as pointed out before the WKO4 model appears to be made up of the stringing together of 4 models that based on the distribution of their residuals function like a pmax model, a CP1-10 model, a CP3-30 model, and something that slopes down to cross the measured curve at the 45-60 minute range. the overlay of the CP models and the WkO4 model is previously illustrated in this post http://veloclinic.tumblr.com/post/72305824574/for-educational-purposes-only-post-contains-only
Just a quick one note here as this Vuelta so far is just following the curve. For the top riders on the day, the performances normalize out in the 6.2-6.3 naW/kg. Valverde and Froome were off the pace a touch at around 6.1 W/kg.
At only about 13 minutes of climb time, Altos Cumbres Verdes is a bit short to use the pVAM model. But it is still somewhat illustrative to take a look at how the performance estimates compare to the 2008-2013 reference range.
The raw nW/kg estimates came in between 6.4-6.5 nW/kg. Since this was a relatively low altitude climb the power comes down to 6.3-6.4 naW/kg when normalized for altitude for sake of the historical comparison.
From this first climb it does look like the favorites are on good (especially considering the heat) but no necessarily shocking form. We may just get the Froome Contador battle hoped at the TDF as both Froome and Contador likely to ride into form as the race progresses. Look for Quintana to bounce back on stage 9 as altitude will be a bit more of a factor.
The Vuelta Climbs by the numbers (as usual thanks to @ammattipyoraily and note that some of the numbers may be adjusted prior to the climb as he hunts down the best available climb data):
Across the board the Vuelta climbs are steep, mostly in the 7-10% range (note that for the climbs with a flat finish at the top this portion of the climb has been excluded for the analysis). The outlier climb is Stage 14 La Camperona at 15%.
The climbs are also relatively short mostly in the range of 4-800 vertical meters of climbing. The longest climb on tap is Stage 20 Puerto Ancares with over 1100 meters of climb.
Altitude ranges from the a 500 meter finish on Stage 18 Monte Castrove to just under 2000 meter on Stage 9 Valdelinares.
In general short steep climbs should diminish the effects of drafting and tactics so flat out efforts will be expected on most climbs.
Although the pVAM/DpVAM metrics are probably not entirely suited for the sub 20 minute climbs I used it nonetheless to get the basic gist of times and power outputs that can be expected.
In the blue is the VAM predicted from the 2008-2013 post-biopassport data set and in red is the prediction from the 2002-2007 doped data set. According the model, VAM is positively related to the natural log of gradient and negatively related to altitude and length of the climb. With the extreme range of climbs the model gives some fairly extreme predictions.
From the pVAM prediction its possible to project ascent times for the climbs. As you can see most of the climbs are below 20 minutes so take these predictions with grain of salt.
Lastly, with predicted times in hand the normalized power output expected can be calculated. Note the huge range of predictions here as short low altitude climbs will likely see big numbers well over 6 nW/kg. The longer or higher altitude climbs on Stages 9 and 20 will likely be in the 5.8-5.9 nW/kg range.
Re: MCV. You missed the point here. Dehydration (from alcohol) might cause changes in MCV (hyperosmolarity of plasma when fluid is lost, thus smaller red cells). EPO influence on MCV not discussed in the case.
right but follow along here for a bit
the heart of the case is the probability of doping given the ABP values estimated with Bayesian statistics.
for simplicity and because the actual ABP software is a bit of a black box consider walking through the argument in terms of Bayes theorem.
the probability of doping given the ABP results equals the prior probability of doping times the probability of the ABP results given doping divided by the probability of the ABP results.
P(doping/ABP) = P(doping)*P(ABP/doping) / P(ABP)
UKAD basically argued that the probability of doping was astronomically high because the probability of the results were astronomically low
the issue is that if you argue that MCV is good enough to rule out dehydration (which its not) then you can argue that its certainly good enough to rule out high dose EPO (which again it is not) AND since the results can not be explained by micro dose EPO then EPO can be ruled out all together.
that last part is a MAJOR issue for UKAD had JTL’s side been savvy enough to pick up on it.
since the entire argument revolves around a Bayesian estimate of probability as soon as UKAD introduced evidence of a normal MCV and also insisted on no dehydration then this evidence must be used to update the Bayesian estimate. (I understand this is not how the legal system works as you seem to get to coast through if your opponent is weak but it is how stats/science/reality works)
so with this evidence (if its good enough for UKAD then its good enough for this blog for the sake of discussion) we can populate the equation with some numbers to see how ruling out EPO changes the probability.
UKAD calculated P(doping/ABP) at 99.9999%
a conservative estimate of the prior probability of doping p(doping) might be 15%
plugging these values in
.999999 = .15*P(ABP/doping) / P(ABP)
6.6666 = P(ABP/doping) / P(ABP)
in terms of oxygen vector doping EPO likely accounts for the majority of doping and we’ll use 65% here as a reasonable example
taking EPO out of the equation means that your prior probability of doping is no longer 15% but now 5.25%
or solving for the probability of doping given the values
P(doping/ABP) = .0525 * 6.6666
P(doping/ABP) = .350
probability of JTL doping = 35%
JTL not guilty
the values were more likely a lab or physiological anomaly
now this is a bit of an oversimplification but had JTL’s side picked up on the above then this case would likely not have played out as such a clear slam dunk decision
on the other hand
if UKAD had said fine there could be dehydration they could have then gone on to point out that the MCV must have come down from an elevated value making high dose EPO all the more likely given the extremely low retic and a still high for even a dehydrated state in season Hgb concentration
for those wondering what MCV has to do with EPO
when EPO stimulates red cell production the new cells tend to be larger in size so that the mean corpuscular volume MCV tends to get elevated. after coming off of EPO the MCV drifts back down as the cells age. MCV has in the past been proposed as an indirect marker for EPO use. MCV can also be affected by blood storage conditions so could potentially have some utility in picking up transfusions as well.
One of the points that the UKAD side used was that a normal MCV (mean corpuscular volume) was evidence against the presence of severe dehydration.
The issue is that taking enough EPO to get your Hgb to 17.9 should also have increased the MCV (young red blood cells are larger and shrink as they age and to still have a Hgb of 17.9 at a point in time when the MCV has normalized would imply that the peak Hgb would have been high enough to even impress Bjarne Riis). So it would be expected that had the mechanism been EPO use then a potentially normal MCV could be expected in a dehydrated state as the MCV was reduced from a drug elevated level.
Alternatively, to make the conclusion they did regarding dehydration, they would need to take the stance that the method was more likely blood transfusion than high dose EPO and that the amount of transfusion was several units. Remember that part of the reason high dose EPO raises Hgb concentration is due to a diuretic effect that does not occur with transfusion. So to get a 90s era Hgb would take more relative doping with blood transfusion than EPO.
Overall, dehydration seems plausible on top of a recent cycle of high dose EPO.
“In fact, as much as 80 percent of healthcare data is unstructured, according to a recent Institute for Health Technology Transformation (iHT2) report. Matters are made worse when you couple in the fact that 50 percent or more of a patient’s health information typically not captured and available for view in an electronic format.”—medicine fights tooth and nail to stay in the dark ages it seems
Hi veloclinic. I was looking at my CP curve in Golden Cheetah this morning and I noticed that the widths of my power zones were not what I'd expect them to be. Specifically, zone 7 is quite wide, which is what you'd expect given the scale of the time axis. But then zone 6 is narrower than zone 5. So I wondered, does the relative width of my power zones tell me anything about how to tailor my training? Thanks!
it might just be the log scale causing some visual gremlins
on the other hand
after we are done with performance modelling study
we will be rewriting some of the power zone concepts
in terms of taking an applied aproach
ie what can actually be determined from field data
versus the current aproach
of trying to shoe horn the data into a priori physiological guruistic constructs
Is it such a bad thing that riders with "dodgy" passports (but no case opened against them) appear to be having difficulty getting a contract? If the UCI are gonna leave such a gaping hole in the passport that Armstrong and Horner drove their figures through, is it so bad that the teams get to make their own minds up looking at prospective riders' values?
this would be fine if teams had altruism to the sport as a priority
“We are the ones making the money and carrying the liability,” Patterson said. “The others don’t make any money. Nobody wants to watch them on TV. I don’t accept the argument that you have to have total socialism.”—
“I’ll reveal something important. Last year there was a rider, quite a famous rider, who we were considering for the team but we didn’t sign him because our experts and our doctors looked at his Biological Passport data and thought there was something wrong with it, with irregularities in there. But one of the other major teams in the peloton did sign him and the UCI was okay with it and now this rider is racing.”—
JV says the same thing happened with Thomas Dekker who was later banned based on positive EPO tests rather than his obviously doped biopassport.
Hopefully these examples are the exception rather than the rule. However, the opacity of the current system prevents anyone from actually knowing.
“Both, paradoxically, and also frustratingly because I know people want a definitive answer.”—Ross Tucker, highlighting that the biggest problem with rational analyses aren’t the methods, but the audience. (via cyclocosm)
Quick point on performance monitoring as a formal anti-doping method
the utility is not to create a second performance passport parallel to the bio-passport for sanctioning or to flag riders for more testing.
the point is to improve the specificity and sensitivity of the current bio-passport by integrating it into the biopassport
this could be done through a combined metric such as a power-OFF score
by using it as a prior (like altitude and gender) to allow tighter thresholds.
the reason you need to do this is that doping is not the only thing that changes blood parameters.
blood parameters are affected by hydration, training load, and illness. as such, the thresholds have to be set wide enough so that these other physiological processes don’t trip the passport.
for example a retic dropping from 1.0 to 0.6 in the middle of a GT could be a couple units of blood transfusion or a viral suppression.
since one would make the performance go up while the other down, power could be used to adjust the thresholds accordingly so that the under-performing rider is not flagged while the other matching their power duration profile.
one other quick point is that like blood, power is an indirect measure of doping, training, and health status. indirect markers will fundamentally have the same major pros and cons that all indirect markers do. arguing for one and against the other is a debate strategy best left to politicians. if you want to argue against power, please be consistent and argue against blood as well.
nice analysis and gets into the VO2 demands of the performances we saw.
the one note I would make is that it needs to be considered at what altitude Peraud’s VO2 max was measured. If it was measured at sea level then the full 85 ml/min/kg will not be available so the actual percent of available VO2max held will be greater than 85%.
The biggest drawback I'm seeing with your analysis is my 'most likely to have blood that glows' list is mainly populated by key domestics who swing off the front mid-climb. Is there anyway to meaningfully compare their performances? Thanks.
I don’t think we could get enough reliable information on guys who swing off prior to the finish
Descriptive analysis of the 2014 Tour de France with 2013 Comparisons
The final 2014 Tour de France podium isn’t quite settled but its clear that Nibali will go on to win and that Pinot, Peraud, or Valverde will round out steps 2 and 3.
In estimated W/kg normalized to a standard weight, CdA, draft and altitude the power range for the podium was from 6.2 - 6.3 naW/kg for shorter 17 minute efforts and down to 5.6 - 5.8 naW/kg for longer 50 minute efforts.
Nibali was unchallenged in any meaningful way. One of the questions though is whether this was due to Froome and Contador crashing, Quintana not contesting, and Rodriguez still recovering, or was Nibali superman. .
Pulling in last years data things get a bit busy.
Once color coded by year it becomes clear that other than Quintana and Rodriguez poor AX3 performances the 2013 podium was very likely superior to 2014.
Comparing just Nibali to 2013 however, it is clear that his performance was very likely on par with the best from last year.
Head to head with Froome, the level is effectively the same and we may have missed out a hell of a race.
In terms of whether or not the performances are concerning, it is a mixed picture.
No it is not concerning that Nibali outclassed the 2014 contenders. They are simply not on the level that we have seen from other top riders recently.
On the other hand, Nibali, like the 2013 podium, would likely have been competitive when compared to the 2002-2007 podium baseline as illustrated by the DpVAM analysis. The remainder of this years podium would not.
The question then comes down to progress. In 6-7 years, have riders been able to make performances gains that were once universally held as unfathomable from a clean rider?