All Things in Moderation. Except Science.

This is my eighth year of blogging. It's a personal thing for me. I enjoy writing about training and racing with power. It's just stuff. Pub chat fodder for those four sad guys that talk about power meters in pubs. You know who you are. It's OK to come out and admit it, there's nothing wrong with it.

Occasionally I post an item that seems to be actually helpful to people, such as items on performance testing or indoor training. By posting I learn things and that's enjoyable. Hopefully others learn too. And I make mistakes. It happens.

I think I've held pretty well to the purpose as stated in my very first post.

I also used the blog to help me through difficult times, as longer term followers would be aware through sharing some of my experiences of getting back to racing after my 2007 accident and amputation. Where possible I tied the two themes together.

It's not a commercial blog and the subject matter is reasonably esoteric, and the following is modest with views numbering in the hundreds of thousands, not millions.

Moderation

I also know that public online interaction forums require moderation. Again it's mostly to keep spammers at bay, but occasionally some stretch the bounds of reasonableness and need to be reeled in or reminded of some basic netiquette. Troll watch duties as well. Sometimes you forget to check the moderation queue for comment that require moderation and they get posted a bit late.

I am a forum moderator myself, at the wattage forum on Google Groups which grew from modest beginnings in 2001 and was originally hosted on Topica. Want to pull apart anything to do with power and cycling? That's the place to do it. The archives are a gold mine of information, and the early days had a high signal to noise ratio. Only very rarely does moderation occur there, it's mostly self policed and the 10,000+ crowd are generally technically or scientifically oriented, so bullshit generally gets called out early and dealt with.

or Censorship?

But what I've never understood is why some forums or blogs dedicated to performance and/or training and racing would want to prevent actual relevant science or scientific discussion from being published/referred to.

I first encountered this a few years ago when I was banned by a triathlon forum owner from posting to the Slowtwitch forum. My crime? I wanted to know why they were persistently censoring the publication of a link to new published scientific research related to pedalling a bike. They didn't like the subject matter, nor my criticism of such censorship, so I was shown the electronic door and all evidence of the matter wiped. I was not offensive, I did not abuse, swear or curse at others, I did not break published forum rules. OK, it's their privately owned business, they can do what they like and they are large so what does it matter?

But it made me think. If you censor science, or discussion of science, is it really a place worth visiting? Well obviously lots think so, because such things are but minor ripples in the ocean of posts about saddle height, cadence, new bike frames, paleo diets, and the inevitable sarcastic pseudonymous responses.

So hooray for science. Perhaps.

Fast forward to a blog followed by many who, like me, enjoy their science in accessible form - the guys at The Science of Sport blog. I've followed it for a number of years, pretty much since it began in 2007. They dissect performance matters from a range of sports, in particular running, and introduce various topics for discussion. All good stuff and I think they do a good job of it.

Eventually they started writing about cycling performance like this one talking about the 2008 TdF and the all pervasive issue of doping. As they began to inform themselves on such issues in the cycling world, they also joined the bandwagon of estimating power from climbing speeds / VAM, and what we can learn from such information. They also began to suggest what is and is not physiologically plausible sans doping.

In the early days of such posts, I helpfully explained to them via the blog comments one of the difficulties in using climbing speed to estimate power, i.e. the large unknown factor of wind, and they got that message loud and clear. They even referenced my comments in subsequent posts. And they have usually referenced the issue of wind confounding such estimates whenever this topic comes up.

In general they have also tried to steer people away from the folly of using individual data points on climbing speed (or power estimations from them) as some means to infer doping status, but rather to consider the longer term trends. All good stuff.

Wham bam, thank you pVAM

Lately though they have been publishing a lot of information derived by the pVAM methodologies, which I am less enamoured with for a couple of reasons.

Firstly, whether intentional or not, it encourages the invalid use of such information as a "dopeometer" and secondly, I'm unconvinced they have sufficiently critically assessed the validity of the pVAM and dpVAM model. I think this is bordering on a desire for publicity than on good science. Not nearly to the extent that Antoine Vayer's occasionally dodgy maths does, but are they pushing the "Publish" button a little too quickly at times?

I get that their blog is designed to provoke discussion about sports science matters, make things accessible and to make people think more critically about stuff, and that's a good thing - but when you represent yourselves as "The Science of Sport" and present methodologies or models under that masthead that have not been peer reviewed or are based on known/published science, or without having done critical assessment against existing models, well I just think more caution is warranted before lending an overweighted amount of credibility to such things. Perhaps greater emphasis is needed on making it clear what is opinion and what is actually science.

I could of course be completely wrong and talking out of my arse. Happy to stand corrected. Most of us out here is the interwebs are not well versed in understanding the credibility differential of such material published by scientists under a heading of "The Science of ...".

It's difficult for me to strongly criticise since I'm not a scientist and not well versed in such peer review, so I just occasionally chip in with my amateur 20 cents worth and hopefully it generates some thoughts, at least in a pub chat kinda way. At the least I have a personal sense of contributing in some way, however insignificant that contribution may actually be. Which is what I did four days ago when I pointed out an error in a statement they made about the pVAM method.

This is what was written on their post:
"The error in the wind component is much larger, which has implications for the assumptions of cdA (drag co-efficients). In the model, however, the relative contribution of the wind during climbing is small, and so the total error is actually not too bad."

I then pointed out the obvious (to me anyway) physics mistake by posting this to the comments:

"The error in CdA isn't the issue, but the wind velocity most definitely is. Ignore that and you may as well throw darts at a W/kg board. That interpretation suggesting the error introduced by wind is, frankly, nonsense.

Even modestly different wind conditions for the same VAM can see estimates of power over a 1W/kg error range. The wind for the Armstrong/Pantani ascent was quite different to this year's ascent."

Perhaps my tone wasn't ideal, but nonsense is nonsense, right? And scientists generally have pretty thick skin.

For anyone wondering what I'm on about, when estimating power from steep climbing speed, if your assumption of the rider's coefficient of drag is (CdA) is wrong, well it doesn't generate a really big error in the estimate of power. It's not "sensitive" to that particular assumption. But it is most definitely sensitive to the wind velocity assumption. Get that wrong and the numbers can easily be wrong, and by a large margin.

Comment MIA

Then yesterday another SoS post about Alpe D'Heuz climbs and the pVAM and dpVAM methods appeared, so I thought I would post a comment with a link to djconnel's cool blog post which introduces a critical appraisal of the pVAM model, from both a physics and physiological basis, by comparing it with actual published and well established science models. I also provided links to a couple of charts about ADH climbing times that were easier to read than the ones they posted up.

As scientists, you think they might be interested in discussing the limitations of a model they are presenting as highly credible, or consider how it might be improved or under what circumstances we need to be very careful in using it.

Well for starters, I suggest using actual physics when estimating power from climbing speed, and also checking how the model stacks up with established physiological models.

Except my comments to them are now being moderated and have not appeared.

As Robert Chung would say, Hmmm.

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The Elusive Dopeometer

Readers of this blog no doubt have seen a number of examples I give about the (in)accuracy of estimating power from climbing speed, and in particular the confounding impact of wind on such estimates. Any climb with wind is going to be subject to error, and it doesn't take much wind at all to introduce quite a sizeable error.

There was my 2010 post about Alpe D'Huez ascent times, timely given the dual ascent this year, and my item yesterday about the significant error introduced by that great unknown, the wind.

I consider such W/kg estimates to be fine for a bit of pub chat fodder, but as a serious means to detect doping, really? I'll get back to this in a bit.

What has been more amusing than climb power estimates was more twitter/blogger/forum sphere musings on the power differential estimates from the Stage 11 individual time trial. Seriously. People are actually thinking they can reliably estimate the difference in power output of riders based solely on their time or speed in an individual time trial.

What complete and utter nonsense.

Aside from that affront to physics by Gazzetta dello Sport's Claudio Ghisalberti, there was also a post by the somewhat infamous Dr Ferrari about speed differentials from the same ITT and what that meant for power differences. Now Ferrari did say that this assumes all riders have the same "aerodynamic efficiency" as he calls it, which he then points out they don't.

So, given that they don't, why would you then proceed to produce and publish numbers as if they were all the same? More nonsense.

All this does is misinform the debate and feed Internet trolls.

OK, so people get the concept of comparing climbing performances (or attempting to) using power to body mass ratios, the now ubiquitous W/kg numbers. But can you do something similar with time trials over flatter terrain?

If you want to normalise flatter terrain ITT data, then you can make a reasonable stab at the differential of each rider's power to aerodynamic drag ratios i.e. the rider's time trial wattage output divided by the rider's coefficient of drag area (CdA), measured in units W/m^2. Of course this assumes the same wind conditions apply for the riders being compared which can be problematic in itself when the riders being compared are on course several hours apart (as was the case for instance with Tony Martin and Chris Froome).

On flatter ground, the higher your W/m^2, the faster you will go. This neat chart courtesy of Robert Chung shows the close relationship between flat road speed and power to aero drag ratio:

Hence we can reasonably say that the faster rider on the day has a higher W/m^2 (putting wind differences to one side of course). This is the flat land equivalent of the hill climber's W/kg.

What we can't say however is how much of that speed difference is due to higher power output and how much is due to a lower CdA. Indeed, it's possible for a rider to produce less power than another yet go faster if their aerodynamics is superior.

I’ve dealt with many riders of similar morphology who have significantly different CdA. I have a former team mate who was same height and weight as me (actually he was a bit heavier) and we have similar power output as well (mine was a bit better), yet his CdA is ~ 20% less than mine on our respective pursuit bikes. Our equipment was very similar. His natural body shape on the bike means he is just far more aerodynamically gifted. That’s why he medals at worlds and I don’t even make state finals.

Unless you know each individual rider’s CdA, attempting to derive power differentials from ITT speed is just pissing in the wind.

In this 2011 post I provided a short snapshot into this, with data provided by Andy Coggan who devised a draft variation to his Power Profiling tables, this time creating an Aero Profiling table:

The top of that list is a good indicator of what's required to set/break Boardman's hour record of 56.375 km (35.030 miles), now classified by the UCI as "best human effort".

Now if people think getting pro riders or their team management to release their power data is proving difficult, try getting their wind tunnel or aerodynamic field test data.

The Dopeometer

OK, so let's get back to obtaining riders' power meter data and using it as a dopeometer.

This seems to be a popular request. Greg Lemond wants the data released, as do the Bike Pure people. All over the net on forums and blogs and twitter and so on people are calling for the data to be released. The point being such transparency is a good thing, and I have no argument with that. But will it actually help? Or will it just be a public relations exercise and not really provide any additional insight into the issue?

Let us for a moment imagine that tomorrow morning we all wake up to find Froome and his professional riding colleagues and competitors release their power meter data.

Then what?

Will having more certainty over the accuracy of power data help us confirm or deny doping status?
A: No. All it will do is re-emphasise confirmation bias for those with an opinion one way or another.

What power output will confirm doping and what won't?
A: Nobody actually knows. All we have is differing opinions on the subject of what constitutes possibly suspicious performances. Yet people already have their suspicions. An SRM file isn't going to change that.

Just where is the doping power plausibility line? Can we really assign such a line? Is 6.2W/kg for an hour proof? 6.3? 6.4? 6.41?
A: In reality we simply can't put a clean line in the sand. The line for each rider may be different, and the line may vary depending on context. How long was the effort? When did it occur? What were the environmental conditions? How steep was the climb? Was it solo or with others? Was it a consistent effort or variable? Who responds better to doping?

Will it change which riders should be placed under scrutiny?
A: No, we already know who they are. They ride bikes professionally and at the elite level, win races and/or go up hills faster than the rest of us mere mortals.

Will it make doping detection easier?
A: Hardly, since proof of doping requires a positive test, a confession or reliable testimony and evidence, and we already know who should be scrutinised.

Will it prove riders aren't doping?
A: Of course not. Since it assumes there is an arbitrary upper power limit for doping to be confirmed, it does nothing to pick up any doping by riders who are below whatever that arbitrary limit is. No green jersey contender for instance is going to out ride the GC contenders on major cols. Hence such data only serves to tell us what we already know, i.e. a handful of riders finished ahead of their competition on the mountain top finishes.

Can power data be manipulated?
A: Yes, of course it can. Accidentally, inadvertently or deliberately. So then we'll have those on the conspiracy trail of a new doping detection avoidance technique of "data doping". Since we already know the amount of slop in power estimates from other methods, then fiddling with the numbers means no-one can really know if numbers are fiddled or not. There are of course forensic data analysis techniques that can identify some examples of that, but only if crude data manipulation methods are used. If riders and their support people are clever enough to manipulate blood to avoid detection, I'm pretty sure they'll be able to work out how to manipulate data to avoid detection.

Has "data doping" happened before? 
A: Sadly, yes as this example shows when a rider attempted to use doctored power data to prove a performance benefit from using a particular type of cycling equipment. Fortunately in this case the fraud was detected - but it took a Professor from Berkeley to point it out.

What would it cost to run such data collection in an independent manner, and free from possible manipulation?
A: Millions of dollars. Think about the number of bikes in the ProTour, the need to carefully calibrate say 1,000 SRMs, to have non-tamperable data loggers, to ensure all riders correctly perform zero-offset checks before and during races. The data collection process. Staff to manage this. Millions of dollars that perhaps would be better directed at improving doping control processes, technology, reducing testing costs, and simply performing more tests and more frequently testing in and out of competition.


I get that people want to see the data, and hope it's a short cut way to provide certainty around establishing whether a particular rider is doping. I get that release of such data may appear to increase transparency. But at the end of the day we'll just be back to where we started before all this data becomes available: i.e. none the wiser about riders' doping status.

SRM make a fine power meter, but it's not a dopeometer.

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Windbags

There seem to be a lot of windbags lately.

Once again people in the twitter/forum sphere are ignoring just how much wind affects speed for the same power output, even on steep climbs where overcoming gravity is the major energy demand factor.

Let me give you a basic example.

Let's take a rider with Pro Tour level power to body mass capability as follows:

400W Functional Threshold Power
69kg body mass
and allow 8kg for bike + kit

So that's a rider with an FTP of 5.80W/kg

A few assumptions about a point along a typical climb:
Gradient: 8%
Air Density: 1.065kg/m^3 (e.g. 1010hPa, 20C, 50% humidity @ 1000m altitude)
Rolling resistance: 0.0045
CdA: 0.350m^2
Wind: none

At that point on the climb, at 400W their speed would be 20.6km/h. But that of course assumes there is zero wind.

Conversely, if we have a rider climbing an 8% gradient at 20.6km/h with those air density, mass and rolling resistance values, then they will be required to output 5.80W/kg.

Pretty straightforward so far.

So what happens to our estimated power based on speed and gradient etc if there is some wind but we don't account for it? In other words we measure their speed as 20.6km/h, but we do not know the actual wind conditions?

Well let's assume we know precisely the mass (body and the bike + kit), rolling resistance, air density and rider's coefficient of aero drag (CdA). I'll get to errors in those later.

If there was an overall tailwind, then for the same power output the rider will climb faster. But if we don't account for that tailwind when estimating power output for that faster speed, then we will over estimate the rider's power output. And conversely, if we don't account for any headwind, we will under estimate the rider's power output.

So just how wrong can we get power estimates if we rely on climbing speed alone and do not account for the wind? Well to save you the trouble, I've plotted the W/kg actually required to climb at 20.6km/h with wind speeds ranging from a tailwind of 5m/s to a headwind of 5m/s.

Just so it's clear, this chart shows the power to body mass required to ride at 20.6km/h on that 8% gradient and with the other assumptions earlier listed. We can see just how much the wind conditions impacts the power required to maintain a given speed.

Hence, if you do not know the wind speed, then you have quite a sizeable potential error in any estimate of power from the rider's speed.

I've colour coded the Beaufort Wind Scale ratings on the chart. Of those shown on chart for instance, a Gentle Breeze is when light flags are extended. Even riding into a light breeze of 2.5m/s (that's not enough to extend light flags) means an error in calculating W/kg of over 9%! If the wind were a gentle head breeze of 4.2m/s, then the error in power estimated from speed increases to over 17%!!

Let's put that into perspective. A 10% power variation about the variation in power output for a trained rider from out of form / off-season to their peak fitness levels. That's the level of potential error in power estimates from a light breeze we can just start to feel on our skin.

Of course the actual wind speed and direction relative to a rider changes during a climb, some climbs have more shelter than others, the amount of shelter varies (trees, vans, people, other vehicles in race convoy etc), the wind does change direction due to the shape of the mountain itself, and of course the road itself changes its direction relative to the prevailing wind. Then there is the impact of drafting other riders, which is more of a factor with increasing headwinds.

So no doubt there are some swings and roundabouts, but who can really tell what the actual wind is? Answer: No-one.

If you can see flags flapping, then forget about making sensible estimations of riders' power to mass values. And if you can't see them flapping, then at least include some error bars in the estimate, unless you know exactly what the wind was doing.

What about the other assumptions, such as CdA, Crr, mass of bike + kit?

OK, well let's examine the impact of getting each one of those assumptions wrong by say adding 10% to each. What does that do to the power required to ride at that same speed?

CdA @ 0.385m^2
Power for same speed = 404W (+0.9% error)

Crr @ 0.00495
Power for same speed = 402W (+0.5% error)

Bike+kit mass @ 8.8kg
Power for same speed = 406W (+1.4% error)

We can see that error in estimates of power from climbing speed are less sensitive to errors in CdA, Crr and bike/kit mass*, and are dwarfed by the error introduced by wind, and wind is rarely, if ever, measured with any accuracy on these mountain ascents.

* even so, it helps to get them as correct as we can

Wind matters a lot when determining cycling power from speed, no matter the gradient.

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Leaps and Bounds of a Watery Kind

Something a bit different today.

Swimming.

Just a chart showing the progression in the men's 1500 metre swimming world record since records began for the event over a century ago for long course, i.e. 50 metre pools.

The data is from this Wikipedia link

Nothing really specific, other than to post up an example of the way progression in performance has occurred over a century has not been linear nor predictable in other ways but rather occurs with quite different rates of improvement. It was prompted by a discussion about, you guessed it, using past performances (i.e. cycling mountain ascent times) or the ubiquitous estimations of power to body mass ratios as a means to calibrate a modern day "dopeometer", which is of course a path fraught with problems.

Lasting of the order of 15 to 20 minutes, the elite 1500m swim is an event dominated by an athlete's aerobic metabolic capabilities, their morphology and water drag characteristics and with quite a deal of technique/form involved, e.g. making best use of turns, and there are far more degrees of freedom of movement than say pedalling bicycle cranks permits.

Looking at the chart we can see rapid improvement occurred in the 1920s, then only gradual change until the late 1950s when there was a consistent and significant rate of improvement for 20 years through until the late 1970s. I'm guessing improved access to suitable facilities enabling more athletes to compete played a big role in helping to drive this rapid change, along with presumably improvements in training, technique and so on.

Since then the improvement has been far more incremental despite the 1990s and 2000s being the EPO era and the 2000s the era of the swimsuit technology wars. What I haven't done though is to consider the change in power demand for these more incremental performance improvements, i.e. does a small change in speed require a significant change in power? I am presently not well versed in how linear or curvilinear the speed versus power relationship is for swimmers. No doubt there has been plenty of research into this.

Of the 46 new world records plotted, 28 (61%) were by American or Australian swimmers. The only "eastern bloc" athlete in this list is Vladimir Salnikov of the former Soviet Union with 3 records set in the early 1980s. Current world record holder is Sun Yang of China with his swim at the 2012 London Olympic Games.

Edit to add:
After Charles' comment about looking at other shorter swimming events - I plotted the progression with the 400 metre world record as well, and overlayed the two - and adjusted the time scales so the relative progression can be directly compared.

A broadly similar pattern, which is not surprising as you'd expect similar means of performance improvement, but the progression with the 400m event is more consistent than for the 1500m event.

The 400m swim, from a energy demand perspective, is similar to cycling's individual pursuit, and I'd expect roughly one-quarter to one-third of the energy demand is met by anaerobic metabolism (compared with say 10% for the 1500m), the balance of course supplied by aerobic glycolosis.

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You can't touch this, Part III

NP Busters again today.

This is third part in a chat about Normalized Power and NP Busters.

In Part I, I reviewed the concepts of Average and Normalized Power and how and when they are useful tools for assessing your ride, the differences between them and how NP accounts for the highly variable nature of our power output as well as the non-linear relationship between the strain we experience and our power output, both things that are masked by inspection of Average Power alone.

In Part II, I expanded on how Normalized Power, well, normalizes rides by providing a reliable indicator (i.e. NP) of the metabolic strain experienced from rides of quite different types, and demonstrated this with an example by comparing a time trial with a criterium race by the same rider.

Part II also provided a definition for an "NP Buster", a term coined many years ago to indicate a ride with an NP somewhat higher than is typical or higher than what would normally be associated with the level of strain experienced. IOW, it significantly over estimates the rider's steady state power output capability. And by significantly, I don't mean double, but by more than 5%.

So, how common are NP busters, and what can we learn from them?

Just to quickly recap, an NP Buster is a ride (or part of a ride) of about an hour's duration where the NP is > 105% of a rider's well established Functional Threshold Power (FTP). There are a few caveats about accurate power measurement, correct application of the NP algorithm, and a valid FTP setting before we declare an NP Buster, but assuming those requirements are satisfied, then we can declare a buster.

Like cyclones, NP busters do happen, and there are places and times when they are more likely to happen. They are also relatively rare, especially when compared to the vast "weather system" of all rides performed. But unlike cyclones, they don't represent some vast destructive force to be feared, but rather provide an opportunity to learn something.

Over the years, of all the files from riders I've coached (and myself of course), I have seen maybe one or two true NP Busters. That represents less than ~0.01% of all rides. Of course I have seen multiple ride files with an NP > 1.05 but they don't qualify for one or more reasons as true busters.

Now it's entirely possible that I have a lower than normal representative sample of NP busters in all of my client's data, but even if NP Busters are under sampled by a factor of 10, that still means they only occur less than 0.1% of the time. For a large proportion of riders, they will just never happen. And that's because for a large proportion of riders, they are just not capable of generating a buster. More on that later.

Over the years the creator of the NP concept, Andy Coggan, did actively seek and collect files from those who had genuinely generated an NP Buster, and I have data on those 20 examples to share (thanks Andy). Yep, only 20 examples out of many tens, perhaps hundreds, of thousands of power meter files. OK, perhaps not a brilliant statistical indicator of their true frequency, but you get the idea that these are not exactly an every day occurrence.

As an exercise in testing his Normalized Power algorithm, Andy also issued a challenge in 2009 at the Google Groups wattage forum for riders to attempt to complete one of a series of suggested workouts, any of which would have resulted in an NP buster. There were few, if any, takers.

Below is a chart showing information about each of the NP Busters Andy had collected files for. Click on the chart to see a larger version.

Let me take you through what is shown.

Each horizontal green and blue bar on the chart represents an NP Buster. The green bars on the left show how far below FTP the Average Power of the NP Buster was. The blue bar on the right shows how far above FTP the Normalized Power of the NP Buster was. Hence, only rides with an NP more than 5% over FTP are shown.

On the right side of the chart I have also included the Intensity Factor (IF) and the Variability Index (VI) for each ride. IF is simply the ratio of NP to FTP (IF = NP/FTP). VI is the ratio of the NP to the AP (VI = NP/AP). So looking at the first bar at the top of the chart, the Average Power for the ride was 22% less than the rider's FTP, the Normalized Power was 5% higher than the rider's FTP, the IF was 1.05 and the VI was 1.34.

Now we can see something interesting when you look at all of the examples of true NP Busters. Even though the Normalized Power over estimates the rider's well defined capability of sustaining an equivalent steady state power output for about an hour, on most occasions NP is significantly closer to the rider's FTP than is Average Power, and by quite some margin. On only four occasions out of the twenty was the rider's AP closer to FTP than NP, and not by much. These four are highlighted by the red translucent boxes.

So even though all of these rides are NP busters (and hence by definition are extreme examples of stretching the NP algorithm) and are clearly nothing like steady state efforts (see the VIs), 80% of the time the NP was still somewhat closer to a rider's FTP than was AP.

So if you have a ride or part of a ride of about an hour with an NP > FTP (and the data and calculation of NP is valid) then it's very likely your FTP will be closer to NP than AP, even if it's an extreme case of an NP Buster.

Indeed, if you are still trying to work out your FTP, and you haven't really performed any testing or settled on a reliable means to establish it just yet, but do have some very hard one-hour ride/race data, then you can peg a reasonable initial estimate of your FTP to be somewhere in the region of 95-100% of the NP.

The other thing to note is if you are in the majority of people who can't or don't produce NP Busters, then charting your 60-minute mean maximal NP, for example with a periodic chart in WKO+ software, is a reliable means to track longer term aerobic fitness changes. Here's an example chart of quarterly progress in 60-minute mean maximal Normalized Power over two years:

Plotting progress with 60-minute mean maximal NP
is a handy way to track longer term fitness changes.

Can you generate an NP Buster?

Of course there are going to be people who see instances of NP Busters a little more frequently, and it comes down to two things:
- the physiological profile of the rider
- the type of rides/races they do

To generate an NP Buster you need to execute a ride which includes a lot of very hard efforts of 30+ seconds duration which are substantially higher than your FTP. Many riders simply do not have the physiological profile to do that, as it requires a rider to posses both high neuromuscular power and a high anaerobic work capacity, especially relative to their aerobic capabilities.

NP Busters often involve out of the saddle efforts that engage the upper body musculature to enable the high power outputs necessary to generate them. An example of the sort of ride where this is likely to occur is in a criterium, and in particular one where there is a 20+ second long hill and/or a U-turn to negotiate each lap. Of course you could design a training session with such efforts or try your hand at one of the challenge sessions posted by Andy Coggan.

The ride file shown below is an example of an NP buster candidate course - a U-turn at the bottom of a hill in a criterium. In this example the rider has an FTP of ~315W (shown by the horizontal dashed line) and was able to continually punch out of the corner hard enough and long enough to contest the race finale. Yellow line = power.

Races such a crits with U-Turns and hills are likely NP Buster candidates
for riders with the right physiological capabilities

To better illustrate the file, this is what it looks like when you apply a 30-second rolling average to the data. This makes it pretty clear how frequently and how hard the rider had to push themselves. And this was with a small break away group, not a large group of riders.

  Using a rolling 30-second average power trace
helps to see why a ride might have been an NP buster

In this case AP was 276W (13% less than FTP) NP was 354W (12% more than FTP).

Now whether we should include rides which involve out of the saddle efforts as examples of NP busters or not is a consideration, but since it's not an uncommon thing in bike racing (especially criteriums like the one above), then I figure we may as well since that's how bikes are raced.


OK, so if you are a rider that can generate an NP buster, then that tells you something about your unique capabilities as a rider, that is you likely posses both a good sprint and a high anaerobic work capacity. You are also in the minority that possess very potent race winning weaponry, provided your aerobic fitness is good enough to use it. But it also means that you'll need to take a little more care in how you choose to interpret the NP from such rides, and don't go immediately assigning yourself a high FTP on the basis of such rides.

For most everyone else, NP provides a robust and reliable means to assess the metabolic strain for a wide variety of rides, and if you see an NP > 105% of your FTP, then there is a very strong likelihood that you've under estimated your FTP and maybe it's time to validate though reliable test methods.

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Aero for slower riders

A quick chart today for future reference whenever that classic online nonsense argument about aero benefits only being for faster riders, or that aero only matters above a certain speed....

Let's set people straight now: Aerodynamic improvements benefit riders of all speeds and power outputs. But who gains the most benefit?

Whether a slower rider should be putting time/energy/effort/resources into gaining or buying an aero improvement when they might perhaps be better focussed on losing weight and training more (or harder or smarter) is a moot point. Really, though, such an argument is a false dichotomy. Why not do both?

The other consideration of course is if you are going to chase an aero improvement, then there are two main ways to achieve that:

• improved aerodynamic positioning, or
• improved aerodynamic equipment

But again, this is not a case of one or the other. It's quite OK to do both and train better. You know, one could train to improve fitness, work on gaining a better aerodynamic position, and treat themselves to some nice aero wheels, or move from using a road bike with clip on bars extensions to a time trial bike. This is not an either/or scenario.

If you are a back/middle of pack rider, then some bling wheels are not going to make you the next world champion, so some perspective here is warranted but the rationale for why you are looking to improve your performance is a matter of personal choice. If you want to be faster, then you do all the things you can given the constraints you have (time, money, knowledge, rest of life factors etc). And we are talking about people riding in competition-like events, not your cruiser to pick up some milk at the local shops (let's be sensible here).

If you are just happy with participating rather than competing, then sure, what does it matter? If you just like having nice equipment and have the money to spend, heck, go for it. Enjoy yourself.

But let's get the physics out of the way with a chart to quickly summarise the situation with an example.

The chart below plots the time taken to complete 10km on a flat road with no wind at various power outputs, from a modest 150 watts, through to a solid 350 watts. Other assumptions are shown on the chart, but changing the parameters really doesn't change the basic principles here. Click on the chart to see a larger version (right click to view in a new tab/window).

There are two lines, showing the reduction in time to complete the 10km as a rider's power output increases. No surprises there, more power with all else the same, you go faster.

The two lines also show the difference between a rider with a coefficient of drag-area (CdA) of 0.30m^2 and 0.27m^2 (a 10% reduction). That's roughly the sort of reduction in CdA you might expect going from standard low profile spoked road bike wheels to specialist aerodynamic wheelset, or riding on the tops to riding on the drops.

Under that are the blue columns, which represent the time saving over that 10km by reducing CdA from 0.30m^2 to 0.27m^2. As you can see, the slower less powerful rider saves more time in absolute terms than the faster more power rider. However, when expressed as a percentage of time saved, they are nearly equivalent savings, with the faster more powerful rider making very slightly better gains in percentage terms.

Now of course some parameters do change under some conditions, e.g. cross winds can affect the apparent CdA to differing degrees at different speeds, so in those situations, a faster rider may benefit a little more in percentage terms, but in general, there really is no physical reality to the old myth that aero only benefits the faster rider, or that óne needs to ride at X km/h to see benefit.

Pithy Power Proverb:

The largest absolute time savings from a given aerodynamic improvement are made by the least powerful/slowest riders.

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l'Alpe d'Huez. Again. Top 200

Some charts for fun. Pub chat, no more, no less.

Published on this Finnish forum are the top 200 ascent times for the Alpe d'Huez climb, often used in the Tour de France. I haven't been able to clarify how the times were established, nor if the same start and finish timing points were used. Official timing of the ascent started sometime in the 1990s but different timing points were used from 2001 onwards, so I cannot say with any confidence if we are comparing apples with apples. There are plenty on forums that definitely dispute Pantani's quoted climbing times (and that they should be somewhat slower than shown here, although still fast).

It's possible they reviewed footage to normalise these things, but in any case, nice work on collating the data, I'd say thanks personally but I don't speak or read/write Finnish. If any reader does, perhaps they can pass on my thanks.

Indeed racing context is also needed, e.g. Was it a long stage? Many previous cols? Attacking for the win or defending the maillot jaune? Conditions/weather/wind? Of course in 2004 the ascent was used for an individual time trial, not as the final climb of an alpine road race stage.

And yes, it's full of dopers and naturally covers the glory days when EPO, blood transfusions and other supporting cocktails were, sadly, the norm. Not that these things still don't happen, just seemingly not with the same outrageous impact on raw performance as before, at least not for the top of Pro Tour, but who knows about lower level riders trying to make the grade? Doping is still prevalent, and while the outrageous performance days may be suspended for now, the consequences are still just as insidious - shady characters and corruption, legitimate riders missing out on contracts and racing opportunities, people losing jobs, sponsors leaving, races and race results skewed and screwed, disillusioned fans. Long term health impacts. The list goes on.

As for conversion of ascent times to power to weight ratios, something that's been gaining in popularity lately with talk of mutants and the like as well as regular guesstimates published in online forums, well I cover that in this July 2010 post.

For reference though, using the methodology outlined in that post, the fastest time quoted in this list (whether or not accurate), would equate to a power to weight ratio of ~6.5W/kg +/- 0.4W/kg. Certainly not the nonsense level 7.2+ W/kg (or even 8W/kg) quoted by some.

In summary, just plot the ascent times and map the trends for individual climbs. Converting to a W/kg guesstimate may provide a way to make comparisons between climbs, but such estimates should be plotted with error bars, because there are too many unknowns in key assumptions and estimations are subject to methodological error. Converting actual performances to plot a wattage for a standard rider of 70kg makes even less sense, since W/kg is already normalising such estimates.

So here are two charts I whipped up from that Finnish forum data.

The first plots the fastest five riders each year. Click on it to see a larger version:

The next is a frequency distribution of the top 200 times for the years l'Alpe was raced.

What this means is that of the fastest 200 times recorded, 16 of them were in 1994, and so on. Quite clearly something changed between 1989 and 1991. One could speculate about other changes since then. Use of EPO, introduction of tests and of course doping detection avoidance measures, better tests, move back to blood transfusions as dominant in competition doping method and so on.

And a new chart, this one plotting the combined average speed of the top 5 riders (except for the first three years of data where there were fewer than 5 riders in the list for each of those years).

So, there you have it. enjoy the pub chat. And if you want to know how you compare, perhaps this other post about mere mortals might give you some beer for thought.

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You can't touch this, Part II

In my previous post, I reviewed the concept of Average and Normalized Power, more as an introduction to some further thoughts about the topic of NP Busters. I also said that this would be a two part discussion, with Part II on the topic of NP Busters. Well I am getting to that but it will actually require three parts, so here continues the discussion on Normalized Power, as another prequel to an NP Buster chat. I will at least introduce what is meant by an NP Buster.

Previously I demonstrated by way of an example of a proposed interval session how average power can be a misleading indicator of metabolic strain, especially when power output is highly variable, and that Normalized Power represents a better means of measuring metabolic strain. Well we don't need to make up theoretical examples, we can turn to real data.

Criteriums versus Time Trials

Let's consider the Normalized and Average power from hard rides of different types but of similar durations. An obvious example would be to compare a time trial with a criterium race.

A TT is typically ridden solo and involves sustaining a high power in a relatively steady state manner, with perhaps some variability if the terrain is not flat or has some technical elements, while a criterium involves substantially variable power outputs as one deals with or dishes out the attacks and surges, the braking and/or coasting into and accelerations out of turns, the inevitable driving of the pace in or to establish a break, and sitting in the slipstream of others when recovering. As rides, they are poles apart.

The following chart (click on it to see a larger version) shows a comparison of the power output over time for a time trial and a criterium race by the same rider, performed within about five weeks of each other and both on relatively flat courses. There are two plots for each race. The lines that jumps up and down are the second by second power data trace, and the two straight horizontal lines are the average power from each race. The time trial (blue) is a little shorter in duration than the criterium (red).

The instantaneous power output is a little hard to follow since it jumps up and down so much, but even so, it's clear that the criterium power line (red) is far more variable than the time trial power line (blue). This is pretty typical. So while both of these races were hard efforts by the same rider and over reasonably similar durations, there was a substantial 40 watt difference in the average power.

On closer inspection we can see a period in the crit race from around the 33-minute mark where power dropped substantially. It happened that the rider had a puncture and "took a lap out" to replace a wheel and rejoin the race (annoyingly as they had established a breakaway prior to that). So we would expect this lower power period would account for some of the lower average power overall, even so, the average power up to that point was 272W, still 25W less than the average power in the time trial.

But let's not forget that time spent not pedalling affects what you can do when you are pedalling, and so that mini break no doubt meant a little freshening up before rejoining the race, and an ability to go a little harder than might have been the case with no recovery.

A good way to gain some insight is to view the power trace after applying a filter to the data, and one simple filter is a rolling 30-second average (i.e. each point on the chart represents the average power for the preceding 30-seconds). Here's the same plot showing the rolling 30-second average power:

The vertical scale is now halved which means variances are amplified. The 30-second rolling average makes it easy to spot differences in the power sustained during sections of a ride. In this example we can readily identify periods during the criterium of sustained harder and easier effort. Likewise, the time trial also shows two brief drops in power output, which correspond to a steep decline on the course with speeds too fast for continued pedalling.

A 30-second rolling average power filter is of particular interest as metabolic responses to changes in effort really start to kick in at around that time frame - many have what we call a "half-life" of around 30-60 seconds. Very brief forays (a handful of seconds) at higher powers are not all that metabolically stressful but sustain the higher power for longer (>20-30 seconds) and it gets ugly, fast. How fast depends on how hard you go.

Hence it's no coincidence the algorithm used to calculated Normalized Power is based (partly) on a rolling 30-second average power filter. There's a couple more important elements to the NP formula than that (although it's not a very complicated formula) but it starts with this 30-second rolling average.

So what was the Normalized Power for these two races? Well here they are plotted on the chart as the two horizontal lines:

In effect, the Normalized Power from each race was the same (OK, one watt different). So even though the races were very different in style, they were both hard and produced a Normalized Power that was more representative of the metabolic strain experienced.

OK, so that's pretty nifty, and is why Normalized Power is a good way to glean from races how your fitness is tracking despite the lack of a formal testing protocol.

It should also be of no surprise there is very little difference between the Average and Normalized Power for the time trial (297W and 299W respectively), since the effort was already relatively steady state, and NP is about providing a steady state power equivalent (hence the name "Normalized").

By definition, Normalized Power will be equal to or greater than Average Power, and the gap between them will depend on the amount of variability there is in the rolling 30-second power, and especially the duration and number of forays at very high power levels.

Using Normalized Power to estimate Functional Threshold Power

Since Normalized Power is providing a steady state power equivalent for longer (dominantly aerobic) durations, then it follows that one can consider NP from hard rides/races of about an hour as one means to estimate FTP.

The well established rule of thumb is for durations of about an hour, Normalized Power will be no more than 5% higher than the maximal quasi-steady state power a rider is truly capable of. Since maximal quasi-steady state power for about an hour is the definition of Functional Threshold Power, then we can simply state:

~1-hour NP <= 105% of FTP

or at least that it will be for the large majority of riders, a large majority of the time.

So if you notice from a hard ride/race of about an hour that NP is > 105% of FTP, then it's quite possible your FTP is higher than you think it is.

Caveats and fruit salad

There are of course caveats to this rule of thumb. I'll go over these as they impact the definition of an NP Buster and can help explain what some perceive to be anomalies when interpreting their own NP numbers.

The duration caveat
Since we are primarily concerned with obtaining a measure of equivalent aerobic metabolic demand/strain, then the duration of any comparison of highly variable versus steady state efforts needs to be sufficiently long to reduce the confounding impacts from individual differences in anaerobic work capacity and neuromuscular power capabilities relative to a rider's aerobic capabilities.

For this reason, NP numbers from rides or parts of a ride of less than 20-minutes duration are not suitable for such comparisons, nor as an indicator of a metabolic steady state power equivalent. I generally take more notice of NP for durations of at least 30-minutes, but it depends on the rider's individual circumstances and capabilities. As the duration of a ride reduces (e.g. down towards 20-minutes), then the difference between NP and a rider's actual maximal steady state power can become somewhat wider.

The circumstantial caveat
There are circumstances where no matter how one rode (steady state or variable), their power output would be somewhat different when compared to another circumstance. Examples of this might be comparing riding on an indoor trainer to an outdoor ride as some people experience a sizeable difference in the power they can sustain indoors versus out.

Another might be comparing long steep hillclimb to flat terrain, or on a road race bike versus an aggressive time trial bike position that might compromise power output for some aerodynamic gains, or really hot day, or at altitude and so on. Another is the use of frequent out of the saddle efforts engaging upper body musculature versus staying in the saddle.

So while Normalized Power enables a comparison of some apples with some oranges, we need to be thoughtful when using it to compare all types of fruit.

The power meter data accuracy caveat
Well it should go without saying that power data needs to be accurate for the interpretation to make sense. While basic accuracy is a factor, there are ways in which data integrity can be compromised even though the individual data points might still be accurate. This mostly concerns the way some power meter head units collect and store data, especially the sampling rate. If the fruit is bad, well no point in trying to use it.

An example of this is/was Garmin's use of "smart recording", which should in current firmware versions be automatically disabled when using a power meter, but it makes sense to ensure it really is disabled. This was also a factor for older model power meters with memory space restrictions, and options to "down-sample" data (e.g. older Powertap head units). You could get away with 2-second sampling (just), but any more than that would compromise data integrity to the extent that the data might not be all that useful.

The software algorithm caveat
While the Normalized Power algorithm is pretty straightforward and in the public domain, not all software (be it commercial desktop software such as WKO+, home designed spreadsheets or websites) produce the same results. There may be a number of reasons for that, e.g. use of an incorrect algorithm (I've seen it many times with people claiming an NP that was incorrectly calculated) or more subtle matters such as how gaps in power data or variable duration time stamps are handled.

So when doing such analyses and/or comparisons, then consider the software you are using as well and validate it is correctly applying the algorithm. Some food processors take the goodness out of the fruit.

So what is an NP Buster?

An NP Buster is a ride that breaks the rule of thumb, or put this way:

~1-hour NP > 105% of FTP

provided:

1. the above caveats are taken into consideration (especially power data accuracy, correct calculation of NP, but also the circumstantial caveats), and
2. FTP at around the time of the claimed buster ride has been well established using one or all of Andy Coggan's Sins 5, 6 and 7 referenced in this post on establishing Functional Threshold Power, i.e.:

• using critical power testing and analysis
• from the power that you can routinely generate during long intervals done in training
• from the average power during a ~1-hour TT

Such NP Buster rides have occurred, and there are riders who can produce them. They are however rare, and I'll talk more about them in Part III.

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You can't touch this, part I

NP Busters 

are the spark for today's musing. It's an old topic but a fun one. I am however going to break this into two parts, first (Part I) to review the concept of Average and Normalized Power, and then (Part II) to chat a little on NP Busters.

An NP Buster?

So before getting into the discussion of NP Busters and just WTF I'm on about, let's just go back to Power 411 to remind us what Average and Normalized Power is all about. This is mostly for those that are new to the concepts, even though NP has been with us for a decade, the number of people beginning to use power in training and racing is ever growing and besides, a refresher is never a bad idea.

For those well versed in power meter analysis and associated software, they are no doubt familiar with the concept of Normalized Power and perhaps don't need to go over old ground the rest of this post covers. Much of what I am covering in Part I is also in this original item by Andy Coggan introducing Normalized Power. I suggest reading it if you have not done so before (and you're interested in learning about this stuff).

In summary, Normalized Power is neat a way of enabling us to make sense of rides that are, by their nature, highly variable in power output, especially when a straight numerical average of a rider's power output is often not that helpful in assessing the "damage" done during a ride.

With that said, you can wait for Part II, the NP Buster chat, or read on...

Average Power

Average Power is by definition fairly straightforward – being the average of a rider’s moment by moment power output over part or whole of a ride. For example, 5-minutes at 100 watts followed by 5-minutes at 200 watts equates to a 10-minute Average Power of 150 watts.

A measure of work done
Average Power tells us how much mechanical work was performed during a ride. This knowledge has numerous benefits, in particular when assessing daily energy intake requirements:
Average Power (watts) x Ride Duration (seconds) = Mechanical Work Performed (joules).
e.g. 150 watts x 600 seconds (10-minutes) = 90,000 joules (90kJ) 

Of course that's just the mechanical work done at the cranks propelling the bike forward, and not the total energy metabolised, which will be approximately 4-5 times that value depending on a few things, primarily a rider's individual gross mechanical efficiency (GME - the ratio of energy reaching the cranks as a proportion of total energy metabolised). The vast majority of energy we metabolise ends up as waste heat. That's just the warm blooded Mammalian way.

A (good) indicator of energy metabolised
Somewhat serendipitously, since 1 Cal (kcal) ~= 4.2kJ, we can as a reasonable first approximation use the kJ reading from a power meter file (e.g. 700kJ) and make a straight conversion of that number to energy metabolised (e.g. 700 Cal) since the GME and conversion of kJ to Cal (almost) neatly cancel each other out. The real conversion is probably more like in the range of:
1.05 - 1.15 x kJ of mechanical work done  = Calories metabolised.

A measure of fitness
The Average Power a rider can maximally sustain in a well-paced steady state effort such as during a flat time trial or on an indoor trainer is one of the most direct and objective measures of fitness. It is usually expressed in terms of maximal average (mean maximal) power for various durations (e.g. 1-minute, 5-minutes, 1-hour), and in terms of watts per kilogram of body mass (W.kg^-1).

It should come as no surprise that we can sustain a higher power output over shorter durations. Over the course of a training block, we seek to raise the power a rider can maximally sustain per kilogram of body mass for durations of relevance to the rider's target events. The higher the mean maximal W.kg^-1 number, the faster one can ride and/or the longer a rider can sustain a given pace. Along with a consideration of the specific demands of a rider's events, this is a fundamental principle that should guide a rider's training.

Normalized Power

So what happens when power output is highly variable, such as typically happens when we ride outdoors over variable terrain, or with a group, in a road, criterium or track race or over a mountain bike course; or perform interval efforts at various power levels with rest periods interspersed?

Racing, group rides, hills all provide for highly variable efforts.

In these common scenarios, Average Power can be a misleading indicator of intensity and understate the level of difficulty of a ride (often substantially so).

That’s because, and to quote Andy Coggan:

1. the physiological responses to rapid changes in exercise intensity are not instantaneous, but follow a predictable time course, and

2. many critical physiological responses (e.g., glycogen utilization, lactate production, stress hormone levels) are curvilinearly, rather than linearly, related to exercise intensity.

This latter point is really important. As power output goes up, the level of strain experienced increases exponentially.

Steady state
By way of example, let’s say a rider is capable of maximally sustaining 200 watts for about an hour . If we asked them to perform a 20-minute steady paced effort at 200 watts, then assuming they are not unduly fatigued, we should expect the rider could actually complete such an effort, since by definition they are capable of sustaining that power output for longer than 20-minutes. It would be hard, but do-able (indeed, over 20-minutes, a rider could typically maximally sustain ~ 104-109% of their 1-hour power).

Not so steady state
But what if we asked the same rider to perform a 20-minute effort with the same average power of 200 watts, except this time the rider is asked to perform 10 x 2-minute interval repeats comprising 300 watts for 1-minute followed by 100 watts for 1-minute?

Those with any experience of this sort of effort will know the rider would be very unlikely to successfully execute the prescribed session, despite the average power being the same. This is because the strain experienced during the 300 watt sections is far greater than the relative increase in power, and is not equally matched by the reduced level of strain experienced when riding the 100 watt '"recovery" sections.

Normalised Power is a clever means by which reported power output is adjusted to take into account the typical and natural variability in power output. To quote Dr Coggan:

“Normalised power provides a better measure of the true physiological demands of a given training session - in essence, it is an estimate of the power that you could have maintained for the same physiological "cost" if your power output had been perfectly constant (e.g., as on a stationary cycle ergometer), rather than variable. Keeping track of normalised power is therefore a more accurate way of quantifying the actual intensity of training sessions, or even races.”

This is one reason why we track Normalised Power, as it represents a more accurate indicator of the level of difficulty and is a helpful guide to changes in fitness over the medium and longer terms when the vast bulk of training data comprises rides of variable effort levels.

Feasible training sessions

Interval training, i.e. the use of periods of higher intensity work coupled with recovery periods, is quite a common feature in many training plans (usually because it can be highly effective in improving fitness). Normalized Power is very helpful in establishing whether a proposed training session is "physiologically feasible".

In the interval example quoted earlier (the 10 x 2-min 300W / 100W intervals), the Normalised Power for such a session would be 234 watts, meaning the equivalent physiological cost of riding at a sustained steady state 234 watts. Typically you would expect a rider with an FTP of 200 watts to be able to maximally sustain ~ 104-109% of their FTP for 20-minutes, or ~ 208-218 watts.

Hence the original prescribed session was unrealistic from the outset. You can use Normalised Power in this manner to guide the level of difficulty of training sessions, so that they are hard enough to provide sufficient stimulus to improve fitness but are not so hard they become impossible to execute. Nifty huh?

The underlying physiological principles and the mathematics of the Normalised Power algorithm are described in more detail in an article by Dr Coggan quoted earlier in this post.

Caveats
There are limitations and caveats to how one uses and interprets Normalized Power, and that's for Part II, so stay tuned....

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A time for a bit of sensitivity (analysis)

The Performance Manager Chart is a tool that's been with us for a while, being first released into the wild by Andy Coggan, and the guys from Training Peaks circa 2006. Before then it was tested by a dozen or so lunatics in a power meter users' asylum known as "TSTWKT".

In the years since, it and its off-shoot variants have become a ubiquitous tool for power meter users to inspect the "forest" that represents our overall training loads, as well as giving additional insight into our training patterns and as a indicator of likely form, either prospectively, or as a retrospective analysis tool.

People use the tool as one guide to their overall training progress, to check their actual and planned workload is appropriate for their current training cycle and training objectives. Of course it's only one part of the picture and as always, one must tend to the individual trees, that is, be concerned with the composition of one's training to ensure the specificity principle of training is not lost in the undergrowth.

There's been plenty written about these issues and the use and sometimes misunderstanding of the use of the tool. I'm not going to delve into the whole shebang here, rather just touch upon one small element about the Performance Manager Chart that the more experienced and/or astute user of this tool will understand.

A quick recap:
The basic Performance Manager Chart plots three things - Acute Training Load (ATL), Chronic Training Load (CTL) and Training Stress Balance, where today's TSB = yesterday's (CTL - ATL). It can also show other information if desired, such as daily training stress scores, best power performances and so on.

In layman's terms, ATL is an indicator of how hard you've been training in recent weeks, and CTL is an indicator of how hard you've been training in recent months. ATL and CTL are both exponentially weighted moving averages of the daily Training Stress Scores (TSS), which in turn are calculated from a rider's power meter data and their current threshold power.

Since ATL and CTL are exponentially weighted moving averages, a key input into their calculation is a time constant. The default time constants used for the PMC are 7-days for ATL and 42-days for CTL.

I thought I'd demonstrate with a video animation what happens if you change these defaults settings and comment on whether and/or why you should or would do so. Cue the (94-second long) video:

Occasionally the question asked is - what time constants should I adopt?

The answers usually include the following points:

• Suggest that you create a range of Performance Manager Charts, each with a different combination of time constants, and see which you consider best reflects your actual performances.
• Note that the chart is not particularly sensitive to changes in the CTL time constant, so you may as well leave that at the default 42-day setting.
• The chart is far more sensitive to changes in the ATL time constant, and some have suggested using a longer time constant for older/masters age riders, and a shorter one for younger riders with faster recovery time, although I'm unsure I would necessarily use such as rule of thumb, as there's more to it than just age.
• Even so, changes to the ATL TC (such that one would still consider it an acute indicator) don't radically change the fundamental patterns displayed on the chart, just the absolute values along with a slight time phase shift in the TSB. Keep in mind that it's the patterns that are more insightful than the absolute numbers.
• If you really want to go there, there is software (RaceDay Apollo) and a method described by Dr Phil Skiba to test yourself regularly such that the "ideal" time constants for you can be calculated, although there is likely a sizeable error range in such calculation of ideal time constants and the effort required to do the frequent regular performance testing to narrow that range is likely beyond the training desire of most.
• If you are a multi-sport athlete, then it gets pretty complicated, as the stress scores from different exercise modalities are not linearly additive, nor will they necessarily use the same time constants.

In my opinion, for vast majority of users there really isn't any need to deviate from the default values, as the additional insight to be gained is likely to be fairly limited. That's not to say it doesn't exist but keep in mind that some won't have TSS data for all rides, and/or TSS values that are possibly subject to errors from an incorrect estimation of threshold power (let alone the chosen source of power data).

But by all means this is not meant to dissuade you from playing with the options. Go forth and explore. Or let coach worry about it. We're good at that.


If you want to read more on the Performance Manager, I suggest the following links as starting points:
My Performance Manager Chart by me
Season Review with a Performance Manager Chart by me again
What is the Performance Manager Chart by Hunter Allen
The scientific inspiration for the Performance Manager by Dr Andrew Coggan

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More ZO Zen

A follow up to my post the other day about the setting of zero-offset / torque zero on power meters, and how we need to be sceptical about how auto-zero functions operate (if your power meter uses one).

I received feedback to suggest that auto-zero on SRM could not be as bad as I am suggesting, and that it might indeed be more accurate to leave the auto-zero on.

Well it hasn't been my personal experience that the auto-ZO is as reliable as theory might suggest, so I thought I'd do a test when I got the chance. More backyard science.

So today happened to be a lovely day, and I decided to go for a ride. Not a long one mind you because right now I'm about as fit as Harry the Hairy Nosed Wombat, but a long enough ride outdoors under fairly typical riding conditions for me.

For those that know Sydney - the ride went from Annandale, through Stanmore, past Sydney University, Redfern and onto Centennial Park where I did a 20-min "test" and rode back home again. I've done that ride about a bazillion times.

And before leaving I set my Powercontrol VI to use SRM's auto-zero function, and had my phone camera along to take a few snaps, to see what I noticed along the way. Here's the Powercontrol screen showing zero-offset before starting my ride:

The bottom line is the zero-offset value stored by the Powercontrol, and is the value used when calculating (and storing) power values. The middle, larger number, is the "live" zero-offset reading, akin to the offset number shown in the video in my previous post. i.e. if you apply some force to the cranks, that's the number that will fluctuate along with the force being applied. The "Auto" along the top just tells you that the auto-zero function is enabled.

The temperature inside and outside my home was not all that different, and my meter  had about 10-minutes outside before this initial check. This is a different SRM to the one in the video (different bike), although it's the same model of SRM.

So, my starting zero-offset was 409Hz.

After about 15-minutes I'm at some traffic lights near Redfern Oval, so I take the chance to pull over to the side and see what the zero-offset has done.

We can see that at some stage along the way in that initial 15-minutes, auto-zero has reset the zero-offset value to 417Hz, while the actual zero-offset is 407Hz, 2Hz less than when I left home 15-minutes earlier.

Just so that's clear, that incorrect zero-offset value is now being used to calculate all my power numbers. I have no idea when or how many times during that initial 15-minutes of riding the zero-offset was changed, nor what the size of those changes might have been, other than when I stopped to make this check.

OK, so I continue on to the Park and do a 20-minute test effort. Then after that I leave the Park to head back home, stopping on the bikeway alongside Moore Park to do another check. This is what I see:

Auto-zero has set zero-offset to 419Hz, when the actual zero-offset is 409Hz, same as when I left home about 50-minutes earlier.

Continue on home, and this is the final check I made after about 75 minutes of riding:

Auto-zero has set zero-offset to 403Hz, when the actual zero-offset is 407Hz.

So, to summarise in table format:

So, I have four actual zero-offset readings in a 1:15 ride that vary by only 2Hz (and this is pretty typical for my SRMs, i.e. not much drift in zero readings), yet the auto-zero function has reset the zero-offset value with a range spanning 16Hz. And that's just what I know it's done, let alone what I don't know it's done. As Rumsfeld would say, it's a known unknown.

Perhaps now you can see why I don't use auto-zero on my SRM Powercontrol.

The possible impact to my average power on this ride of a 16Hz differential in zero-offset is 3.5% and for my modest 20-minute test effort today, that's 8 watts. I reckon 8 watts is worth knowing about no matter how fast you are. If it were true, that's nearly one second per km in a time trial. But it ain't.

Now I have no idea whether the auto-zero performed better or worse than that on average because we just don't know. We can never know since no power meter keeps a log of zero-offset changes.

As I said in my previous post, such anomalous changes in zero-offset would make some analysis not worth doing (e.g. aero field testing when you are fine tuning equipment and position choices). I don't know about you, but I think a possible 3.5% variance is pretty significant. It's not something you can correct post-hoc either, since there is no record of what and when changes to zero-offset were made (power values are calculated based on the zero-offset value used at the time of recording).

At least with the Powercontrol, you can easily turn off the auto-zero function (just press the "Pro" button on the zero-offset screen), and checking the zero-offset is trivial press of the Mode & Set buttons at same time.

That's far better than having to navigate through various menus to perform one of the most important checks a power meter user needs to make every time they ride, let alone not being able to disable the auto-zero.

Of course YMMV

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