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% …
Author: Mike
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 …
Developing a test for match fixing, an illustrative example
Match fixing may not be the first topic that springs to mind when thinking about the problem of doping and what to do about it. But it is a useful one. It forces the discussion to step well outside of the usual box. The reason of course is that nobody expects match fixing to be …
Invitation to Clean Athletes competing in the 2014 Ironman World Championship
Invitation to Clean Athletes from Clean Protocol.
Athlete Biological Passport For Level Change Detection
One of the weaknesses of the current Athlete Biological Passport software seems to be an over-emphasis on outlier detection versus level change detection. To illustrate this point consider the Lance Armstrong case: According to the UCI, Armstrongs blood data never triggered a flag on the software. The software screen was non-positive despite the obvious …
Comparing Performances Across Grand Tours
One of the fun parts about modelling climbing performances in Grand Tours is the ability to make historical and virtual head to head comparisons. In the figure above the grey dots are the average performance of the top 3 finishers from each Grand Tour for each finishing climb from 2008 – 2013. Overlayed on this …