Why look at pedaling data? Short term cycling performance – excluding long term training effects for now – is, in my understanding, about:
Physiological efficiency in generating muscular force that works on the contact points – pedals, handlebar, saddle – with the least short term side effects like fatigue. As there are different pathways to generate muscular force – like burning fat or carbs – this includes finding the most efficient combination of fuels to sustain the targeted output over the targeted duration.
Effectiveness of how you apply that muscular force and your body mass to turn the cranks. Tangential force on the cranks is effective and results in rotation but radial force does not. Pulling at the handlebar as a counterforce to increase your pressure on the pedals contributes to crank rotation but gripping the handlebar strongly does not. Body mass can be utilized rather statically as downward gravity or more dynamically as an inertial force to keep the cranks spinning at high cadence.
Mechanical efficiency in turning the crank rotation into forward or uphill motion with the least amount of friction. Here, friction includes mechanical losses in the drivetrain, rolling resitance of the tyres and shifting air out of your way. If your ride goes downhill, you might include how to brake as little and keep as much momentum as possible.
The second item undeniably includes pedaling technique. But is it trainable? And, does pedaling data available on some power meters provide actionable information? It may not provide immediate answers but I think it provides a lot of food for thought.
There are a few different ways to graphically visualize pedaling and a few different ways to condense the characteristics into single numbers.
These are ride averages from a recent endurance climb on the Fuji-Subaru-line tollroad which leads halfway up on Mt.Fuji, Japan, and is the stage for an yearly hillclimb race with almost ten-thousand (!) participants. While the numbers – cadence, power, balance – should require no explanation, the graphs might be new to you. The left column shows average for seated pedaling while the right shows standing. The vectors in the graphs at the top show the direction and amount of the applied force at each of 12 crank angles whereas the botom graphs shows the amount of tangential force on the cranks, useful for judging for example where you had your peak (here it’s around 3 o’clock for sitting and 4 o’clock for standing) or where the forward rotational force (red) turned into counteracting resistance (blue) . There is also a left-right unbalance in the location of the peak when standing (later on the right). The percentage number in the center of the graphs is what pioneer calls “pedaling efficiency” defined as the ratio between tangential force and the sum of tangential and rotational force averaged over whole crank rotations. It would be 100% if the force vectors are perfectly tangential but does not require the amount of the force to be constant over the whole rotation.
Out of the advanced pedaling metrices available on cycling computers, this one makes most sense to me as a quantification of the effectiveness of using body weight and muscular force to turn the cranks. Pedaling smoothness defined as the ratio of maximum tangential power to average tangential power – which is a crude way to express smoothness by the way – is probably pointless as it ignores that human biomechanics makes downward pushing easier than pulling up. Torque effectiveness defined as the ratio of forward power against total power is slightly more meaningful but often reaches 100% at which point further improvements cannot be quantified. My only criticism with Pioneer’s pedaling efficiency is that it’s actually not measuring efficiency but effectiveness and should therefore be called “pedaling effectiveness” and not “pedaling efficiency”.
That said, those three metrices are mostly correlated as visible in the following graphs:
Pedaling efficiency, torque effectiveness and pedaling smoothness vs timePedaling efficiency vs torque effectiveness (left leg)Pedaling efficiency vs torque effectiveness (right leg – TE saturates at 100%)Pedaling efficiency vs pedaling smoothness (left right average)
Getting back to the Mt.Fuji ride, the complete ride was about 2 hours uphill at a comfortable pace of between 3.0 and 3.5W/kg, with stops at a traffic light, the toll booth, then stopping to add a long sleeved shirt after gaining about 1000m in altitude and a brief rest before the final incline. The light green section is what the power meter noticed to be standing pedaling. There is usually more detail that becomes apparent only if you zoom.
Climbing up Mt.Fuji
The following scatter plot of pedaling efficiency against power shows where most of my pedaling was: I rode mainly between 150W and 225W in power – a comfortable endurance pace centered around 75% FTP – and 40% to 60% in pedaling efficiency. What we learn is that some points including standing pedaling (in green) are below 40% and some go as high as 80%.
Pedaling efficiency vs Power (Mt.Fuji, whole ride)
For this ride which went practically uphill all the time, the following efficiency vs altitude scatterplot shows how efficiency changed over time. We see a decrease in efficiency but which is probably small enough to be negligible.
Efficiency vs altitude (Note: this ride was uphill throughout)
If we drill down into details, you will notice that an interesting characteristic of my pedaling is that I alternate in about 20 second cycles (almost unconsciously) between downward-focused pedaling at around 50% efficiency and a more circular pedaling style at around 80% efficiency.
I believe I’ve had this custom for quite a long time. I have no information about how common such pedaling is. It’s somewhat comparable to alternating between sitting and standing to switch muscles used and avoid fatiguing the same ones. It doesn’t have to be a bad thing, but I seeing this fact, I wonder whether to not care about this or attempt finding a middle path between the two styles or increase the time spend in one or the other for example to increase overall efficiency or to reduce overworking my calves which tend to cramp in long rides. I’ve added a rough visualization (below the tangential/radial pedaling force vs crank angle graph) showing which muscles are used at each crank angle in the hope of noticing something but couldn’t make anything of it yet. I probably need to record activation of specific muscles using EMG sync’ed to crank angle to identify how I am using which muscles in either of these pedaling styles. Alternatively I could use SmO2 sensors (which I have already and which I needn’t sync with crank angle) to quantify muscle from O2 levels. We’ll see if I can get the data collected sometime during the winter.
Another observation would be that the slight imbalance in power towards right leg also coincides with higher efficiency on the right, and also that left and right look similar for downward pedaling on the force vs crank angle diagram but there is a significant imbalance for circular pedaling in tangential force between 6 and 12 o’clock and around 3, 6 and 11 o’clock for radial force.
For standing pedaling at similar power, the efficiency is significantly lower at around 40%, with peaks of tangential power occuring later at around 4 and 5 o’clock as more body mass rests on the legs and is used to push the pedals downwards. It’s somewhat interesting that there is less left-right imbalance in power but there is a significant difference in radial force at 6 o’clock, where it seems that I am less able to pull my body weight off my left pedals. Reasons for this could be that I am less dexterous with my left leg or that I am actually using more of my body weight when pushing down the left pedals to cover less muscular force.
Downward pedaling (Standing) @ FTP
So much from the Mt.Fuji data set. To complete the picture with high and low power data, the following data is from an indoor ride.
The efficiency vs power scatter plot now covers the whole range of my cycling power up to about 900W or 16W/kg and we see more easily that higher power generally conincides with higher efficiency and that I saturate at about 80% (for all wattages).
Pedaling efficiency vs power
First, let’s look at a high power sample at about 400% FTP or 14W/kg. Compared with the previous sample of standing pedaling, efficiency is much higher at above 70% compared with about 40%. Left and right leg have to counteract to put down this much force and which naturally leads to more circular pedaling.
Circular pedaling (standing) @ 14W/kg
Cadence is lower than my usual 80 to 90rpm when sitting and similar to the previous sample at FTP. Does this affect efficiency? For sitting, efficiency does not seem to be affected by cadence.
Efficiency vs Cadence, whole Mt.Fuji ride
How about standing though? There I see two extremes – low efficiency at low power/cadence and high efficiency at high power/cadence but nothing in between. This might or might not indicate potential for improvement and makes me realize that I haven’t done standing high cadence drills for quite some time.
Efficiency vs Cadence, short high power intervals, green dots are standingEfficiency vs Cadence, endurance ride, green dots are standing
Also note that I had about 2.5kgs of wind jackets etc in my waist bag which made me aware of up-down motion of my waist. I consciously attempted to avoid up-down bouncing of that bag and felt that the overall impact on pedaling was positive. I probably should compare pedaling data with and without that weight to find out.
Finally a low power example at about 50% FTP. It is quite difficult to perfect motion at such low power – not only for myself but also for others. But that shouldn’t mean that it’s a necessity and I wonder if that can be overcome with better body awareness and control.
Uncontrolled pedaling @ 50% FTP
All the above was mostly on the hoods, and a complete analysis should look at pedaling in the drops and in aero bars separately from this. I actually noticed an imbalance in the range of motion of my legs when riding my TT bike, which led me to choose short 150mm cranks to stay inside the range of my left legs. (Unluckily there is no 150mm version of the Pioneer power meter.) I learned afterwards that range of motion can be increased by training, so it would be interesting to see if that would not just benefit aero position but also resolve some part of the imbalance in left and right efficiency on a road bike.
Thanks for reading. Could have missed something or misinterpreted, so would appreciate any thoughts.
When I bought my first power meter – a Power2max, later renamed “classic” to distinguish from subsequent models – some four years ago, I mainly depended on DC Rainmaker’s blog to guide my choice. In the days since, I have tried five more power meters – a Pioneer, two Power2max (S and NG), Vector 2 and P1 – out of engineering interest. The first one now has been retired because of unexplainable spikes that appear only outside when going downhill, but on the way, I learned a few things that are not often mentioned.
Accuracy and Precision
I’ve written about this in some other posts already so I will spare the details here and just summarize: While most manufacturers make some accuracy claims about their products, there is currently no standard about how accuracy is measured. This means that a statement like “within 2%” doesn’t say more than there was at least one testing condition under which it showed less than 2% error in comparison to some reference about which we don’t know much. and isn’t comparable between manufacturers. No manufacturer I know makes statements about the delay of its power meters and crit racers seem to complain when they accidentally switched to a power meter with more delay than their previous one. If the “delay” is same for power in- and decreases it wouldn’t change the overall data, but often those characteristics differ and together with some built-in averaging short bursts can get swallowed.
When comparing power meters, graphing them separately on a power vs time graph of averaged power only allows for rough visual checking, meaning that you’d notice only if it’s really far off – and it can be difficult to spot trends. Using a peak power graph doesn’t make a scientific comparison either – it will show how much a peak power graph will be off but not much more.
A scatter plot graphing the output of one power meter against that of another is slightly more scientific as it makes spotting trends easier although you need to be aware of the fact that the sampling isn’t synchronized so the dots won’t line up perfectly – still how they distribute can give some hints about the behavior of each meter. If you draw a trend line for all dots and that line has an angle of 45 degrees, you know that both meters are accurate relative to each other. If the line is more like a curve, you know that at least one of them is not really responding linearly to your power. If all dots are very close to the trend line, both meters have high precision (i.e. small random noise) and a similar delay. If really a lot of the data points are far from the trend line, it’s probably not just a sync issue or a delay issue but really noise in the data of at least one of the compared meters. A (cumulative) histogram of the power difference between two meter can also tell more about the meters than a time sequence comparison.
A potential problem with left/right separate meters is that their left and right gauges cannot be calibrated against each other, so there may be a bias in the reported balance that goes unnoticed. A combined meter that reports pseudo balance based on down- and upstroke cannot report the true balance but does not suffer from separate calibration; on the other hand, a biased sensor for crank position could be, though less likely, a problem. Ideally one would combine separate and combined strain gauges and calibrate the three sensors continuously against each other.
The ANT+ power meter protocols – there are many depending on power meter type (i.e. mainly location) complicating things – allows for sending data multiple times per second and SRM used to utilize this in both their power meters and head units, but recently they seem to have dialed back on this feature. More frequent data points may help getting a more accurate picture of short bursts.
You will probably use several different power meters over your lifetime and sometimes you will want to look at long term trends, so, while some argue that precision (meaning little random noise) is more important than accuracy (the average having no bias from true value), I think that’s only half the truth. A power meter really should be both precise and accurate. But by how much?
In my experience, over the long term, the difference between my power meters falls within 2%, but for single rides the difference could be 5%, or in rare cases even 10%. That’s a lot. So, don’t believe or depend too much on either precision nor accuracy nor (lack of) latency.
Calibration
In rotational systems, power is calculated as the product of torqueτ and angular velocity ω. Both vary over a crank rotation and need to be measure multiple times. Angular velocity is commonly sensed using acceleration sensors and an optional magnet to improve accuracy. Torque is sensed by strain gauges that measure the length of the underlying material. That length changes due to thermal expansion and applied forces.
The first calibration step is a zero reset, i.e. determining the readout of the strain gauges without any applied weight at the current temperature and deals with thermal expansion. This is usually enough for day-to-day calibration.
To be complete, zero reset should be followed once in a while by checking how the material reacts to applied forces. Due to structural changes in the material – for example metal fatigue in crank arms – and chemical changes in the glue that attaches the strain gauges or some errors during production that affect long term durability, this might change from what was measured and stored in the power meter during factory calibration.
A few power meters (like SRM, Garmin and Pioneer) can display on their respective head units either the currently measured torque or force. This means that you can hang your own static, calibrated weights to check accuracy and then store a linear correction parameter if necessary. This is generally good, but depending on the characteristics of the inaccuracy, a single linear factor might for example undercorrect for some power ranges while overcorrect for others and not be sufficient to correct completely. If the power meter/head unit does not support this, the best you can do is comparing with another simultaneously mounted power meter and on suspicision sent in to the manufacturer, but that means you won’t have your power meter for a few weeks every few years.
Temperatures may change during a ride. One approach is to trigger automatic zero resets in moments where there is no force applied. Obviously this can go wrong if the power meter misjudges whether or not there is force. The other approach is to have a built-in temperature sensor and a look-up table so that it learns over time what the zero reset values for any temperature should be so that it can choose values from the look-up table.
Advanced metrics
While some won’t stop noting that advanced metrics haven’t been proven scientifically to benefit cycling performance or only in rehabilitation after some injuries, it’s mainly a question of which metric, what purpose and the capabilities of the user in utilizing the data. If you love trusting and doing what your coach or some book tells you and trust your LBS or fitter with finding the best position for you, then you might not need any of this.
But if you are scientifically minded and think that just using your body and not your brain defeats the purpose of being alive, don’t let yourself be discouraged from choosing a power meter that will give you more data, though of course you have to decide which data you want/need.
The Pioneer pedaling monitors can record and visualize not only tangential force but also radial force though only on their own head units and support some interesting visualizations on their cyclosphere portal. This is somewhat similar to what some bike fitters use and can be really useful when optimizing your position on your bike. Unluckily exporting that data into other formats for your own analyses is not supported.
The torque efficiency supported by left/right separate power meters like the Vector and P1 pedals and the Pioneer in ANT+ compatibility mode has been useful for me as well, while the power phases of the Vector and pedaling smoothness less so.
P1 supports some potentially useful graphical visualizations on their iOS app but the readability and post-ride analysis support leaves a lot to desire at this moment, although it seems they are still working on this.
Torque efficiency can usefully be measured on a left/right separate system. The spider-based Power2max NG reports a combined torque efficiency which I haven’t figured out yet how to use.
Verve and Rotor could be worth looking into but require PC’s for real time analysis and, I believe, recording.
Single leg drills
The P1 didn’t work when pedaling right only as their left pedal which sends out the combined ANT data used to power down. In my most recent ride I noticed it seems to report power to a Garmin Edge during right only drills though cadence disappeared.
Sitting vs standing
The Vector and the Pioneer can differentiate between sitting and standing pedaling when combined with their respective head units. (I still need to check whether that works on a turbo trainer as well.)
Location
There are some conceptual and usability differences depending on the location of power meters. A spider, crank or pedal-based system can be less accurate at low cadences, while a hub-based system can be less accurate – I thing, but I might be wrong with this – at slow wheel rotations. If you are only interested in tangential forces, the Rotor in-power left side crank with strain gauges in the BB axis is possibly the best location for the left side, followed by spider and hubs for combined measurement. Pedals are a difficult location to engineer well, and cranks can be difficult if not designed specifically for power metering as with Verve cycling’s Infocranks. More obvious is the difference in measuring before power train loss (using spider, crank or pedal-based meters) or after (using hubs) – and the power train loss can be significant.
Maintenance
A crank or spider is naturally maintenance free except for checking and if necessary calibrating long term changes over several years. The Vector pedals can be serviced by the user to the point of swapping the internals into Ultegra pedals using their optional kit.
The Powertap pedals and hubs needs servicing to be done at the manufacturer – especially the pedals are a really complicated construction. Pedals are never maintenance free, so you should think twice before getting some that need to be send back for maintenance or at least include fees (the pedals have a two year warranty except for the bearings which are covered only fir six month) and downtime in your cost projection. The issue then is that they don’t have a clear maintenance cost table (yet).
Waterproofing
Waterproofing can be an acute issue in heavy rain and a corrosive issue over the long term. Ideally you’d have a hermetically sealed system with inductive charging and wireless firmware updates. Unluckily, that’s not available (yet) so we have to live with compromises.
Some users seem to have problems with the P1 acutely in heavy rain and with humidity over the long run; I myself have occasionally noticed condensation in the battery compartment. (Note to P1 fanboys: No, I have never done a battery change in the rain.) The contact between the Vector pods and pedals could potentially be an acute issue in heavy rain. Same with the design of the rubber band sealing on the battery compartment of the classic and type S Power2max meters though I believe their electronics is sealed. Their newest “NG” charges over USB and the rubber cap is flimsy but I believe the USB port itself is water proof – although USB ports are known to corrode over time if kept wet.
Robustness
The Vector pods are a potential liability, not just off-road but also when transporting the bike and I really wish for some design changes. The Pioneer magnets (which are not simple magnets but a less common design and required in pedaling monitor mode) can be stripped from the frame when the chain drops – and stick to the chain as I experienced. If you don’t notice that when fixing the chain, I imagine it might play badly with your rear derailleur when you continue riding.
I have been using a few self-made Connect IQ apps on my Garmin Edge cycling computers for over a year now as it doesn’t take much to write a few lines and side-load them onto one’s Edge over USB to try out. Debugging is less convenient than, say, when developing a PC application in Visual Studio, but compared with the early days of Connect IQ it has become much more stable and enjoyable now.
One of my apps – the one above – has become my go-to data field for indoor rides. It helps me judge how hard I am pushing myself during a training session compared with my past performance providing motivation to go hard where meaningful – but also to go slow when recovery was needed. I do believe it helped me reach my goal of 2016 – a FTP of 4W/kg.
In short, it shows the Peak Power of the current ride from start until the current moment, together with all-time Peak Power and a graph of live average power values. To my knowledge, this is the first time this has been implemented in real time. A worlds first!
As this takes up all of the screen of the Edge 520/820, I have also added some of the other basic information onto the same screen.
(Don’t ask me why in this screen shot speed is zero and cadence is an aggresive 186 rpm while I am putting out 96W: These screenshots were captured in the simulator software which allows convenient debugging on a PC, so no one was harmed by that cadence. Still, of course, you could ask Garmin why they think these values make sense – but maybe they thought it’s important to test especially those rare freak cases that should not occur?)
I’ve now put that data field on Garmin’s Connect IQ app store for anyone owning a Garmin Edge 520/820/1000 cycling computer to play with. As with most Connect IQ apps, it’s free for everyone to download and use. The concept behind it, namely displaying and using Peak Power live while riding isn’t common – yet – and possibly slightly more mathematical than the general public would appreciate, so, here are some simple explanations of the thoughts behind it and a few hints about how to use it.
Peak Power
Peak Power (sometimes also called Maximum Average Power or Maximum Mean Power – note that average and mean have slightly different definitions in mathematics and if I had to choose now I’d vote for the former) is the largest average power for some specified interval duration. For example, if you take all possible 20 second intervals of a ride, i.e. from 1s to 20s, 2s to 21s, 3s to 22s, etc, calculate the average power of each, and then take the largest of those, that number is the 20 second Peak Power of that ride. If you are interested in your sprint capabilities, you’ll look at short duration peak power; if you are interested in your endurance, Peak Power over longer durations will give you some guidance.
One well known example actually is Functional Threshold Power (FTP), which is not an invention but simply a naming for 60 minutes Peak Power. If is often approximated by multiplying your 8 minutes or 20 minutes Peak Power with some constant to correct for the fact that you would be stronger for such short intervals on the assumption that that constant would be same for all cyclists.
It is sometimes useful to look at the long term changes of some specific Peak Power to capture a hint of long term trends.
The Peak Power Graph
If you calculate Peak Power for all past rides for all possible intervals from 1s up to, say, 1 hour, and plot them on a graph where the horizontal axis starts from 1s on the left and goes up to the longest interval on the right, the Peak Power will form a decreasing curve.
Peak Power graph in Today’s PlanPeak Power graph in Trainer Road
The Peak Power graph shows a lot more information than a single Peak Power value as it allows to judge, for example, what your specific strengths are, whether you are more a sprinter or an endurance athlete, whether you should add strength training or longer rides, whether you are making progress in what you are currently targeting, etc.
Of course it does not show everything. For example, this graph does not show how fast you recover from an effort and how often you can complete an interval before exhaustion. There is also not one absolute truth about how a Peak Power curve of, say, a sprinter should look like, but Velo magazine from August 2015 for example suggests these examples.
from Velo Magazine Aug 2015
Another way to look at the Peak Power graph could be to divide the interval lengths into zones depending on the dominant metabolism, i.e. the main physiological systems contributing (or limiting) the effort, of the three metabolic energy pathways:
The phosphagen system that regenerates ATP (adenosine triphosphate), the energy source of all physical activity, from stored CP (creatine phosphate) without using either carbohydrates nor fat.
Gycolysis, which converts carbohydrates from either blood glucose (sugar) or muscle glycogen (stored in muscles). In the presence of enough oxygen (aerobic), an intermediate product called acetyl coenzyme A will be further processed in to more ATP, but if oxygen supply is insufficient (anaerobic), it will be converted into lactate.
The Aerobic System uses everything, blood glucose, glycogen and fat as fuels and is most efficient (producing 18 times more ATP from each glucose molecule than anaerobic glycolysis) but is the slowest.
These three – or four if we divide Glycolysis into anaerobic and aerobic – roughly map to interval duration as follows:
5 to 10 seconds: Neuromuscular – Phosphagen system.
30 seconds to 2 minutes: Anaerobic Capacity – Anaerobic glycolysis.
3 to 8 minutes: VO2 max – Aerobic and anaerobic glycolysis.
above 10 minutes: Endurance – Aerobic system.
These mappings are obviously just rough guidelines and neither clear cut nor exactly same across individuals. Still, when I look at my peak power data in comparison with other cyclists – more about those comparisons later on – anaerobic glycolysis seems to be a weakpoint of me while the other system are average. And anaerobic capacity is known to decrease faster than aerobic with age, so, while this could be simply due to age, I also should do something against this.
The results of my last test of the respiratory exchange ratio (or RER) in which one cycles on a trainer in a step protocol with a mask connected to an apparatus that measures the amount of oxygen consumed and carbodioxide generated paint a slightly different picture. I found out that I already have a high RER of about 0.9 at rest which increases to about 1.0 between 50% and 120% FTP and then further to about 1.1 above 130% FTP, coinciding with a generally high level of lactate. From the chemical equations corresponding to the chemical reactions we learn that RER would be close to 0.7 for purely burning fat and 1.0 for purely burning carbohydrates, so, in my personal case, the Aerobic system is rarely really dominant.
The beauty of the Peak Power curve I think lies in the fact that it shows the pure data without all those assumptions and interpretations that are based on generalization that might or might not apply to you. It’s also free of rules of thumb that might have just accidentally survived. Of course it’s not omnipotent – it does not show everything. For example, this graph does not show how fast you recover from an effort and how often you can complete an interval before exhaustion.
Peak Power of a training ride – for post-ride analysis
Some tools visualize the Peak Power of a training ride – after completing and uploading the ride data – in comparison to all-time Peak Power. This can be useful to judge how good you have performed in the last training session, whether you have indeed pushed yourself to your limit or even bested a personal record – or whether you sucked. Or may be it was just a tame regeneration ride.
Peak Power of a training ride compared with all time Peak Power in Cyclo-SpherePeak Power of a training ride compared with all-time Peak Power in Today’s PlanPeak Power of a training ride compared with all-time (and monthly) Peak Power in Cycling Analytics
Live Peak Power of current ride
If the Peak Power of the current ride could be visualized while riding and in comparison to all-time Peak Power, it could be used not only for post-ride analysis but to adjust the riding immediately.
How did I perform in my last sprint? Did I achieve a new personal record? Or do I need to try harder? May be even give up and re-aim this ride as a regeneration ride? How was the new warm-up protocol I tried today? Did it allow me to perform better already, or should I add a few minutes of warming up at low power?
After a while of thinking, I found an algorithm to implement the calculations in a way that would fit even into the somewhat arbitrary 28KB limit that Garmin has for Edge 520 data fields and that would run in realtime even with the limited computing power of that device. It does sometimes skip a refresh cycle and not update every second, may consume more battery power than other data fields and sporadically fail to start at all, but overall it seems to work:
The horizontal axis is the length of the interval from 1 second up to 3 hours. It’s an logarithmic scale as is common with Peak Power graphs, meaning that the resolution is higher for short durations – e.g. 1 second increments below 10 seconds – than for long durations – e.g. 1 hour increments above 1 hour, so that you can see details where needed but also have an overview of the long tail.
The vertical axis is a normal linear scale, again as usual with Peak Power graphs. On the left, power is given in W, on the right, power-to-weight ratio is given in W/kg. For this to work, your body weight has to be set correctly in your rider profile.
I also noticed that I should make it clear when I improved a personal record, so I decided to color the area between the previous all-time record and the new record in red, as shown here:
That area can be rather small and difficult to notive if the improvement is, say, 10W out of 1000W or 1%, so I have now changed the app (V0.3.30 from Feb. 1st, 2017) and added coloring of the area below the all-time record (in light red) as well, as shown in this screenshot. This coloring works in two steps to indicate if you are close to a new record by changing the coloring from beige to light orange at 90% of the all time peak before changing to light red at 100%.
Graph of Power Averages
As soon as I started using an earlier version of this data field showing my current as well as all-time Peak Power, I noticed that there was something missing. What is my current performance compared with the current Peak Power? Am I on my way to improve on a personal record or should I bail out? How long have I been maintaining the current power level – both when pushing hard but also to judge when to end a refresh break between hard intervals?
I found that many of these questions could be answered by adding a curve showing average power values for all interval durations. On the left, it would start with the current instant power, which is the average for 1 second, and continue rightwards to the 2 second average, 3 second average, and so on, until the 3 hour average on the right end of the graph. Whenever this curve overlaps with the Peak Power of the current ride, it would mean that you are currently riding at the Peak Power of the current ride and about to improve that section of the Current Ride Peak Power if you push a little bit more. If you are far below the Peak Power of the current ride, you will need to build some momentum first before having a shot at improving the Current Ride Peak Power.
Statistical reference curves for Peak Power
Looking just at your own Peak Power might be useful, but wouldn’t it be even nicer if one could also compare with others? Cyclinganalytics provides a nice rainbow colored graph to judge where one stands in comparison to all other Cyclinganalytics users.
The gray line shows my peak power while the rainbow shows the zone of all other users from bottom (in blue) to top (in red). So, this graph is showing that I am pretty average for short durations … and suck for any interval longer than a minute … if we compare by raw, absolute power. While absolute power is significant for speed when riding on flat roads, it also is a measure at which heavier riders fare much better than light weight riders like myself and even better than they’d do on a real flat road. When going uphill, the power-to-weight-ratio makes more sense as a performance measure as that is the physical metric that determines climbing speed – even though one could argue that that disproportionately favours lighter riders. Still I believe that power-to-weight ratio is the somewhat “fairer” way to compare athletes of different body size. The power-to-weight statistics for is significantly more favourable for me, as I now seem to be pretty average for all durations above a minute and slightly – or even significantly better between 10 and 30 seconds.
Instead of using actual statistical data of a large population, I turned to a famous table that’s also often quoted on the internet and looked up reference data about typical relative Peak Power for cyclists racing in Categories 1 to 5 and created some approximation curves to fill in the gaps. Obviously, these are very rough reference guides as there will be a lot of differences between riders in the same category and there really is no typical Category X cyclist. And of course these curves do not change the fact that peak power does not capture how quick one regenerates from an effort and becomes ready for the next break away. Still, I found these reference lines very useful, both to provide motivation to improve my relative weaknesses (from around 1 to 3 minutes) but also to further enhance my relative strengths (like between 10 and 30 seconds). The app will read from your profile settings whether you are male or female in order to adjust the reference curves. (I admit the approximation does not adequately model the flattening of the curve at very short durations – this could be improved later.)
App configuration
It would be really nice if this app didn’t require any configuration. In some way it doesn’t: if you do all rides with it, it will update the historical Peak Power curve after every personal record and require no configuration except for setting your body weight in your user profile.
But if you have just started using this app, it will take some time until the historical Peak Power curve becomes useful, and in the long run, you’ll probably do some rides without it even if you became a big fan of it. It would be nice if you could just connect it to the data accumulation site you are using and let the data field download all data via a connected smartphone – unluckily Garmin does not allow data fields to do that, and I am still divided whether to turn this from a data field which is easier to use within your training screens into an app.
The current solution is to use the settings screen in either your Garmin Connect App on your smartphone or Garmin Express app on your Mac/PC, to write or read the currently set values of your historical Peak Power. It requires 25 numbers in total, one each for 1-2-3-5-10-15-20-30-40-60-90 seconds and 2-3-5-10-15-20-30-40-60-80-100-120-150-180 minutes. You should be able to get most of those numbers from your TrainingPeaks, TrainerRoad, Cyclo-Sphere, Today’s Plan or Strava Premium account pages or Golden Cheetah – or whatever else you are using. Please approximate if your source does not supply data for some of the intervals this app is using. Note: Depending on the sleep settings of your Edge, it might enter sleep mode before you have finished typing all 25 numbers and saved, in which case you might lose your effort. So I recommend to save once half way through.
Peak power graph of a time period
Many sites also allow comparing all time Peak Power with Peak Power of some time period, like the most recent month or year, in order to judge how you are trending. I don’t think I haven’t seen any site offering comparison with the same month of the previous year, which is a standard comparison in economics to look at yearly differences for something with a seasonal bias and could be useful to cylists too who structure their training with on- and off-seasons over the duration of a year.
I have not yet found a way to implement such in a data field, due to memory limitations for the Edge 520 and Garmin not allowing internet communication over connected smartphones for data fields. It might be possible if I turned this data field into an app – may be sometime in the future.
Training PeaksCycling Analytics
Notes
Peak Power curves usually monotonically decrease – that’s the mathematical terminology for a curve that is either horizontal or sloping downwards to the right – with increasing interval duration but, a bit surprisingly, they don’t have to. Consider the weird but not impossible sequence 0W, 100W, 0W, 100W, 0W. Peak Power for 1s, 2s and 3s are 100W, 50W (average of 0W, 100W or 100W, 0W) and 66W (average of 100W, 0W, 100W) respectively with an increase from 2s to 3s.
The discontinued Pioneer SGX-CA900 cycling computer had a CP curve display but without the curve of averages which, I believe, really transforms these curves from a more post-ride analysis visualization to a tangible, actionable, real time display that reacts instantly to the way you ride and enables you to adjust.
I am not a big fan of using FTP. Being just one number it is about as simple as it may get and I myself use it when I set rough annual goals – because annual goals should be rough and leave room for correction. The FTP value alone does not say much – two cyclists with same FTP might perform very differently at 80% FTP or 120% or even at 105% FTP. Many cyclists will not be able to complete an hour at the FTP that was calculated from a 8 or 20 minute interval, while others might totally exceed. When FTP was proposed, which was pretty much still in an analog world, it may hit a good balance between complexity and modeling accuracy. But now in the age of big data analysis I think it’s about time to replace FTP by something more complex but also more scientific. Some might even go as far as saying that all the zones calculated in percentages from FTP and structured training programs that are scaled by a single FTP multiplier are nonsense.
I have also tried W’ which aims to model fatigue. As useful it would be to be able to calculate time to exhaustion or number of matches burned, this again is overly simplified and, in my experience, pretty much useless as the model does not mostly not fit whatever the choice of the – just – two parameters and without the ability to incorporate daily condition.
One might think that using live physiological data to generate and continuously correct a more complex model might be a solution. Unluckily it seems the human body is very complicated and the data we can measure is still very limited. Of what value is the easily measurable heartrate for an exact mathematical model of the cardiac output – which is what we are really interested in – without the much more difficult to measure stroke volume? I have been told that as a rule of thumb, SV is constant over a large part of the heart rate range and the linear HR-CO relationship only deteriorates at very low and very high heart rate – still, how low, how high and how constant are those limits for me? As informative as it is to use physiological measures and learn about the body, it seems we are still years away from being able to capture a sufficiently complete picture.
This app is my attempt to visualize that what we know – minus power meter accuracy – instead of hopeful and often practical but in the end illusionary ideas of what we hope we know but in reality do not. Of course this means that you have to do the interpretation yourself and you will need some experience with that. On the other hand, grasping relations and interpreting is something that the human brain might actually be good at. In the past year I think this data field has served me well to get a better understanding for average and Peak Power, both intuitively and consciously, which helped me to reach the training goal I was going after.
I should note that I do not recommend pushing yourself to your limit too frequently, even if you now have a tool that makes it easy to do so. Running marathons in your daily training would wear you down pretty quickly, like, in one day – chasing down personal records every day can be fun but is likely more stressing than is useful. In the extreme case, there have even been reports of athletes who had to give up sports due to scar tissue in their heart muscles likely caused by repeated overreaching and there is some speculation that the never seen before prevalence of cardio training could lead to a severely damaged, overstrained, unhealthy elderly population in the near future. My wish is that this app help making better decisions, and that should not always mean going to your limit.
But of course, it can be a huge motivator to best some of your records – of which there are plenty if you have the whole Peak Power curve from one second to three hours at your disposal – every few weeks, and one possible use of this data field is to judge whether today is the day to go for it.
Races are not my thing. I wouldn’t exclude the possibility that some time in the future I might change my mind and start participating in races – not that I am a believer in the stupid “never say never” movement, just open to changing my mind if given new evidence – but at this moment I’d categorize even a more deterministic competition like time trialling to be effected too much by random circumstances to make it worthwhile spending any time on it. Not that I’d be so naive to think that hard work should be always be rewarded. It’s more that I’ve never been happy winning something by luck.
How then to compete, not just with yesterdays self, but with others, beyond reaching for the occasional KOM or Sprint jersey in Zwift while training? How would I judge my strengths and weaknesses compared to other cyclists?
Answer: By power data statistics.
Cycling analytics (http://cyclinganalytics.com)
Cycling analytics allows to overlay the peak power distribution of all cycling analytics users (of same gender) to your peak power graph, which looks like this:
The rainbow colors represent the distribution of other riders and the gray line the own performance level, where higher on the vertical axis means better. What we see here is that, if we look at power weight ratio, I am generally pretty much average or slightly above, with a strength in short intervals from 10 seconds to up to 2 minutes. The “Rank” is the rank within the distribution where 0% is bottom, 50% median and 100% is top; my rank ranges from exactly 50% (i.e. pretty average) for 8 minute intervals to 87% (i.e. almost-not-able-to-improve-much good) for 20 and 30 seconds. So, from this data it seems clear that there is plenty potential to improve my endurance whereas trying to improve on short intervals might not lead to much further improvement. Or, if I’d start racing (and wanted to be successful), I might want to find a competition where short sprints are relevant. The colored, bottom-most “Rating” is a score for the riders relative strength/weakness when comparing different interval lengths not across population but within the rider.
Cyclinganalytics is free for one time analysis but requires a small monthly fee if you want to store and accumulate data.
Cyclo-Sphere (http://cyclo-sphere.com)
Cyclo-Sphere recently added a “Ranking” function where you can compare 20 minute max average power (or MMP in short for I believe Mean Max Power in their terminology) and – if you are using the Pioneer power meter – pedaling efficiency. In it’s simplest form, it looks like this, which is essentially a vertical cut through the CyclingAnalytics graph at 20 min on the horizontal axis showing the actual sample size on the vertical axis, with the bar that includes me colored in yellow. Note that, here, the percentage ranking counts down from top (i.e. smaller = better), opposite to what CyclingAnalytics does. And in the bar graph, more right = better.
Further, we can select a scatter plot, keeping power weight ratio on the horizontal axis but replacing the vertical axis by Pedaling Efficiency and the size of the blue circles now representing the number of samples.
We can further narrow down by gender and age group. Obviously, there is a shift towards less power with increasing age, which seems natural. I am not sure whether the small change in ranking number at the bottom left of the graph really means something. I am actually surprised that the large visual difference in distribution only changes 1 percent in the ranking. If this is true, it would mean that performance of super powerful riders decreases with age but everyone else is not affected by age – which seems weird.
In summary, the Cyclo-Sphere data again shows that I am slightly better than average, with the 41% ranking (counted from top) in Cyclo-Sphere pretty much exactly matching the 61% ranking (counted from bottom) for 20 min intervals in Cyclinganalytics – which is super surprising and maybe a random coincidence, considering that the user base could be pretty different (or do large populations of cyclists converge to some representative distributions?). There is actually also a slight difference in data processing as the latter uses overall historical best whereas the former looks at recent performance, but at this moment I am at my historical best (at least for the period I have been collecting data), so that doesn’t matter here.
The Pioneer power meter is a rare one: Whereas most power meters measure only tangential force which is the factor contributing to power (or, more precisely, process the data to extract only the tangential force and throw everything else out), the Pioneer isolates both tangential and radial forces and calculates efficiency using both. This difference also continues with the efficiency metrics: The more common efficiency metric in Garmin Vectors and others represents efficiency of tangential forces only, i.e. it measures mainly the tendency to not pulling up your legs enough between 6pm and 12am and leaving an force opposite to the rotation on your pedals. So, one could score a 100% efficiency with a Garmin Vector even if you’d be stomping on your pedals with a not-contributing vertical force at the top or bottom dead point, whereas a 100% score with a Pioneer means ideal pedaling with zero such radial forces. So, my 56.5% efficiency measured by Pioneer seems low if compared to tangential efficiency which is often much higher, but is actually pretty good and within the top 15% according to Cyclo-Sphere.
If we look in more detail, shifting from statistical population data to personal ride history shown in the graph below, we see a small but continuous improvement of overall efficiency over the last two years, and, separating left and right efficiency, we see that I am not astonishingly but still noticeably better on the right than the left. This could mean potential for efficiency improvement on the left leg even if I cannot eliminate a possibly natural tendency to be stronger on the right. Although of course, a natural dexterity advantage of the right leg could mean that I will always be less efficient on my left leg, one thought is that my right leg is more flexible with a larger motion range, so, improving flexibility of my left leg could be worth attempting. An extremely rough calculation of 8% efficiency improvement of the left leg or 4% overall would mean potential for an increase in 20 min Power Weight Ratio of 3.78W/kg to 3.93W/kg. Not huge but significant and probably worth attempting.
Cyclo-Sphere is free to use for anyone. I haven’t checked whether it will use the efficiency data of other, non-Pioneer power meters.
And the future?
The statistics I am really interested in and what may become available in the near future, given the broader use of power meters which allow for data driven, objective comparison over time and between cyclists are:
We are starting to learn that cyclists differ a lot in their response to training stimuli, and that standardized training, even if adopted by (single) measures like FTP, is somewhat of a lottery. It would therefore be extremely worthwhile to learn what “types” of training responses there are, to which type oneself belongs, and what kind of training is most effective over the long run or given time constraints.
We’d start selecting between services like TrainerRoad and TrainingPeaks depending on objective data about how much their users are really improving.
Training plans would not just be selected by the type of competition we are targeting and scaled by a single metric like FTP, but we’d have personalized custom training plans that combine the most effective training stimuli depending on an automated analysis of ones own and others performance data.
This could then lead to statistics showing where someone stands within cyclists spending the same amount of time in the saddle. It’s good to be fast and just spending more time on the bike is not guarantee to be faster, but still, you’d somewhat expect someone with significantly more time spent cycling to be faster. A “handicap” for less training time would feel going to far, but a competition for most effective training could be interesting.
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.
These are ride averages from a recent endurance climb on the Fuji-Subaru-line tollroad which leads halfway up on Mt.Fuji, Japan, and is the stage for an yearly hillclimb race with almost ten-thousand (!) participants. While the numbers – cadence, power, balance – should require no explanation, the graphs might be new to you. The left column shows average for seated pedaling while the right shows standing. The vectors in the graphs at the top show the direction and amount of the applied force at each of 12 crank angles whereas the botom graphs shows the amount of tangential force on the cranks, useful for judging for example where you had your peak (here it’s around 3 o’clock for sitting and 4 o’clock for standing) or where the forward rotational force (red) turned into counteracting resistance (blue) . There is also a left-right unbalance in the location of the peak when standing (later on the right). The percentage number in the center of the graphs is what pioneer calls “pedaling efficiency” defined as the ratio between tangential force and the sum of tangential and rotational force averaged over whole crank rotations. It would be 100% if the force vectors are perfectly tangential but does not require the amount of the force to be constant over the whole rotation.
