Saturday, June 02, 2018

A new home: Watt Matters ±

From here on this blog will have a new home:

Watt Matters ±

Nothing like a nice new coat of paint to freshen things up.

Most posts in the blog archive should automatically redirect.

See you on the other side!

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Wednesday, May 30, 2018

VAM & W/kg estimates

Just wanted to post a few charts for reference.

First chart compares W/kg estimates based on the same vertical ascent rate (VAM) for each of two methods:

Dr Ferrari's formula and the mathematical model described in the paper by Martin et al:

The plot compares the W/kg estimates for each of three gradients: 6%, 8% and 10% for a range of VAMs between ~800 m/h and ~2000 m/h. A red unity line is included for reference.

To make the variances between each method a little easier to see, the following chart plots the difference between the two W/kg estimates, again a line for each of the three gradients: 6%, 8% and 10%.

I don't have a lot to add to the charts other than to say the Dr Ferrari formula is a bit of a quick and dirty way to estimate W/kg from ascent rates but it does not consider a range of variables included in the Martin et al formula.

In particular we can see the estimates can vary quite a bit depending on both gradient and on VAM.

Same plot as above but this time with the variance expressed as a percentage of the Martin et al method estimate:

I would certainly place more faith in the Martin et al method, and that was also the conclusion of this paper by @ammattipyoraily and @veloclinic (Dr. Mike Puchowicz) which examined the different methods of calculation compared with actual power meter data from a large number of data files.
Estimating climbing performances of professional cyclists: a larger dataset

For consistency with the analysis in the paper above I chose rider mass and CdA in the middle of the range from that paper. I do not know what Crr assumption were used though. I settled on 0.005.

A Crr value in the range 0.004 to 0.005 would be typical and the impact on calculations of a difference between 0.004 and 0.005 is equivalent to adding 0.1% to gradient (and an associated bias error in W/kg estimates of about the same order).

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Sunday, October 15, 2017

Kona power meter usage trends: 2009 to 2017

Update for 2017 based on the Lava Magazine bike count data. Previous posts links showing trend data up to 2013, 2014, 2015 and 2016 are here:

This chart shows the trend in relative usage of power meters since 2009, along with the total number of bikes (click  on images to see larger versions):

Here are the numbers. Data in order of year of introduction.:

In the nine years of this data being available, power meter usage has risen from 17.3% of all bikes to 58.7% of all bikes, although the growth slowed  this year, and was well below the longer term trend of an increase of 5.5 percentage points per year.

Finally the year on year change data and order ranking:

Not too much change to report compared with last year.

Stages is the big loser this year with the largest fall in both overall numbers and in relative share, dropping one ranking place from 4th to 5th most commonly used meter at Kona.

For another view, DC Rainmaker has this year done a similar analysis.

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Friday, July 28, 2017

TdF Speed Trends 1947 - 2017

The 2017 edition of the Tour was a pretty quick tour in terms of average speed, and I suspect the relative lack of mountains played its part in that. Having said that, I can't specifically say whether of not the number of ascent meters was substantially different, it was just an impression from looking at the overall route.

Last year I posted an item with various charts about speed trends for the Tour de France. If you want to know more about the charts, what they mean, the data and where I stole the idea for some of them from (thanks Robert Chung) then have a read of that earlier post - it's not overly long.

So here's an update of the charts to see where 2017 falls.

First the overall speed trend by year:

2017 was the second quickest Tour on record.

How about average stage distance?

Right in line with the overall trend of shorter average stage distances. This may well continue as the ASO experiments with more short punchy stages, plus the ITT distances were less than in most recent tours.

Here's the average speed v overall distance plot with each decade colour coded:

We can see 2017 is still within the speed v distance cluster of 21st century Tours.

And the residuals plot?

Here we see the 2017 edition is right in line with the expected trend.

Now the obvious question about the overall trends relate to two other factors besides overall distance, one being impact of doping during different eras and the other being total ascent meters during the tour as a proportion of total distance.

The doping stuff has been done to death here and elsewhere and there really isn't much in these plots to definitively say much about it anyway. You can look at the peak in residuals in the period of 1990s to 2000s and say "ah ha! doping!!" but then you'd also have to explain the other peak in residuals from late 1950s to 1960s. No EPO or blood bags back then. So while doping has played its part, it's not the only or whole story.

About the amount of climbing though, in the last year I made an attempt to work out the number of climbs ascended each Tour. I went through online archive data to count the number of times each col was climbed in the tour for each year. It was pretty laborious research.

I got to something like 750 different mountains in the database and counts for each year, but was unable to complete the project of identifying the data for each (distance, vertical metres, gradient), nor did the archive identify in which direction the mountain was ascended. which matters quite a bit for some climbs.  Add to that the archives were not consistent in which climbs were included in the stage descriptions - and climb categorisation (i.e. HC, Cat 1, 2, 3, 4) has evolved over the years.

It became apparent that unless there is a source available that provides the actual race routes, then attempting to work out the meters ascended for each year was a pretty futile task. I gave it a go though.

Perhaps someone out there has actual race route data going back to 1947? If that's the case then we can map them using current technology to come up with a pretty decent indicator of ascent and descent metres for each edition of the Tour.

Conclusions? Not a lot with respect to 2017 edition. It's pretty much on trend.

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Tuesday, January 24, 2017

Do I need a power meter?

On the Slowtwitch forum recently, a frequently asked question was posted about whether one needs a power meter. This question has come up regularly on cycling and triathlon forums for the past decade or more and there have been a number of posts and articles written by plenty of power meter advocates over the years (including myself) that have laid out the case.

In the specific thread there were a number of responses, mostly with a heavy focus on training to a specific intensity, pacing, that sort of thing. All of which are fine, but in my opinion these responses are not overly compelling reasons to use a power meter.

It's actually a really good question, and I don't think many people have adequately answered it. So I suspect it might be a theme worthy of blogging about from time to time.

I'm not going to delve into it deeply today, but thought I'd keep a copy of my forum response here on the blog for easy reference, and perhaps in future posts I'll explore some of the reasons given by myself and others and whether they stack up as sound and valid for using a power meter versus an alternative.

Here is the question posed in that thread:
I have been read the time crunched triathlete. Carmichael makes it sound like you can get pretty good result from a HR monitor. Sooo do I really need a power meter
My response is reproduced below:

You don't get good results from any device. You get good results from executing sound basic training principles of consistency, frequency, progressive overload with recovery as needed, specificity and individualisation of your training and development needs.

Most half decent training plans and basic monitoring tools (a watch, RPE, HR and even power meter used in a really basic manner) will get people some way towards executing these principles, e.g. it's very rare that someone I give a 2-3 month training plan to and who executes it does not improve, however such plans sacrifice some level of load management optimisation, specificity and individualisation.

Power meters (good ones at least) and the data they produce provide you with objectivity in assessing the training you are actually doing v. what you think you are doing. Neither RPE or HR can do that.

I mean far more than monitoring your work rate at any particular moment but right though to considering what you are doing in a more global sense. How what you are doing now (or previously, or this week/month etc) fits in with and impacts your season and even your entire athletic career.

Power data also helps one to better understand their current and historical physiological capabilities and its relationship with and response to your training, your physical attributes (e.g. aerodynamics), the specific demands of your races or goal events, and can help assessment of some riding/racing skills/execution, which leads to individualising and optimising your training and development program to suit your specific needs.

As a communications and logging tool, the objective power data balances the subjective feelings about how you are going. Both matter and it's more useful when subjective and objective are assessed together.

And interestingly, and somewhat in opposition to what many seem to think, power meters can actually provide you with a lot of freedom in the way you go about your training since once you recognise what's actually important you realise there are many ways to skin the training cat. Applying good training principles does not automatically imply overly regimented training.

To use power meters wisely and to a reasonable proportion of their potential for performance improvement requires an investment on your part to learn how to understand and apply the data.

Or you could just use it as a fancy speedo, effort monitor and ride logger. If that's all you intend to do though, I'd save your money and just follow a half decent plan and keep things fun.

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Monday, October 10, 2016

Kona power meter usage trends: 2009 to 2016

Update for 2016 based on the Lava Magazine bike count data. Previous posts links showing trend data up to 2013, 2014 and 2015 are here:

Here are the numbers for 2009 through to 2016 (click  on images to see larger versions):

And below is the breakdown showing proportion of bikes with and without power meters, and the split for each power meter as a proportion of all bikes. e.g. the slice of pie for the Powertap is 175 Powertap power meters which is 7.9% of the 2,229 bikes in the the Kona bike count.

2016 continued the long term trend of an increase in use of power meters by Kona IM athletes, and for the first time ever a majority of bikes (57.4%) were fitted with a power meter.

So the pie is getting bigger for all power meter manufacturers. at least as a share of Kona athletes. How indicative these numbers are of broader power meter trends is hard to say.

So how are they all doing as a share of that increasing Kona power meter pie slice?

Below are the year on year trends, ranked by total share of power meters:

Quarq and Garmin Vector maintained their lead as the most used power meters and like most brands each saw a small increase in their share of the total power meter pie. However their relative share of the bikes fitted with power meters took a hit with Quarq dropping 3.4% to 23.7% and Garmin Vector down 3.0%, to 17.8%. These were the biggest falls in relative share of all the major power meter brands. While this continues Quarq's trend from the previous year of a decline while still maintaining top place, it's a reversal of fortunes for Garmin Vector who showed strong year of year relative share growth last year.

The big mover up the rankings was Powertap which like most brands improved its share of all bikes but more importantly their share of bikes fitted with a power meter was up 6.4% to 13.7%  (nearly doubling their 2015 share). This is no doubt due to the introduction of Powertap's new power meter models, in particular the P1 pedal based meter, which complements their well established hub-based and new C1 chain ring-based power meters.

This reversed the trend in recent years for Powertap, whose numbers were probably a little under represented as the Powertap hub is the one that most likely to be used as a training wheel for some athletes but not as a race day wheel. Unfortunately the Lava Magazine data does not parse the Powertap data into model sub-categories so we can't know exactly the trends for each model, however the pedal count shows 82 bikes with Powertap P1s, which means hubs and chainrings (if any) make up the 93 remaining Powertap models. In 2015 Powertap hubs numbered just 78 units.

Rotor and Pioneer also saw their share of all bikes and all power meters improve, although from a smaller base.

Stages share of the Kona power meter pie has stabilised after strong growth from 2014 to 2015, with a slight drop in their relative share of power meters.

Power2Max is declining in their relative share of power meters used at Kona and this is the second year they have experienced such a decline.

SRM continues its slow drop in relative share on all bikes and of those fitted with a power meter.

A few new power meter brands make a guest appearance but none have really exploded onto the Kona scene.

Overall observations

These numbers continue the broad trends of previous few years:

i. Power meter usage as a proportion of all bikes used at Kona continues to rise at a rate of nearly 6% year on year. This has been a consistent trend since 2009. If the trend continues, we should expect that in 2017, approximately 63% of all bikes will be fitted with power meters.

ii. Most growth in usage comes from newer power meter models.
For 2016 the majority of growth came from Powertap with 45% of the growth, Rotor 21% and Stages 11%, with the rest making up the remaining quarter of the growth (SRM being the only model with negative growth).

iii. after an initial period of growth, models tend to stabilise their Kona athlete market share for a year or so before beginning a gradual decline in share

iv. no power meter model dominates Kona athlete market share. Quarq maintains its place as the lead choice being fitted to 23.7% of bikes with power meters.

Some caveats:
- obviously this is a sample of athletes that qualified and participated in Kona and hence we can't simply project these trends as necessarily being representative of the overall market.

- the  athletes that qualify obviously changes from year to year.

OK, so that's the latest on power meter usage trends from Kona. See you in 2017!

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Thursday, August 11, 2016

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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