Looking under Froome's hood. Again.

I posted this item in December 2015 after some data on physiological testing of Chris Froome was made public in a mostly PR piece. Have a read there first if you haven't already done so.

Today I saw the published science paper was released and from the abstract I pulled out a few extra pieces of information, namely Froome's gross efficiency (23% at ambient conditions), power at blood lactate level of 4mmol/l (419W). His reported weight for the test was 71kg, which is likely above his racing weight.

So I thought I'd do up another chart, this time fixing the gross efficiency and VO2max values, and plotting the curve of aerobic power in W/kg terms versus fractional utilisation of VO2max:

The relationship between aerobic energy yield per litre of oxygen, gross efficiency, VO2max, fractional utilisation of VO2max and power output is outlined in this earlier blog post.

So what can we make of this?

1. A TdF winning cyclist has the physiology you'd expect of a TdF winning cyclist. That should be hardly surprising.

2. Froome has both high VO2max and high gross efficiency, which is a killer combo. Neither represent out of this world values. What that means is Froome's sustainable aerobic power output is then a function of his fractional utilisation of VO2max, and FUVO2max at threshold is a highly trainable aspect of one's fitness, more so than gross efficiency or VO2max.

3. The sustainable power as measured in this test was at a blood lactate level of 4mmol/litre, which is an arbitrary level for such testing. What any individual rider's BL level is at their actual "threshold" is quite variable, often somewhat higher.

4. It would seem that Froome's fractional utilisation of VO2max at this power level was ~86-87%. That's a pretty reasonable value for longer duration efforts of at least an hour for highly trained cyclists and it can quite feasibly be higher than that at threshold power, and certainly higher over shorter durations, e.g. 15-20 minutes.

5. The testing was also conducted at high humidity (60%) and temperature (30C) and somewhat interestingly Froome's gross efficiency was higher (23.6%) than when tested at ambient temperature (20C) and humidity (40%). That would add ~0.15W/kg at threshold, a very handy result for hot days. The reported his sustainable power was 429.6W at high humidity and temperature versus 419W at ambient temp and humidity. That power difference of 10.6W / 71kg = 0.15W/kg.

6. Weight. I'd expect Froome's race weight would have been a few kgs less than at the time of testing. e.g. 67kg at same power would add 0.35W/kg to threshold power.

Doping? Once again, this sort of data tells us nothing about any rider's doping status.

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FTP variability (and doping)

In one of the five hundred and twenty five thousand online forum threads about why Chris Froome is or is not a doper, one of the questions raised was about whether a coach could detect if an athlete was on the juice based on their performance (power) data.

That led to a comment about typical changes in a rider's power over the course of a season.

As to the question of a coach's ability to detect doping from performance, performance changes are multifactoral and so that makes it nigh on impossible.

It's relatively easy to measure the performance change (power meters enable that), far more difficult to parse out the specific reasons why it occurs.

Now of course one can wonder if you have known an athlete for a long time and know their training and performance history and have a reasonable understanding of their potential. If they find a sudden large boost when nothing else in particular has changed, well you might naturally begin to wonder.

Consider that I have seen athletes attain Functional Threshold Power improvement of between 5% and 100% in 6 months of training and you can immediately see the problem, especially given doping provides performance advantages well within the range of those attainable by completely legitimate means.

Better training, better diet, better sleep, better psychology, better aero, better planning and support, better race skills and race craft, better equipment and tools, and of course, doping. These are not mutually exclusive means to improve performance.

This is the problem e.g. that makes up much of the discussion about Froome or others. Lot's of Clinic focus on his "transformation". The problem is that there are plenty of legitimate as well as illegitimate means by which such performance changes can be explained.

Balance that with the fact that in the past 30 years half the riders standing on the podium for the major Euro pro races and top 20 in GTs are known to be dopers (let alone the ones that slipped though the net). Objective assessment therefore needs to consider all such possibilities.

However that still doesn't mean one can immediately infer from performance data or even physiological testing data such as lactate threshold or VO2max the reasons for one's performance, or more to the point, their change in performance.

I think the only way an ethical coach is likely to spot or suspect doping is if they are in frequent eye ball contact with the athlete, and it's not so much going to be from their on-bike performance, but rather from observing off-the-bike behaviour.

As much as coaches might like to be in frequent eye ball contact so they can do a better job, coaches are often not in such frequent close quarters with their clients. Riders travel and coach can't be with all their clients all the time. The exception are squad/institute coaches that interact multiple times per week and travel with their athletes that typically attend the same races.

More usually the contact is via phone/skype/chat/email and other social and electronic media style interactions, as well as the athlete's diary notes that accompany their power meter files. For the most part this works pretty well (athlete results demonstrate that to us all the time) but of course there are some things for which seeing the athlete is preferable and some personalities that require more eye ball contact than others.

Anyway, on one of the forums I made a comment about the typical variability in FTP for an active racing cyclist. An often quoted value is about 10% variance from out of form/off season to peak fitness. That was questioned as being quite a large variance. I really had nothing other than my years of coaching and personal experience to suggest whether or not this was realistic.

So I thought about attempting to answer the question with some data.

Fire up WKO4 and create a report using the following expression:

max(ftp(meanmax(power),90)) / min(ftp(meanmax(power),90))

and apply it to ranges covering entire years of data (with power data for >>90% of rides).

That expression calculates the modelled FTP for the date range selected, locates the maximum and minimum values for FTP that are calculated during that date range, and calculates the ratio of the maximum to the minimum FTP.

I did that for a selection of 10 athletes over 2 seasons. These athletes are mostly competitive amateur through to elite level (but no full time pros), and have power data for >> 90% of their rides.

This is the summary:

What I find interesting is the variance as measured by the modelled FTP in WKO4 is larger than I would have expected.

Over 10 riders for 2 seasons each, we have an average maximum to minimum modelled FTP ratio of 1.23, meaning the peak modelled FTP for a season was, on average, 23% higher than the minimum modelled FTP for that same season.

Good luck trying to pick out one specific reason for performance changes when models are showing this sort of variance in FTP.

Do I think their FTP really varies that much? Well possibly not quite, but then with time I am seeing mFTP to be quite reliable indicator, provided the quality of input data is good. One erroneous power spike can mess with the power-duration data and mFTP value. Indeed when there are large changes in the modelled power-duration metrics, it's often due to input data error than anything else.

For reference I also provided an indication of their annual TSS (~27,000) and average CTL (~77 TSS/day) for this selection, just to show that theses are riders on average have quite decent training volume. I would not rely totally on those TSS values though, as they probably need an audit of the FTP history applied in WKO4 to generate them, so I consider them as just indicative for now.

I also looked at my own data for 2009 and 2010, and my annual mFTP variance was 15% each year, so a bit lower than the average reported above.

Now of course with all such things one needs to consider context, and quality of the input data. For now that's a study beyond what I have time for.

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Looking under Froome's hood

A little over two years ago I wrote about the relationship between four key underpinning physiological parameters that determine a rider's sustainable power output:

• VO2max
• Energy yield from aerobic metabolism
• Efficiency
• Fractional utilisation of VO2max at threshold

I don't propose to repeat myself, so go here to read that first if you'd like a more detailed explanation.

Data on some physiological testing by Chris Froome was released earlier today, so I thought I'd put a marker on one of the charts I posted in that earlier item to see where he sits.

I took the data from the cyclingnews article linked below:
http://www.cyclingnews.com/news/chris-froomes-physiological-test-data-released/

In it the key 2015 data are listed as:

Weight: Test: 69.9kg, TdF: 67kg
VO2max: Test: 84.6ml/kg/min, TdF weight adjusted: 88.2ml/kg/min
Threshold power (20-40 min): 419W
W/kg: 5.98W/kg, TdF weight adjusted: 6.25W/kg

So given we are talking 20+ minute power, a fractional utilisation of 90% of VO2max for an elite athlete is not unreasonable, so here's that particular chart, and overlayed on that is a pink box defining the area covering a range of VO2max from 75ml/kg/min to 95ml/kg/min and gross efficiency range from 19% to 25%. You'd expect elite cyclists to be somewhere in that range.

Froome's estimated TdF VO2max and 20+ minute power/mass are then shown by the green dot:

What can we infer from this?

Not a lot really, other than the data are in line with what you would expect for a rider with the performances of a grand tour winner. Certainly the physiological values are in line with historical data on plausible physiological parameters for elite aerobic endurance athletes.

As far as informing on doping status, as with power meter data and climbing power estimates, it tells us SFA. In any case I doubt it will change anyone's opinion either way.

Edit: here is a link to the lab report:
https://www.gskhpl.com/dyn/_assets/_pdfs/ChrisFroome-BodyCompositionandAerobicPhysiology.pdf

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Where will Wiggo wind up?

Chart showing the progress of the UCI hour record since 1893 (click on it to view a bigger version):

The chart shows all the successful hour records recorded by the UCI. It doesn't show failed attempts.

The blue dots show the incremental increase in what is the absolute furthest distance attained.

The red dots show successful records for various categories of hour record but that did not surpass the furthest record for all categories up to that date.

For example, up until the early 1990s, the UCI had separate hour record categories for:
- amateur and professional riders
- above and below 600 metres altitude
- indoor and open air tracks

As a result, there were six categories of hour record for the period from about 1940 to the early 1990s.

And of course there have been bike/equipment regulation changes at times, most notably after Obree's and Boardman's records in the mid 1990s,

So where will Bradley Wiggins end up?

I'm pretty sure it'll be another red dot and not get close to Boardman's 1996 record and I doubt he'll beat Rominger's 1994 mark either. But he will likely beat Alex Dowsett's record (52.937km - the currently recognised record) by 1km or so.

I think anything above 54km will be very tough going. 54.5km perhaps if things go well. Closer towards 55km if everything is perfect.

Power 440-460W

CdA - who knows?

Say 0.200m^2.

Such a power range would net him around 53.5 - 54.4km at typical air density. 

On a low air density day that range would stretch to 54.5 - 55.4km. 

Weather forecast suggests low air density is unlikely although there is plenty of chat that they will raise the air temperature a lot, even up to 32C (yikes!).

So if velodrome air is heated to say 30C and air pressure is say 1020hPa, then at that power range and guessed CdA, the distance for a well paced effort will be in the 53.9 - 54.7km range.

Of course his CdA is the big unknown. Looks like he's been doing some work on it.

Drop that to 0.190m^2 and we can add about another lap (260 metres) to those estimated ranges.

Best of luck to Wiggo!

Read More......

Looking under the hood

Today I'm going to take a look under the hood of Functional Threshold Power and explore the relationship between four key underpinning physiological parameters that determine FTP:

• VO2max
• Energy yield from aerobic metabolism
• Efficiency
• Fractional utilisation of VO2max at threshold

I've prepared a chart (sample below), which I will come back to later to explore this relationship a little more. Those with an existing understanding of the relationship will likely need not look further than the various charts posted, and as normal you can click on them to reveal a larger version.

Scroll down and you will see several versions, showing the relationship between FTP, VO2max and GE at fractional utilisation of 75%, 80%, 85% and 90% of VO2max.

Our maximal sustainable aerobic power is primarily a function of our VO2max,
our gross efficiency, and our fractional utilsation of VO2max at threshold.

For everyone else, I'll introduce and explain the significance of each of these factors and then give an example of how changes affect the power we can sustain.

VO2Max

VO2max testing by the AIS
as reported by Katya Crema

VO2max is a measure of the maximal rate at which we can utilise oxygen. Normally it's also defined by how it is measured, e.g. during an incremental exercise test where the power demand is increased at a specified rate, and how long VO2max is sustained for, so that we don't rely on instantaneous peak values. Measurement of oxygen utilisation requires laboratory testing equipment that records the flow and composition of the body's respiratory gases while performing exercise.

VO2max will typically occur eventually when attempting to sustain a power output above functional threshold, and once reached is typically not sustainable for more than a handful of minutes. How quickly we attain a state of VO2max, and how long we can sustain it are determined by how far above functional threshold power we are attempting to ride, our fitness, power profile and some other individual characteristics.

VO2max is expressed in units of oxygen consumption per unit time, either absolute, i.e. litres of oxygen per minute, or relative to body mass, i.e. millilitres of oxygen per kilogram per minute.

e.g. if a 70kg rider has a VO2max of 60ml/kg/min, it means that for every kilogram of body mass, they can maximally utilise 60 millilitres of oxygen per minute, or 70kg  x  60ml/kg/min  /  1000 ml/litre = 4.2 litres of oxygen per minute.

VO2max sets the ceiling on our aerobic performance capability and as such is a reasonable determinant of our endurance performance potential, however it's not a particularly good predictor of performance. All one can really say is that to be an elite and/or professional cyclist, you will need a relatively high VO2max, typically in excess of 70ml/kg/min, however higher doesn't necessarily mean you will perform better. It just gets you a ticket to the game, but won't necessarily mean you'll be good enough to play.

That's because power output matters far more than how much oxygen we happen to use to generate it, and VO2max is not the sole factor in how much power we are capable of sustaining. And of course there are other factors beyond physiological that determine performance, but all things considered, in endurance cycling power output is a major factor.

VO2max is trainable, although it is also significantly genetically determined (perhaps half), so in a sense, you need to have chosen your parents wisely. You may not see much improvement in absolute VO2max from training, or quite a lot, or something in between. Trainability, which differs by individual and also has a sizeable genetic component, and starting fitness level are big factors. Improvements in VO2max of around 10-25%, can occur in a matter of months. Of course one can attain improvement in VO2max when expressed per unit of body mass simply through weight loss.

There have been some phenomenally high VO2max values occasionally recorded, well into the 90+ ml/kg/min range, with young Norwegian cyclist Oskar Svendsen reported to have the highest recorded VO2 max of 97.5ml/kg/min. Greg Lemond, the American professional cyclist of the 1980s and early 1990s and winner of three Tours de France, was reported to have had a VO2max of 92.5ml/kg/min, and he responded in an interview once that it was in the 92-94ml/kg/min  range. I don't vouch for the validity of these numbers, merely pointing out some of what's been reported.

Energy yield from aerobic metabolism

Citric acid cycle as per wikipedia

Without oxygen we'll die (well duh), and it's critical for sustaining our body's energy production needs, and just like many means of releasing energy through chemical reactions (e.g. rockets, campfires, internal combustion engines and many other chemical reactions), our bodies also use oxygen to help release useful energy from fuel.

Of the biochemical reactions that release energy aerobically (i.e. with oxygen), we utilise two primary fuel sources, one being glycogen and the other free fatty acids (from our body fat stores). Most of the time we obtain energy from both, but when exercising at near threshold power and above, we are heavily, if not solely, reliant on glycogen to meet the energy demand.

Using glycogen as fuel, our body can release around 21.1 kilojoules (kJ) of energy per litre of oxygen. We get a little less from aerobic fat metabolism, around 19.8kJ per litre. So in general the energy released per litre of oxygen utilised is somewhere around 20-21 kJ depending on the mix of fuel substrate used.

We do have the means to also produce energy without oxygen (i.e. anaerobically), however such energy pathways are available to us only for brief periods and are not sustainable, but are good for rapid energy demand (e.g. sprinting) and to supplement the energy provided via aerobic means when the energy demand exceeds our ability to supply via aerobic metabolism alone. Due to the limited supply of such energy though, such efforts are of short duration (seconds to minutes).

Here's a summary of the main energy pathways used by our bodies. It's a fairly complex topic (e.g. just look up the Kreb's Cycle for starters), and is one for the physiologists to chat about over a beer, beer being another key fuel substrate and one of the major food groups, along with burritos, donuts, caffeine and chocolate.

Efficiency

The basic definition of gross efficiency (GE) is the ratio of work done during the specific activity to the total energy expended and expressed as a percentage.

In the case of cycling, GE is the ratio of the energy delivered to the cranks of the bicycle to the total energy metabolised by our body. Sometimes this is referred to as gross mechanical efficiency (GME), just to emphasise the relationship between the mechanical work done at the cranks, to the total energy metabolised by the body.

There are a number of definitions of efficiency in exercise physiology and if you'd like to read about them in a little more depth, then this paper: The reliability of cycling efficiency by Lukh Moseley and Asker Jeukendrup (MSSE 0195-9131/01/3304-0621/$3.00/0 ) is a reasonable place to start and I'm sure there are others. That's just the PubMed extract which doesn't say much about the definitions, but you can find full text version online if you search, and it's a little more instructive.

As highlighted in that paper, trained cyclists typically perform with a GME of around 19-24%, meaning that of the energy metabolised, only about one-fifth to one-quarter actually ends up propelling us forward on a bike. The balance is mostly given off as waste heat, with a little energy of course needed for life support functions!

Have a quick think about that: for every watt you generate at the cranks, you are geneating around another 4 watts of heat. A rider performing longer intervals at 300W is generating somewhere in the vicinity of 1200W of heat! This is precisely why cooling is so vital for performance, as we need to dissipate that excess heat in order to continue to perform at that level.

To measure efficiency we need to measure both our energy output to the bicycle (via a power meter) and our total energy metabolised, which is done via the same respiratory gas exchange analysis equipment used to test VO2max, indeed the two factors are usually measured from the same test. Perhaps one day there will be practical and portable means to measure energy metabolised in the field but for now, the only reliable means is in the lab.

Gross mechanical efficiency can be acutely affected by things such as fatigue, hydration status, glycogen levels, environmental conditions and so on. Chronically we have an efficiency level granted to us by genetic inheritance plus however much we can manage to improve over the course of our cycling careers.

Efficiency is trainable, in particular over the long term, perhaps not to the degree of VO2max or lactate threshold, however other than by performing large volumes of training over many years, it's not totally clear whether or what specific training one can perform to achieve short term improvements. There's lots of noise from many purveyors of a fast performance gains to do with changing pedalling "techniques" or equipment choices, and while some are based on sound science and worth paying attention to, some are far more speculative, while others fall into the snake oil category.

One thought on efficiency is it's related to mix of muscle fibre types, as slower twitch fibres tend to operate with greater efficiency than their faster twitch cousins (which are better at utilising rapid energy release but less efficient metabolism), and so a fast twitch dominant sprinter is more likely to have a lower overall gross effiiency than their diesel mate. The science demonstrating the scope for chronic improvement in efficiency is a bit more limited than for VO2max and lactate threshold, and longitudinal studies are not common.

One thing efficiency is not: it isn't how you pedal, nor the way in which you apply forces to the cranks. It would really help if manufacturers of various cycle training aids would stop misusing the term - it confuses people no end.

Fractional utilisation of VO2max at threshold

This is the percentage of your VO2max you sustain when riding at your functional threshold power. It might range for example from 75% of VO2max to ~ 90% for very fit riders and is an aspect of our fitness and performance that is very trainable over the short to medium term, and can be the element of fitness we gain the greatest improvement from, but it is also something that can be developed over many years of training.

To briefly illustrate, if two riders have an FTP of say 300W, and one is doing so at 80% of their VO2max, while the other at 90% of their VO2max, the rider at 80% of VO2max has quite likely far more scope to further improve their threshold power output.

Ok, so how do all of these factors relate?

There have been suggestions VO2max and efficiency are inversely correlated, although I'm not sure how firm that relationship is, if indeed it exists across the board, or if there is a sound physiological reason why that might be the case.

The maths of the relationship is pretty straighforward though:

FTP = Energy per litre O2 (J)  x  VO2max (ml/kg/min)  x  Fractional VO2max at threshold (%)  x  GME (%)  /  60 (seconds/minute)  / 1000 (ml/litre)

e.g. 

Energy per litre of O2: 20,900 joules (say, refer above for details)
VO2max: 65ml/kg/min (say)
Fractional utilsation of VO2max at threshold: 80% (say)
Gross mechanical efficiency: 22% (say)

FTP = 20,900J  x  65ml/kg/min  x  0.80  x  0.22  /  60,000  =  3.98W/kg

So, now we can see that FTP is a function of those four variables, although we can reasonably assume the energy released per litre of oxygen at threshold is fixed, leaving us three variables to tinker with, and of course you can flip that equation around to ascertain any of the variables chosen given the other factors are known or assumed.

So back to the chart I posted earlier, see this example:

FTP W/kg for a fractional VO2max of 80%.
This rider has scope to improve threshold power by
increasing the fraction of VO2max they can sustain at FTP

On the vertical axis is gross efficiency, the horizontal axis VO2max, and plotted are curves representing various threshold power to body mass ratios, in steps of 0.5W/kg from 2.5W/kg through to 7.0W/kg, for a rider whose threshold power (FTP) occurs at 80% of their VO2max.

So for instance, if a rider has a VO2max of 65ml/kg/min and a GE of 22%, then we can see these intersect at around the 4.0W/kg curve. They could also have the same FTP with higher VO2max but lower efficiency (and vice versa).

If this rider managed to improve their fractional utilisation of VO2max at threshold from 80% up to 90%, then this is what happens with the very same GE and VO2max:

FTP W/kg for a fractional VO2max of 90%.
At same GE and VO2max, rider can sustain a far high power output

All of the W/kg curves have moved down and to the left. Now we can see that the same combination of GE and VO2max results in an FTP of ~4.5W/kg. 

Check the maths: 20,900 x 65 x 0.90 x 0.22 / 60,000 = 4.48W/kg.

To attain the same level of power improvement without increasing fractional VO2max utilisation, it would require an increase in GE from 22% to a little under 25% (not very likely in the short term), or alternatively an increase in their VO2max from 65ml/kg/min to 73.4ml/kg/min.

Of course, one can attain a power improvement via a combination of all three factors, although it's more likely that one will improve VO2max and/or their fractional utlisation of VO2max at threshold in the short to medium term, than attain any short to medium term improvments in gross efficiency.

So what's possible for the freaks exceptionally talented?

The magical troika of high VO2max, high fractional VO2max utilisation at threshold, and a high GE may well be exceptionally rare in the same individual, if it's possible at all, but given a GE of 25% is not exactly unheard of (higher GE values have been reported although some question the validity of those measurements) and we have seen VO2max values reported well beyond 90ml/kg/min, and very fit cyclists will have a fractional VO2max utilisation of ~90% at FTP (I'm not sure if or how much higher that may potentially go), then if we have another look at the above chart and see where a combination of 25% GE and a VO2max of 97.5ml/kg/min intersects, it's beyond the 7.0W/kg curve. It's actually at 7.6W/kg. Yikes.

Of course no-one we know has been measured to have an FTP near that level, certainly no-one without blood/oxygen-vector doping assistance, but just what is actually physiologically possible or plausible? Who really knows?

And what's possible for us mere mortals?

Well have a look at the chart, find a threshold power line near where you are, or would like to be, or perhaps a VO2max value if you happen to know it, and see what various combinations of W/kg, VO2max and GE are required. See what happens at different fractional VO2max utilisation levels.

I dislike setting limits on what's possible, but it's clear that if your highest VO2max is say 60ml/kg/min and there's limited scope for pushing that up much further, then I hate to be the bearer of bad news but you will never see an FTP of 6.0W/kg, but 4.0-4.5W/kg is definitely within reach. 

What does it mean for training?

Well while it's fun to occasionally look under the hood to see what elements of physiology we need to work on to improve our threshold power, one doesn't really need to get too hung up on these individual factors, as they are all inter-related and power measurement not only conveniently condenses the outcome for us but is the primary physiological measure of performance that matters. So keep training hard and smart. There's still no short cuts to improved fitness. There's also no real need to rush out and get your VO2max tested, the power meter will tell you most of what's important.

When you are the limit of  your current improvement in power, then perhaps it might be time to consolidate those gains, and then consider whether a change of tack is necessary to make the next step up. Do you need to give your VO2max a bit more attention, or have you still room to move in lifting your fractional VO2max utilisation? How well do you personally respond to such training?

We can gain some insight into these relationships though inspecting our power profile, and relationships between shorter and longer range power outputs, or for example, how our Functional Threshold Power and our Maximal Aerobic Power relate.

Of course a focus on training to improve one element can and does impact the others, but not always. Perhaps some additional weight loss is required. What one chooses to focus on may be different for a seasoned pro than a local club amateur, but the principles are the same.

OK, enough of that for now - it's time to close the hood, get back into the saddle, and rev the engine!

OK, here are the same charts for each of the fractional VO2max levels I mentiioned earlier:

75% of VO2max:

80% of VO2max:

85% of VO2max:

90% of VO2max:

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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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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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l'Alpe d'Huez - one for the mortals

About this time last year I posted this item about ascent times of leading professional riders up l'Alpe d'Huez and what power to body mass ratio would be required to do that.

There's a chart which shows the relationship between ascent time and power to body mass ratio (watts per kg - W/kg). It also provides an indication the impact of wind can have on climbing speeds.

Times for leading riders since 2001 are shown on the chart.

The guys over at the Science of Sport blog referenced it in a post here, after seeing it on a cycling chat forum I posted to recently.

Well for a bit of fun (and considering the Tour de France is heading up the Alpe in a few days), I thought I'd post a follow up chart which covers the power to body mass ratio for the rest of us mere mortals.

Here it is (click to embiggen):


It's not a hard chart to read.

Want to ride up l'Alpe d'Huez in 1 hour dead? Then you'll need to be able to sustain around 3.75 W/kg, give or take depending on the wind. If you are 70kg, then that's around 260-265 watts.

If you know your sustainable power is 3.4W/kg, then you can expect to get up the Alpe in around 66 minutes.

In calculating these values, I've made a few assumptions (listed on the chart), although the relationship between speed and W/kg on steep climbs is not particularly sensitive to those assumptions.

After power and mass, wind has the biggest impact on speeds when climbing. Hence the two extra lines for head and tailwinds.

At my best form*, I would expect to climb it in around 56 minutes.

How fast have you been up l'Alpe?


* My power to body mass ratio for 1-hour at best is ~ 4.2W/kg (based on my racing power at the UCI World Cup this year), but I have to allow a bit of extra mass for my prosthetic leg. I'll get to do it one day.

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Aero Profiling

Earlier this week I posted this item about power to aerodynamic drag ratio for the individual pursuit.

I mentioned in that item a table prepared some time back by Andy Coggan, which was similar to his power profiling table (which lists typical power to body mass of riders - W/kg for various time ), except it showed Functional Threshold Power (FTP) relative to aerodynamic drag (CdA) - W/m^2, instead of as per the original power profiling table.

The top end of the table would represent what's required to set a World Best Hour record.

In this way, a rider's power to aero drag ratio can be compared against the world's best.

Andy has kindly sent me the data, so here is the table for reference. Thanks Andy.

Enjoy!
(click on the pic to superaeronate)

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Power Profiling - Now and Then

Often I am asked how my cycling power ouput compares now, to before my accident and amputation.

As I progressed with my training during 2009, and as my fitness continued to improve under Ric Stern's guidance, the answer to that question kept changing, mainly as I started to close in on pre-amputation power levels.

Now that I have a full year's worth of data from 2009, I decided to take a look at my annual power profile and see how it compares to pre-amp levels.

Below is aggregate power profile data covering the past 5 years. It shows my best power to mass ratio (W/kg) for each of 4 separate durations for the years 2005 through to 2009:


The power durations shown are:
5 seconds
1 minute
5 minutes, and
95% of my 20-minute power.

Each of these power-durations represents key elements of cycling fitness, with different energy systems being the primary contributor to performance at each duration. That's why it's such a telling indicator of your overall cycling makeup, and an excellent indicator of your relative strengths and weaknesses.

This is provided of course the profile does in fact contain data from best efforts for the duration. Given it's aggregate data for whole years, then I think it's a reasonable assumption. Nevertheless, sometimes the 1-minute column can still be under stated as that usually requires dedicated efforts not often performed in training or racing.

You can read more about power profiling in this original item by Dr Andrew Coggan here:
Power Profiling


So the group of columns on the left shows my best 5-second power to mass ratio for each year from 2005 to 2009. Each group of columns moving to the right covers the other durations, with 95% of 20-minute power shown by the columns on the far right.

What matters with power profiling is the overall shape of the profile, rather than the absolute numbers. The shape in this case indicated by the lines joining the columns, which I have shown for 2006 (orange line) and for 2009 (blue line). I chose those two years as they are the two complete years representing pre-amputation and post-amputation training/racing data.

I notice a few things:
- the overall shape of each line is similar
- my short duration power has taken a large nosedive
- my longer duration power to mass ratio is actually higher than previously attained

This clearly demonstrates that it's my sprint power that has suffered the most from my lower leg amputation, yet the predominantly aerobic power durations (5-min and 20+ min) have not.

This suggests a few things to me.

One is it's an example of how we are not force (strength) limited when cycling at aerobic power levels, since even though I have lost significant leg musculature and with it strength, I have still been able to generate the longer duration power.

Another is that the lack of a lower leg muscular-skeletal system has a significant impact on sprint ability. The lower leg matters a lot more in the generation of short duration sprint power, than for longer duration aerobic power.

What can I make of this information? Well for one I no longer have the sprint I used to, yet I am as likely to be as well set up for the end of a race as I was before, since I have the engine to deliver me there. But now I lack the finishing ability. My strategy and tactics in racing may need to be modified a little as a result.

I can still work on improving my sprint of course (all track/roadie riders should) but I would say that reclaiming pre-amp sprint power levels is not going to be anywhere near as "easy" as it was for aerobic power durations, if in fact it is actually possible.

It also points to me reconsidering what events I may in fact focus on. They may change as well.

Plenty to ponder with a power profile.

What's yours look like?

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Testing is Training....

One of the Pithy Power Proverbs is "Training is testing, testing is training." by Andy Coggan. It's really a way of saying that one shouldn't be afraid of "mucking up their training" in order to schedule a performance test, since by their very nature, tests are very high quality training efforts anyway.

Many think that one needs to taper or rest up significantly for such tests and that's what "mucks up your training". Well yes and no. A lot depends on where you are at in your training.

Certainly at lower Chronic Training Levels (less than ~70-80 TSS/day), then a significant rest up really isn't necessary. Sure, don't go and smash yourself the day before hand but not too much more concern should be had with resting up. At high CTLs then perhaps a little more recovery time is in order before tests.

So over the past couple of weeks I have been doing some testing. Coach Ric figured it was time we checked under the hood to see whether I was running a 2 pot screamer, a Wankel rotary, a turbo 4, the family 6 or a big donk of a V8.

Before I get to how that panned out, here's a quick summary of my training over the past 7.5 weeks in the form of a Performance Manager Chart:


You can see that following a break after the National (Apr-09) and Oceania (May-09) Paralympic road race championships, my CTL had fallen significantly (was at ~ 70 CTL at time of the champs). I had expected it to drop a bit as I was taking a week off and then some easy riding but a series of events led to quite a long interruption to my training of about 8 weeks. Initially I had problems with my new walking prosthetic and after that was finally sorted and I rode again for a couple of weeks, I took then ill for a while with some weirdo viral bug. So CTL dropped to ~ 32 TSS/day with lots of time off the bike.

OK, so once I was healthy enough to train and had my prosthetic sorted, it was time to ramp it back up again. In the period leading up to testing my CTL was rising at a little over 6 TSS/day per week, which you can see by that steady upward march of the blue CTL line in the chart above.

So after that five week block of training, I had a week with a 16km time trial (TT) scheduled for a Tuesday and a Maximal Aerobic Power (MAP) test on the Thursday. Those days are shown on the chart above.

Here's the power trace from the 16km TT:


Well it's actually a bit shorter than 16km at 15.3km. Four laps of Sydney's Centennial Park (a gradually undulating course) but it's close enough for the purpose and is a testing ground I have used many times. I did the TT on my road bike (no TT rig at the moment).

299 watts for 24:29 (37.4 km/h)
Peak 20-min: 301W
CTL: 69
TSB: -31

That's 92% of my pre-injury PB power (326W) on the same course.

Testing Part II was the MAP test on the Thursday. Here's the result:

MAP is the maximal 1-minute average power from a ramp test to exhaustion using a ramp rate of 20 or 25W/min (depending on category of rider). I use a 25W/min ramp protocol.

MAP: 410 watts
CTL: 70.6 TSS/day
TSB: -25.2

That's an all time PB MAP result for me and is 103% of my pre-injury PB (399W).

Note the Training Stress Balance (TSB) at the time of both tests - both what I would call significantly negative (meaning I was quite fatigued), yet I still produced post-accident PB power levels and in the case of my MAP, well I'm a little astounded at setting an all time best just 14 months after I tried to pedal on a bike again for the first time since my accident.

So astounded was I on the day that I decided to make a special effort to re-check the slope calibration of the SRM power meter on my ergo bike. It was slightly off and my numbers were lowered by 4W (initially I had 414W).

Here's a look at my previous MAP test results over the past two and a bit years. Also marked are the months where I had my accident and amputation, as well as when I started back on the home trainer:


One can wonder - was I fully developed as an athlete beforehand? Has that skewed the results?

Well probably not fully developed (I sure had plans of becoming more powerful), but I wasn't un- or under-developed either. I had an FTP well over 300W and a CTL of the order of 100 TSS/day. Up to that point I had attained podium in 6 team pursuit championships including a championship win in state record time, 1 x podium at teams time trial championships, 1 x podium at criterium championships as well as an open criterium win, and a podium at the national masters track points race champsionships (right before my accident).

Now I don't know what specific conclusions you can draw from this n=1 study, but as an athlete who has severals years of power meter data prior to and after a lower leg amputation I think it will no doubt be of interest to those who study the performance implications for such injuries and the use of prosthetics in cycling. Clearly there are many high performing athletes using similar prosthetics.


One outcome of testing is to establish or validate an estimate for Functional Threshold (~1-hour TT) Power (FTP) . On the basis of these tests (the TT in particular and my recent longer threshold tolerance intervals), I have reset my FTP to 280 watts as of the day of the TT. It was previously set at 270 watts.

So what now? Well one thing to note is the ratio of FTP to MAP.

Currently that puts me at a ratio of 280W / 410W = 68%
My previous best pre-injury I was 315W / 399W = 79%

That's quite a remarkable difference in the ratios and I'm not entirely sure of the reason.

Typically the ratio of FTP to MAP is in the range of 72% - 77%, so on both accounts I fall outside the typical range (it happens). Pre-accident I was always somewhere around the upper end of the range. Everyone's ratio is different and can vary through the course of training and be due to your physiological and power profile (anaerobic capacity, VO2max, % of VO2max one can sustain at threshold and so on).

One way to think of it is MAP is like your aerobic ceiling* and FTP is how close to that ceiling you are able to get when riding a TT. So in this sense, it suggests that my roof is plenty high and that I have lots of room to further improve my TT power before I starting bumping my head. Which is good!

* of course there is an anaerobic component to MAP as well (as indeed there is in shorter TTs albeit a smaller overall contributor to total energy output) but examination of hundreds, if not thousands, of MAP tests have shown it to be a reliable indicator of aerobic performance potential.

My testing isn't actually finished. Since I will be targeting the 3km and 4km individual pursuit (and track TT 750m and kilometre) over the next 6 months we have also scheduled a trial 3km pursuit effort for this coming week. That'll be fun.

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