who may be Athletes, Trainers, Coaches, and Doctors. The individuals may instigate doping behavior, be reactive to doping behavior (dope if pressured, …
GoldenR Cheetah Script for Visualizing Delta W’balance By Interval
In the white is Skiba’s W’balance that you all know and love. I’ve combined this now with the interval discovery algorithm so that we can now visualize the change in W’bal by interval in green. ## R script will run on selection. ## ## GC.activity() ## GC.metrics(all=FALSE) ## ## Get the current ride or metrics …
GoldenR Cheetah Script For Interval Discovery
The Golden Cheetah crew has now integrated R into Golden Cheetah in the latest development build. This update instantly gave Golden Cheetah statistical and modelling super-powers to the point where I gave it the nickname GoldenR Cheetah. As an example, this script uses the change point package to auto discover intervals within a power file. …
Estimating the Probability of Doping as a Function of Power
A repeating theme heard in performance analysis discussions is that “Performance doesn’t prove doping.” Of course this is true because proof, as an absolute, doesn’t really exist. Instead, most real-world evidence driven judgments are based on probability and not “proof.” From this perspective, this post will illustrate the concept of using a mathematical model to …