## 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 …

## The Imminent Arrival of Reward Side Anti-Doping

For longer than I’d like to admit, I have been advising on Clean Protocol based out of Australia. I say longer than I’d like to admit because it has taken so damn long to get to the point were meaningful forward progress has finally started. In anticipation of finalizing some key pieces of technology I wanted …

## 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 …

## 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 …

## 2014 Giro d’Italia Climb Preview

The upcoming 2014 Giro d’Italia features 6 that are likely to play a role in shaping the final podium. As with previous Grand Tours, @ammattipyoraily is my go to source for the most consistent data. The process is simple: 1. Measure the climbs. 2. Time the finishing climbs. 3. Estimate normalized W/kg power outputs using …

## Video explainers of the veloclinic Mean Maximal Power Duration Models

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 …