Power meter accuracy specifications

As far as I know, Verve cycling is the only power meter manufacturer that publishes somewhat trustable accuracy specifications:

  • Power range: 0–3000 Watts
  • Cadence range: 10–200 rpm
  • Accuracy of cadence: ±1 rpm
  • Accuracy torque: ±0.2 Nm accuracy for measurements below 20 Nm, and ±1% of actual readings for measurements above 20 Nm (ask for our Accuracy Certification)
  • Power: Can be calculated from any cadence value within the range at any torque
  • Power update rate: Every rotation

Surprisingly, even SRM only gives one single number, although added with a blunt statement:

Accuracy  ±1% (Scientifically Proven)

No proper scientist would state an error number like ±1% without specifying for what range of conditions that number is valid. So much about science.

Assuming cadence as a function of power:

accuracy - cadence

the specifications of verve systems gives this error:

accuracy - power error in percent with cadence error

 

which translates into W like this:

accuracy - power error in Watt

In other words: Verve cyclings’s Infocrank has an accuracy of about 2% above 110W (like most other power meters) and about 5% or 2W at 50W. Well, that doesn’t seem really significant to me, but still: hiding (including just forgetting to mention) such a fact doesn’t seem right either, and I strongly feel that since most power meters are engineered using somewhat similar principles, a lot of manufacturers have some clarifications to make.

(added new section from here on)

The interesting point here might actually be: How did I get from a 1% torque error to a 2% power error, if power and torque are related linearly?

accuracy - power error in percent without cadence error

Now we see: without cadence error, 1% torque error of course results in 1% power error, but a cadence error of just 1 rpm (or 1% at cadence 100!) will add another 1% to the power error. So, if you have a cadence sensor with an absolute 1 rpm error range, you’d want to pedal quickly to get more accurate power figures …. well, that’s just a joke, but, measuring rotation accurately is a really important factor here, and the more wheel rotations of a hub-based power meter compared with crank- = pedal-rotations would make a hub-based meter easier to engineer for high accuracy.

(I hope I didn’t make any calculation errors and would be happy to be corrected.)

Comparing responsiveness of Power Meters

Note (May 2018): I should probably re-collect, re-analyze and re-write this to reflect what I learned about the ANT+ power meter protocol and through programming for ConnectIQ. Looking at the data now, the agreement between instant and average power of the P1 seems to indicate that it simply had zero drops – whereas the others had much more. Wished the WASP would record drop rate … or I might add that to my CIQ datafields.


One issue I see with power meters – when you go beyond just using it for day-to-day training and start to compare the data with that from other power meters or over a longer time period – is that although most manufacturers give some number about the accuracy of their devices, usually in the 2-3% range, they really give you just that single number.

Given that it is difficult to engineer – sorry, I won’t explain this deeper at this moment – a power meter that is accurate at very low power, at very high power, for quick changes, and over a long time period, that single number is not at all useful. Neither for comparing different power meters when shopping nor as a guideline about how much you can trust your data.

I will write at some other time about other accuracy issues; in this post I will briefly compare Pioneer’s second generation (crank-based), Garmin’s Vector2 and PowerTap’s P1 (both pedal-based) about how they respond to changes in power, or, in other words, their delay time from measurement to output. Although I do have a background in engineering and science, these are just simple tests of single devices bought through common sales channels, so I don’t claim that this data is in any way representative: It’s just what I got when I rode some time. (Note: Unluckily I am lacking a hub-based power meter, which would really be nice to have for such a comparison. I still need to check how useful the power data from the KICKR are: I know that their power data isn’t that accurate as I’d like but they could still be helpful if their sampling rate is high enough.)

First up, Pioneer in ANT+ mode versus Vector2, recorded using North Pole Engineering’s WASP unit (note: this allows me to record synchronized at a 1 sec resolution without relying on any specific head unit) on a KICKR (note: I used TrainerRoad to design a ride including constant sections at different power levels, ramp-up and downs, as well as 15 sec spurts at different power levels. The KICKR was controlled from TrainerRoad with PowerMeter feedback from the Pioneer in automatic mode). These are one-to-one comparisons, so there is no way to know which if any of them is right. In most cases, both have delays and both data have some error.

When looking at the whole ride, the power numbers seem to match more or less:20150808 Vector2 vs Pioneer allIf one starts looking at the details, it seems that the Pioneer is slower to respond to ramp ups than the Vector2. Interestingly, this hold only for the up-ramp and not for the down side:

20150808 Vector2 vs Pioneer rampWhen looking at 15 second sprints, the delay seems negligible but the maximum power numbers are lower for the Pioneer:

20150808 Vector2 vs Pioneer sprint

Next, comparison of Pioneer vs PowerTap P1. Again, no significant difference on a larger scale:

20150807 P1 vs Pioneer allAgain, we see that Pioneer has some delay on the up-ramps. The P1 might even be a bit faster to respond than the Vector2, but it also seems to have a bit more spikes.

20150807 P1 vs Pioneer rampHere, the first three sprints show that the P1 is more responsive than the Pioneer. Again, there is also a difference in maximum power values.

20150807 P1 vs Pioneer sprintA natural question now might be: What happens if someone does extremely short power bursts? It seems you can get away with 1 to 2 seconds of very brief bursts while the Pioneer is undecided whether that’s a burst or a noise spike. Although not noticable from the data alone: It seems that the Vector2 is slow to get down to zero and often didn’t go completely down as well, so, whereas the up-ramp of the Vector2 is more trustable than the Pioneer (which shows smaller than real power values because of it’s delay), the Vector2 may show inflated power values because of the delayed down step.

20150810 Vector2 vs Pioneer bursts(Section starting here added on August 11th)

Actually things are not that simple, for two reasons.

First, the ANT+ power meter protocol has to get power meters of fundamentally different designs like hub-based (where power is calculated from torque and wheel rpm) and crank-, spider- or pedal-based (where power is calculated from torque, cadence and, in the case of pedal-based meters, crank length) as well as head-units of different levels of sophistication (just displaying instant power, or being able to do calculations and recordings) under one roof. So the standard actucally includes different ways of communication. For example, in one such communication protocol, there is a data field for instant power, meant for simple displays, as well as accumulated power, from which you can either calculate average power (as the difference between current and last accumulated power; that’s I believe what the WASP does to calculate the average power data field) or correct accumulated statistics like TSS.

Second, current crank-, spider- and pedal-based meters all rely on a torque sensor and a basic physics formula that requires cadence to calculate power. (That’s also the issue with oval rings which kills the assumption of a constant cadence.) For these kinds of short bursts, even using accelerometers instead of a simple magnet that only triggers once for every crank rotation, it may be difficult to sense cadence accurately. (If they did, they’d all be able to provide correct data for oval rings, too.) So, these short bursts are likely outside of the not-published working conditions of these power meters. (Even Verve cycling which gives more information about working conditions than the other manufacturers doesn’t say how responsive their cadence data would be in such a condition.)

So, with this knowledge and including all relevant data, the above graph looks like this:

20150810 Vector2 vs Pioneer bursts with average power filled with cadenceAlthough not visible from the data alone (if you trust me with this), I had stopped pedaling between the bursts, so, cadence data from both units are messed up, meaning that all the power data doesn’t look trustworthy to me (unless they are internally calculating with some other cadence data that they don’t send over ANT+). On the good side, Vector’s average power data seems to avoid the effect of sticking to high power values even after the burst has ended that I observed with their instant power data and on the Garmin Edge display. (Actually, they might have designed instant power this way so that you don’t miss data when briefly looking at your computer during a ride.)

Now, when we compare the Pioneer in its proprietary pedaling-monitor mode, we get this:

20150811 Vector2 vs Pioneer in pedaling monitor modeIn pedaling-monitor mode, the Pioneer’s data cannot be recorded with the WASP, so I had to export from Cyclo-Sphere and convert the .fit file to .csv using GoldenCheetah, and manually align them as good as possible (note: a perfect alignment is not possible with devices that are not synchronized).

Now, the interesting thing here is that the Pioneer’s cadence data from Cyclo-Sphere looks much better than the one I got via ANT+, probably also contributing to power data that is closer to data from the Vector, although still lower, and there is not much of a delay compared with Vector.

Since I’d gotten myself already knee-deep into this, I also briefly swapped pedals and compared Pioneer in ANT+ mode with PowerTap’s P1.

20150811 P1 vs Pioneer corrected colorsA few interesting observations: The P1 does not distinguish between instant and average power in the ANT+ protocol – I don’t actually know what the intention was for creating a protocol that allows separate numbers for them. The length of the bursts in the P1 data seems correct too, although I don’t have any data to back that up. On the other hand, the Pioneer seems to distinguish between cadence data available via Cyclo-Sphere and the “instant” cadence data from their ANT+ stream, possibly resulting in their Cyclo-Sphere power data to be more likely than the power data from their ANT+ stream.

Follow-up (August 15th):

Here is a set of Vector2 vs Power2Max comparison data. Slightly different setup, with data set taken on a roller and not the KICKR.20150815 bursts p2m vs vectorObservations: Genereally there is quite some difference between Vector and P2M. Between 21s and 60s I did some single crank rotations, which are much better picked up by the P2M. For two and more crank rotations, there is more agreement between the two in both start timing and power value, but vector seems to take longer to notice stops. P2M does not distinguish between instant and average power, while vector again has some larger differences between them.

Conclusion (revised on August 11th):

For normal riding, all three power meters seem pretty much good enough to me.

If anyone wants correct data for very short bursts, there is a fundamental limitation here: A crank- or pedal-based power meter depends on how exact it can measure cadence during such a brief burst, and even using accelerometers or mounting the cadence magnet to utilize polarization change for higher accuracy when sensing crank position.

So, my recommendation would be, to either try a hub-based power meter (although I admit I’ve never used one before and have no idea how they’d perform under such conditions) or go with the P1 (which seems to provide honest data, an impression that also somewhat aligns with their claim of using a large number of sensors (8) and enough computation power).

Personally, I highly value the realtime pedaling analysis data that the Pioneer power meter gives when combined with their head unit, which can be helpful for understanding and changing pedaling technique (whereas I personally found the advanced metrics of the Vector and Garmin’s visualization on the newer Edge units less useful, but that might be just myself). Therefore, as a total package, I’d still thing that the Pioneer will have the most impact on someone’s cycling performance although only in combination with their head unit and if you ride regularly indoors and are concerned about pedaling technique. (Yes, choices are never easy.) And, if you really need data from bursts using Pioneer, may be look at Cyclo-Sphere data and not their ANT+ stream.

A common way to reduce noise is to use something called a Kalman filter or to do at least some simple averaging; both necessarily delays the data output. It seems the Pioneer has been engineered more towards reducing erroneous spikes than the Vector2 and the P1, or it’s simply looking at a longer time window given that it was fundamentally designed as a pedaling monitor and averages less over crank rotation.

Other thoughts:

The general consensus in cycling data collection to use 1 second sampling seems old, considering how much communication bandwidth and memory capacity is nowadays available and that most power meters are actually sending at a higher rate. A higher rate could simplify simultaneous correct recording and undelayed display under all conditions including quick bursts.

But even at 1Hz, one should expect “instant power” to be instant possibly including spikes and “average power” to give correct data when accumulated over time. Power meter manufacturers should make clear what their specific conditions for accuracy are.

If I was to design a power meter from ground up, I’d possibly integrate a high resolution optical rotational encoder in the bottom bracket that together with accelerometers would enable giving exact rotational position and velocity, solving both oval ring issues as well as accuracy under bursts.

The comparison was also restriced by the WASP iOS app to be able to record only at 1Hz and not all the data that the power meters are sending, which would have allowed for more exact assessments of delay time. I was not able to check yet whether the WASP’s ANT+ to WIFI bridge functionality filters down to 1Hz (I need to sign their NDA first!) or whether it is a restriction of their iOS app. I was neither able to find a PC or Mac application for simply recording all ANT+ traffic.

The processing involved here is possibly somewhat comparable to high ISO noise reduction in digital cameras which reduces noise patterns but also image details. It might be good if power meter manufacturers made these noise reduction levels user-configurable as in higher-end digital cameras, empowering the user to choose the processing that is best for their usage.

List: Websites (June 2015)

TrainingPeaks: Storage for all training data, use to plan and check training by fitness, fatigue and training load measures from power meter data. I am not a gifted athlete and I learned that the closing the right training intensity without under as well as over training is not easy. This should help proper management of my training intensity.

TrainerRoad: Sheer endless variations of training rides for all kinds of purposes, duration and intensity, scaled to your FTP, structured into training plans. Clients for iOS, Win and Mac. Speed-based virtual power for those without a power meter yet. Frequent updates and excellent customer service.

CyclingAnalytics: Neat visualization of power data. Scripting for own analysis. Statistical comparison with data of other members for example of max average power and power weight ratio. For me, this replaces participating in races as a goal or for bench marks.

Cyclo-Sphere: Post-ride analysis of pedaling technique using time and scatter plots. Separate analysis for sitting vs standing.

Garmin connect: Time and map plots of rides.

Information sources: DC Rainmaker blog, Jürgen Pansy’s Blog, Cycling Center Dallas, Moxy forum, etc.

List: Bicycles and trainers (June 2015)

Bicycles:

Cannondale Super Six 5 (2012): designed at a time when comfort didn’t count much, this bike changes force very effectively into speed, but is uncomfortable and tiring over long distances. Luckily that is not a problem when riding mostly indoors. At 71.5 degrees head angle, which is surprisingly one of the steeper head angles for a 44cm size frame, handling is nimble enough and not bad.

Panasonic custom geometry track bike (ordered): this is my first custom ordered frame with a fully self designed geometry. Taking mountain bike forward geometry as an inspiration, I combined a long top tube with a short stem and balanced rider weight by employing ultra short chainstays as well. With such sub 50cm frames, toe overlap can be a problem leading most designs to head angles of 70 or less degrees, but this design achieves a relatively steep head angle of 72.5 degrees, which should be comparable in handling to the classic 74 degrees on the longer wheelbase of a standard, say, 55cm frame, with zero toe overlap.

Turbo trainer: Wahoo KICKR

Roller: TruTrainer

List: Sensors (June 2015)

Data is the starting point of every analysis. Here’s a list of sensors I am currently using.

Power meters:

-Power2Max (first gen. after minor change): very basic, reliable, quick reaction to intensity changes. Spider-type.

-Pioneer (second gen.): pedaling technique visualization (useful especially indoors, less so on the road). Crank-type.

-Garmin Vector 2: some advanced metrics (though of doubtful value during ride). Pedal-type with pod.

-PowerTap P1 (ordered): pedal-type.

Biometric sensors:

-SmO2: Moxy: NIRS sensor giving raw data of (remaining) O2 saturation in blood flow of muscles. Currently the only sensor for home use to actually show what’s going on in the specific muscles, although it is still a somewhat indirect measure that requires interpretation. Potential to show you how to warm up most effectively, what systems your weak points are, whether you are really at your limit, etc. Learned for example that my left and right legs are so completely different even when riding at a 50% to 50% balance. Possibly revolutionary to training.

-HR: Garmin (chest, ANT+), 4iiii (chest, ANT+ and BT), Mio (arm, ANT+ and BT).

-LT: BSX insight: was only able to do few rides so far: I do not seem to be the only one with connection issues. Concept of measuring LT might not be the holy grail anymore when you can now have SmO2 data instead.

Data collection:

-NorthPoleEngineering WASP-B with iOS app.

The thrill of riding indoors

To many, indoor cycling is the most boring way to ride a bike. Some will admit that it’s nonetheless a very efficient way to train, like during the winter months, even if they’d actually prefer rain and snow.

But how could indoor cycling be thrilling?

I actually agree that riding on one of those aero bikes you see unused in hotel fitness rooms which never fit my body size, or riding on a mediocre trainer with its low inertia and constant resisting feeling like walking through mud, or even the average roller where you simply put your road bike on and have the freedom to pedal and balance or fall, is suicidally unbearable.

Until I found a nice enough roller and a somewhat acceptable turbo trainer. Which kinda turned my world upside down.

No stinking cars driven by narcisstic maniacs, no waiting for red lights that screw your exercise and data, no waiting for the elevator to get to the ground floor in the first place, no concerns about weather, no pollens, no summer heat, no darkness, no pedestrians, no broken parts that you can’t repair without special tools, no punctures that make you walk home, no need to keep in mind to spare some power for the last uphill road to get home, no need to carry food, no helmet, no gloves, no feeling of creeping along compared to the thrills of my motorbike and racing car, no stupid races with random cyclists either real or on STRAVA.

Instead: Exact exercise protocols and beautiful data for most efficient training. Ability to watch and analyze graphs of biometric and power data in realtime on multiple real computer displays to see the effects of adjustments to e.g. pedaling technique as they are implemented. Ability to read on a kindle or to study classical music recordings to make double use of my time. Ability to ride until exhaustion on every ride without fear of not making it back home. Or to watch TV if I’m really lacking motivation.

Absolutely thrilling and beautifully efficient at the same time.  Everytime. On every ride.

Some might question how indoor riding could be thrilling at all and how anyone could be serious about performance without racing. I’d probably have thought similarly myself. Until I learned that that was all stupid bias and prejudice.