““I think it’s unfortunate,” Cookson said of the MPCC and UCI contrasts. “Let’s be clear. At the end of the day the UCI’s rules are the rules that go into the sport; the MPCC is a voluntary organization that has guidelines for its members. If those guidelines put any team or individual on conflict with the UCI’s rules, then it’s still the UCI’s rule that apply. So in the case of Astana, the MPCC rules forced them to miss the Tour of Beijing. That’s been referred to the disciplinary commission of the UCI, and they will make a judgment on the matter in due course. But missing a WorldTour event puts them in contravention of the regulations, and there are consequences for that.”
Read more at http://velonews.competitor.com/2014/10/news/cookson-serious-astana-issue-mpcc_349730#PikxyqqYxXUOh1F0.99”—again what tools are allowed by the UCI to enforce ethical standards by teams if anything that goes beyond UCI rules is a problem ?
What anti-doping tools does the UCI expect Astana to use?
Remember Cookson’s recent statement, “I think the MPCC is a good organisation, it does good work but it is confusing that some teams are operating under an additional set of rules,” Cookson said. “Really, there is only one set of anti-doping rules: the UCI’s, which are in line with the WADA code.” effectively calling for an end to any rules that go beyond WADA code ?
So if it is incorrect for Astana or any other team to refer to a more stringent code then why is it implied that the failure is Astana’s for not enforcing some inherently more stringent internal code ?
Hi Dr. Veloclinic. I started experiencing severe hamstring cramps last season, at the same time that I increased my average cadence to 95-100 rpm. Could it be that my hamstrings just don't like firing that frequently? If so, is this trainable? Or am I just exceeding my natural cadence and I'll have to dial it down a bit. Thanks!
the cause of cramps is still actively debated and your theory sounds as reasonable as a lot of what is out there. but if you’ve noted a worsening in performance with higher cadence in the form of cramps there is surely lower hanging fruit out there than trying to adapt to a technique that so far has you going in the wrong direction.
"Doping w/ EPO or blood transfusions is one way of boosting an already-high VO2 max—and it’s possible cheating may have contributed to the drop in the record, and could even be the secret that allows runners to approach sub-two in the future. But Radcliffe’s #s offer a reminder that such tactics aren’t necessary to achieve boundary-breaking performances: Her VO2 max was already exceptional when she was a teenager, and it stayed at a relatively constant level throughout her career"-@sweatscience
@Vaughters (alternate title let's just do the calculations) Let's Throttle Back on the Bullshit a Bit
“The first thing you’re going to establish is your ascension speed, how long it takes to gain a certain altitude. From that you can then calculate your Newton -metres, which is the force it takes to lift one kilogram, one metre vertically, in one second. One Newton metre equals one watt, so on that basis you can work out how many watts it would take to do a certain climb if you were riding in a vacuum. So far so scientific but then you get into the subjective stuff: what is the rolling resistance, air resistance, tailwind, headwind, temperature, the effect of drafting? This is all very difficult to do. For example, look at the difference in times on the Ventoux when it was resurfaced in 2004. I mean, you can say that we were all on EPO – which we were – but the times just nosedived by about a minute on average. And that was to do with the tarmac and the rolling resistance. So, sure, there’s a massive difference but how do you quantify that?”
Vaughters from http://www.fredericgrappe.com/wp-content/uploads/2014/09/model-power.pdf
"These may only account for a small margin of the final calculation but if you get a few of them wrong and it causes quite big mistakes. To get some of these variables nailed on, we’d have to go to a wind tunnel, get a guy in his climbing position with his jersey flapping, food in one pocket, a water bottle on his bike, and climbing at 21kph… That’s just one guy on one climb. We’ve never done that and probably never will."
Vaughters starts out correct and then seems to want to build Ventoux out of a bunch of mole hills.
Let’s take the most impressive power output from the 2014 Vuelta on Stage 18 and break it down by component:
The total nW/kg estimate is in green and the nW/kg needed to overcome wind resistance, gravity, and rolling resistance in blue, red, and yellow respectively.
Note how gravity (the red bar) dwarfs the other components accounting for over 80% of the total power estimate. Since gravity, is gravity, is gravity, regardless of the rider or the climb the majority of the estimate is going to be very solid with mostly just trivial error. Since gravity makes up so much of the estimate, you’ve really got to completely screw everything else up to get to the point that things get “nebulous.”
Take rolling resistance. In the estimate above it accounts for less than 5% of the total estimate. And while some roads may actually be washboard rough, or god forbid you get stuck on some mythical watt suck tires, think about what that does to the estimate… Yes, that is correct it makes you underestimate the actual power. On the flip side, there’s only very small fractions off efficiency left to be had in drive trains and tires. Mathematically, a small fraction of a small fraction is a very small fraction.
So realistically, wind resistance is really about the only place you can screw up. Fortunately, screwing up is potentially harder than it might look. Consider air density. There’s an equation for that so altitude and temperature are easily measured and accounted for. Drafting, again there’s an equation for that. Single file drafting has been modeled well enough to know that perfect single file drafting is good for a drop of about 25% of CdA. So be smart about it, don’t model false flat climbs and if there is still a big group together on a steeper climb they probably aren’t going fast enough for anybody to care much anyway. And even if you do get the single file drafting bit wrong you are talking about a 25% error on only 15% of the estimate or less than 4% if getting it completely wrong for the whole climb. All the others stuff, flapping jerseys, burrito stuffed pockets etc, like rolling resistance will all have you underestimating. Again there is only so much aero efficiency out there so in terms of overestimating power we are talking small fractions of a small fraction.
The last one to deal with wind. Yes on a straight barren climb wind can really screw things up. But even when it does, since wind speed is squared, a head wind will cause a greater underestimation than the overestimation caused by a tail wind of the same exact speed. So even in the worst case scenario of a straight barren climb there is a greater chance of under estimating than overestimating. Fortunately, climbs for the most part aren’t straight or barren. Irregularities in the ground, vegetation, vehicles, barriers, people make the ground rough. This roughness means that the wind at rider level is a good bit less then hitting that flag just a few feet higher. Similarly, wind does not chase riders back and forth up the switch backs. On a typical climb with a “perfect” tail wind, only about a third of the direction of the movement is going to be in the same direction as the wind cutting the effect down by two thirds. A perfect cross wind on the other hand, effectively means the rider is alternating from head to tail wind sections. Remembering the math above, the net effect is that a perfect cross wind will slow a rider down more than it helps them again resulting in an underestimate.
And then there’s this, look at more than one climb and at more than one rider.
Take home point: JV you started out well but then decided to leave the math out of a conversation about math; “Extrapolating from that is going to be useless.”
In discussing the JTL case some strongly disagreed with me on whether UKADA was ok to break the dehydration argument off into a separate issue dealt with by expert opinion the way they did versus needing to integrate it into a complete probabilistic one.
The issue now looks reversed with the Kreuziger case. His defense looks to be that his values never crossed thresholds and the UCI expert opinion on the abnormal pattern from the grand tours was rejected.
Again my take is that the data needs to be treated as a whole and the conclusion should be based on probability not opinion.
Regarding the power analysis of the Vuelta, would it be possible that the model was affected by the steepness of the climbs as well as their short lenght like you mentionned? the extremely slow speed we have seen could mean that very little energy is spent "fighting the wind" and more is spent going up, hence the apparent very high power being put out by riders. Thanks.
the power estimate model gets better the steeper the climb as drafting and CdA become less dominant sources of error. on the other hand the short length increases the error of timing and elevation change though to a lesser degree.
i don’t have enough data to know how well the DpVAM model handles very short climbs.
With the climbs done and Contador nearly sure of victory, its time to give some context to his winning effort.
Looking at the power duration plot its becomes evident just how well Contador controlled the race. On the stages were he lost time notice the minimal spread in power outputs of the GC contenders. Contrast that now to stage 16 and 20. On both of these stages the spread in the power outputs was large and Contador came out on top.
The pVAM for this race was is a bit difficult to interpret. The clear trend of highly positive values on short climbs and negative values on short climbs makes me wonder a bit if the model is simply not handling the short distances well.
To try to solve this question, consider Froomes 2013 TDF and 2014 Vuelta data.
From the power duration plot above, it looks like the short climbs from the Vuelta actually fall very nicely along the same trend from Froome’s 2013 TDF data. The 2 slow longer climbs from the Vuelta appear to be the outliers of the data set.
Taken together it is a reasonable generalization to say that aside from the 2 outliers, 2014 Contador and Froome were on par with 2014 Nibali and 2013 Froome. Like these performances, the 2014 Vuelta performances from Contador and Froome would likely have been frairly competitive with the 2002-2007 period of known doping.
Adding stage 14 and 15 into the analysis the power numbers reinforce the impression of a cautious race. Typically, the deeper that riders go trying to push the limits of their ability the more likely we are to see some erratic performances. With 5 finishing climbs completed, the estimates have a distinct lack of of this erraticness. Instead, the power estimates are falling incredibly neatly on a power duration curve with a large amount of clustering any given climb. This is not entirely surprising considering the definitive climb of the race (Ancares) does not come until stage 20. Waiting for stage 20 however, will surely leave some wondering what if.
As for the pVAM, the longer climbs according to the model have not been particularly impressive. Again, the short climbs are in a faded color as the model validity may be fairly question at these very short durations.
It seems CP models attempt to derive the CP value by extrapolating from a series of MMP values. The 'slope' of these MMP values can effect the derived CP. It is also suggested that some cyclists have a low CP but high W' or visa-versa. Could it be that CP value is actually 'constant' but the duration for which efforts can be sustained at the CP level is the variable? IE We should have a "CP Time"?
Yes there is clearly a Critical Time at play when it comes to fitting the Critical Power model. This issue is one of the major reasons for using the Veloclinic Plot.
From the power duration perspective, a more robust definition of Critical Power might be a threshold phenomena above which an identifiable W’ exists for a relatively broad range of power ie little penalty for intermittency within the Super Critical range.
For the explanation of the plot see: http://veloclinic.com/veloclinic-plot-w-cp-subtraction-plot/
For more on intermittency see: http://veloclinic.com/rethinking-intermittent-modelling/
With 3 finishing climbs done and analyzed the power duration curve is starting to take shape.
Overall, the performances have been more clustered than spread with the exception of Froome and Valverde falling off the pace on stage 9. Estimate wise, stage 11, the longest of the three was also the most pedestrian but road surface and tactics may have also been a larger than usual factor.
In terms of the pVAM model, only stage 11 is long enough to fall within the range that the model was intended for. I did leave the others stages in but I faded them out a bit as a reminder to not get to confident about the model result there.
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.