Funky things with a Power Meter #77
OK, maybe I haven't demonstrated 76 other funky things with a power meter but then I have discussed or demonstrated the following:
- Aerodynamic drag field testing with a power meter (no, that's not racing your bike while dressed up like Kylie)
- How to determine your Maximal Accumulated Oxygen Deficit (which is kind of like what we're capable of doing without breathing - like what all used to do as kids)
- What impact pacing has on an hour record attempt (i.e. what happens when you get it wrong - it's spectacular in its painfulness)
- What impact pacing has on individual pursuits (i.e. what happens when you get it wrong AND get it right)
- Pulling apart a power file from a race and analysing what we can learn from it and how it reflects what happened (i.e. a chance to brag and show off a power file when you have a win)
- What's the significance of Normalised Power vs Average Power ("crikey that was a hard race, how come my average power was so low?")
- Comparing a training stress metric vs kilojoules (what the? or how can we expect a sprinter to lose weight - ha!)
- Maximal Aerobic Power testing: how, why and what for? (IOW going to failure for a good cause)
- Time trial testing (i.e "puhleeaase let me put out more power today!!")
- Intervals - time trial power (how to suffer for extended periods)
- Intervals - VO2 Max and here (how to suffer for shorter periods)
- Intervals - Lactate tolerance (how to suffer for next to no time at all!)
- Torque readings (or "why the hell is my power meter not working right? #%$^#@$")
- What happens to us when we train "this hard" (can someone just put it on a picture so I don't have to work it out, thanks?)
- Using a season's worth of power meter data to analyse what happened and why (otherwise known as the "I really need to train more" moment) - The good and the ugly versions.
- Using a power meter and associated tools to successfully guide a comeback to cycling part I and part II (my friend had cancer and want to return after surgery to compete at the worlds)
- Using power data to predict performance (like, who's going to suffer the most in today's team time trial?)
OK, that's only 17 dots, so this will really be Funky thing # 18. I should add that most of what I've written is based on tools and analysis developed by guys far cleverer than I but is simply there to demonstrate a number of power training ideas and principles. If anything up there sparks your curiosity, then just click on link or look it up via the index at the right to find posts grouped into various categories.
So what's Funky Thing #18 all about?
The Chung Method (Virtual Elevation)
Well it's linked to aerodynamic field testing but using a different methodology, known as "The Chung Method", developed by a data analysis guru and regular power training forum contributor Robert Chung. It also acts as a proxy for developing an elevation profile of a loop course without the aid of an altimeter (now that's the really funky bit). It works best when you ride a course that passes the same point more than once (the more times the better). What am I on about?
From analysing the power & speed data from a power meter file for a typical ride, estimates for both the coefficient of drag-area (CdA) and coefficient of rolling resistance (Crr) can be made. These key measures indicate the degree to which air resistance and the road surface serve to retard our forward progress or how hard we have to push on the pedals to overcome these forces at any given speed.
The method works by using the equations of motion for a cyclist (well a slightly cut down version), with a few assumptions thrown in (such as a low wind day). If we know a little more data about the course and the conditions, the estimates of CdA and Crr derived and elevation profile obtained can be pretty good. Good enough that changes in rider position, equipment (or conditions) can be readily detected and that the elevation profile be correct to within a few metres.
And it does not require the usual protocol for field testing, that of doing multiple runs under highly controlled conditions. Just use ordinary power meter data from a loop course. It helps if you have a near windless day (a little wind is OK).
Now Robert's paper which discusses
this method in detail can be found here:
So I thought I'd have a go and with the aid of a spreadsheet posted on one of the training forums I frequent, I applied it to a sample of my own data.
I picked a training file from Boxing Day 2006. Here is a pic of the training loop I rode that day, a popular local training ground - Centennial Park in Sydney. Grand Drive is a 3.8km roughly circular loop, flattish. There is also an option to climb up a hill to the Ocean Street gates, then across to the Paddington gates before descending back down to Grand Drive, which adds about 2.5km to a loop. Sydney-siders would be pretty familiar with the Park.
Here is the graph of my power and speed file for the day chosen. It was a tempo effort of 90-minutes duration where I did laps of Grand Dr with a climb up the hill to Ocean St every second lap. You can see that by and large I kept my power output within a range and let my speed vary (not that that's necessary for this method - it's just what I happened to do that day).
Using this data and the spreadsheet with the funky formulas which use the equations of motion, here is the chart produced showing the ride elevation profile of my ride in Centennial Park that day. On the chart I show the CdA and Crr estimates needed to provide a consistent elevation for the same points in the ride. Since I already knew the elevation difference from the lowest point to the highest point in the park, that helped me adjust the CdA and Crr numbers such that the profile provided an accurate representation of the course (to within a few metres).
That's quite remarkable if you ask me. Now it was just a training run, not a time trial, so I was on my training bike, probably riding with my hands on the hoods, maybe occasionally on the bar tops going up the hill. A cool morning too, so probably a bit of extra clothing on for warmth. Hence the relatively high CdA of 0.384. A Crr of 0.005 was settled on and seems to be a reasonable estimate for the mostly decent quality hotmix/asphalt surface in the Park.
You will note some variations, particularly the opening kilometres and the final lap, where the profile varies from the consistent elevations shown from km 6 to km 40. I suspect that during this middle section of the ride I was using a consistent position on my bike.
For the final lap, since the derived elevation data doesn't match the other laps, it appears that either I rode in a different position, changed clothing, conditions changed (perhaps the wind) or I was mixed in with other riders. I'm not sure, I can't recall. But the change is very distinct with this method and is one way of assessing the impact of changes to equipment and/or rider position.
The forum thread where this sparked my interest in having a look at it myself is here:
So what's Funky Thing #19 gunna be?
As Robert Chung would say, "Hmmm...."