Analyzing interval workouts using power data

by Andrew R. Coggan, Ph.D.

Interval training has been used by athletes in practically all endurance sports for decades. In many cases, designing and interpreting such workouts with respect to their specific physiological demands is relatively straightforward. For example, constant-intensity efforts of, say, 3-8 min duration with a work:rest ratio of 1:0.5 to 1:2 would, if done at an adequate intensity, be expected to place significant strain upon the O2 transport system, and thus contribute to an improvement in VO2max. At the same time, however, it is unlikely that any individual could repeatedly perform efforts of this duration at a high enough intensity to result in truly marked improvement in anaerobic capacity (although there might be some increase).

While the physiological demands of workouts similar to the one described above are fairly easy to understand, cyclists often perform interval sessions that are far more complex in nature, and thus much more difficult to interpret. For example, the work:rest ratio is often manipulated to try to mimic the demands of competition, i.e., to prepare for the seemingly stochastic nature of racing. In addition, most cyclists perform their interval sessions on the open road, where the terrain may vary, versus on a flat track as, e.g., runners tend to do. Finally, the higher speeds achievable by cyclists vs. runners (or swimmers) means that they often accelerate “violently” at the start of intervals, which at least has the potential to result in significant neuromuscular demands not found with steadier efforts, or those that begin with more of a rolling or flying start. Examples of the sorts of training sessions that would fall into this 2nd category include the 30 s on, 30 s off (or 40 s on, 20 s) off intervals often used to simulate the changes in pace that occur in criteriums or when racing off-road, ladder sessions such as the 1 min – 2 min – 3 min – 4 min – 4 min – 3 min – 2 min – 1 min intervals described by Joe Friel or the 1500 m descending to 100 m session popularized by Eddie Borysewicz, etc.

In our book we provide guidelines (via the training levels) for appropriate power ranges to target when doing different types of intervals, and also discuss how to pace such intervals as well as when to stop doing them (i.e., the intervals-to-exhaustion concept). These guidelines and suggestions, however, are most useful when applied to “plain vanilla” intervals workouts such as described in the 1st paragraph above, and don’t always tell the fully story when it comes to the more complex sorts of training sessions described in the 2nd paragraph. What I would like to discuss here is how you can gain deeper insight into the specific demands of such interval workouts by analyzing them to answer three questions:

What was the power after smoothing the data using a 30 s rolling average?

The physiological responses to exercise follow a characteristic time course, many of which are directly or indirectly related to the kinetics of changes in the rate of mitochondrial respiration in muscle at the onset and offset of exercise. Thus, smoothing of the power data using a 30 s rolling average can be used to provide an approximation of the time course of changes in muscle, and hence whole-body, VO2, and hence aid in understanding the metabolic and cardiorespiratory demands of a given training session. (An exponentially-weighted moving average would theoretically provide an even better estimate of changes in VO2, but even that would be an oversimplification due to intensity- and duration/fatigue-related changes in O2 demand and kinetics.)

For example, if some particular combination of work and rest periods and power outputs results in a smoothed power output that approaches or exceeds perhaps 110% of the individual’s functional threshold power for several minutes at a time, then that workout likely represents a significant stimulus for adaptation in, or at least maintenance of, VO2max. On the other hand, if the work periods are of insufficient intensity or duration and/or the rest periods between them are so easy and/or so long that this is not true, then this will not be true – in that case, there is much less assurance that the individual’s maximal aerobic capacity has been significantly stressed, and some other aspect of physiological function, e.g., neuromuscular power or anaerobic capacity must have been primarily “targeted”. Indeed, if the smoothed power significantly exceeds approximately 120% of functional threshold power for any significant duration, then the athlete’s anaerobic capacity has almost certainly been stressed to at least some degree, since in most cyclists 120% of functional threshold power approximates power at VO2max, and VO2max sets the upper limit to aerobic energy production. Improvements in anaerobic capacity may also be stimulated by repeatedly incurring a large O2 deficit at the onset of each interval, which would be apparent from a significant difference in the areas under the curves of the actual and smoothed powers during the early portion of each effort.

What does a quadrant analysis show?

Examining the smoothed power from a series of intervals can provide insight into the metabolic and cardiovascular demands of the workout, as described above. To fully understand the neuromuscular demands, however, it is often useful to perform a quadrant analysis, i.e., to calculate the average effective pedal force (AEPF) and circumferential pedal velocity (CPV) used to generate the power and plot them against each other. Doing so provides an easy way of appreciating the effects of, e.g., starting each interval with a hard jump in a large gear from a slow speed versus just rolling into the start of each effort in a more moderate gear, or performing intervals on a hill versus on the flats. Quadrant analysis is especially helpful in understanding how use of very short work and rest periods (i.e., performing “microintervals”) can at least partially dissociate the metabolic/cardiorespiratory and neuromuscular demands, allowing greater emphasis on the latter than would otherwise occur.

Less obviously, quadrant analysis also provides an effective (albeit clearly not only) means of quickly understanding what the rider did during the rest periods. In particular, less experienced or less well-trained cyclists often experience the overwhelming urge to stop pedaling entirely after a strenuous effort. If so, there will be an absence of, or at least a relative paucity of, points in quadrants III and IV (since when cadence and hence CPV is zero, AEPF is undefined). Except when descending, though, it isn’t often that you get to coast for any extended period of time when racing. Continuing to “soft pedal” during each rest period may therefore not only better mimic the demands of actual competition, but could also enhance recovery from a physiological perspective, e.g., by increasing clearance of lactate, by activating the muscle pump, etc. Coaches therefore may (or may not) wish to encourage their athletes to pedal easily instead of coasting between intervals, with quadrant analysis providing a quick-and-easy way of visualizing whether this instruction was followed.

What was the normalized power for the entire session?

The normalized power algorithm is intended to be a predictor of physiological strain, not the adaptation(s) resulting from that strain. As such, the actual power really says more about the specific adaptations likely to result from a particular interval workout, especially when the data are smoothed in a physiologically-relevant manner as described above. Nonetheless, examining the normalized power for an interval training session can often be enlightening. Specifically, if the normalized power for a series of intervals (across all sets, and including the final rest period) lasting more than 20 min significantly exceeds the individual’s well-established mean maximal power curve for that duration, this implies that either:


1) their fitness has improved,
2) their power meter was miscalibrated, or
3) they have generated an “NP buster”.


While performing a series of interval-like efforts is one way of “breaking” the normalized power algorithm, this is actually quite difficult to do, especially when considering longer workouts. Calculating the normalized power for a series of intervals therefore provides a way of quickly ascertaining whether either of the first 1st possibilities listed above may hold true. In addition, the normalized power algorithm provides a good “sanity check” when designing new intervals sessions, i.e., if the normalized power over the entire series of work and rest periods exceeds a rider’s mean maximal power curve, it is highly unlikely that they will be able to complete the workout as planned. (Indeed, I have previously posted a list of theoretically-impossible intervals workouts on the wattage list here: http://groups.google.com/group/wattage/msg/22bd6beb194270eb and only one person reported that they were able to meet the challenge I put forth.) Conversely, if based on the normalized power (and the rider’s functional threshold power) the workout should be “doable” yet the rider still must cut the workout short, then this provides evidence that something else is amiss, e.g., they have not recovered adequately from prior training sessions, they are getting ill, their motivation was abnormally low due to outside stressors, etc.

Analyzing interval workouts using power data: a case study of 30 s on, 30 s off intervals

To help illustrate the ideas presented above, I analyzed an interval workout performed by another cyclist that consisted (after a ~30 min warm-up) of 20 x 30 s on, 30 s off, during which the rider targeted 370 W (~125% of their functional threshold power) during the work periods and 250 W (~85% of their functional threshold power) during the rest periods. Figure 1 below shows the actual power data during the intervals both without and with smoothing using a 30 s rolling average:

Figure 1. Power output during an interval training session consisting of 20 x 30 s on, 30 s off

As shown in Figure 1, the rider was reasonably successful in repeatedly hitting their goal powers, and completed the workout as planned. This is consistent with the fact that their normalized power for that 20 min block was 316 W, versus a functional threshold power of 300 W (i.e., the ratio of the functional threshold power to their normalized power during those 20 min was 300/316 = 0.95). The 30 s rolling average of power, on the other hand, oscillated between ~100 and ~120% of functional threshold power during the work periods. Based on this, the workout would be expected to place a sufficiently high demand upon the cardiovascular system to be considered a VO2max, or level 5, training session, at least from a maintenance perspective. On the other hand, the short rest periods and especially the relatively high intensity at which they were performed constrained the power the rider could produce during the work periods such that their anaerobic capacity does not appear to have been significantly challenged. They would have incurred a small O2 deficit at the onset of each intervals, but based on the “work deficit” determined by comparison of the unsmoothed and smoothed power data this would have represented only ~10% of their likely anaerobic capacity. Thus, to improve the latter they would need to increase the duration or reduce the intensity of the rest periods (or both), so that they could increase the intensity of work periods. They could also better target anaerobic capacity by increasing the duration (and intensity) of the work periods, e.g., by performing 10 x 60 s on, 60 s off, targeting 400 W, or 133% of their functional threshold power, during the work periods and 100 W, or 33% of functional threshold power, during the recovery periods (thus keeping the normalized power for the 20 min block to less than 316 W).

Although the workout in question is not likely to have contributed to any improvement in the rider’s anaerobic capacity, it did entail a moderate neuromuscular demand, as evidenced by the quadrant analysis plot shown in Figure 2 below:

Figure 2. Quadrant analysis of an interval training session consisting of 20 x 30 s on, 30 s off

As shown in the figure, the rider spent a significant portion of their time (56%, to be exact) in quadrants I and II during the intervals, implying that significant recruitment of type II, or fast-twitch, muscle fibers must have occurred. This was the result of not only the rider’s supra-functional threshold power during each work period, but also the fact that they initiated each 30 s effort from a cadence somewhat below their preferred cadence when time-trialing. Whether their AEPF represented a sufficient overload to improve their maximal neuromuscular power could only really be determined by comparing the data from this interval session to their maximal AEPF-CPV relationship. At a minimum, however, it is apparent that the intervals were effective in replicating the frequent, moderately high power accelerations that occur during mass start races, e.g., when exiting a sharp turn.

In summary, in this article I have described how to analyze power data from interval training sessions to gain deeper insight into their true physiological demands, and hence the nature of the adaptation(s) they are likely to produce. Applied to a typical 30 s, 30 s off series of intervals, the approach demonstrates why such intervals are so commonly used to prepare for the constant, “on-off” nature of criterium racing, which tend to place greater emphasis on neuromuscular power (e.g., jumping out of turns, launching attacks, sprinting at the finish) and VO2max (e.g., chasing down breakaways) than on anaerobic capacity or functional threshold power, both of which are called upon during more extended efforts of somewhat lower intensity than typically occur during criteriums. Indeed, this is reflected in the ‘\/\’ power profile found in many riders who tend to focus on/excel in such events (or who do a lot of unstructured endurance training combined with group rides and races). This is not to say, of course, that these are the perfect way of preparing for such races – rather, the point is that the approach I have described can often be profitably used to analyze interval workouts sessions of all types, so that training sessions are appropriately prescribed to meet the demands of any event.

Fatigability and BMX performance at the Olympic level

by Hunter Allen and Andrew R. Coggan, Ph.D.

HA: I have been involved in BMX since I was 11 years old, when I competed in my first race, and BMX has been dear to my heart ever since. It gave me the skills to become an elite MTB racer and later a pro on the road. Luckily for me, though, I realized that I don’t have enough fast twitch muscles to really succeed in BMX, and endurance was more my forté. However, I have continued to follow BMX over the years and when BMX became an Olympic sport in 2006 (for the 2008 Games), I knew that I wanted to contribute to the cause (besides, I had no data on elite BMXers!). As Technical Coach to the 2008 BMX Olympic Team, it was my job to outfit the team with SRM power meters, conduct a series of on-track and off -track tests, and begin to define the demands of BMX racing along with the abilities of the best riders in the world. Some of these data are described in the section on BMX in the new 2nd edition of our book, but we thought that it might be interesting to go into a bit more detail here.

For starters, it is important to realize that the Olympic BMX Super Cross track is not your regular backyard local track. This thing is practically a motocross track! It has a 30 foot tall starting ramp, the jumps are over 40 feet apart, the first turn berm is 25 feet tall, and the race lasted about 36 seconds, which is quite long for a BMX race. It is EXTREME. The demands of the track are different than a regular track and therefore some of the best BMXers on the national BMX circuit did not excel on this track. Check out the picture below (taken at the replica track at the Olympic Training Center in Chula Vista, CA)or view this quick video (http://feelbmx.com/videos/olympic-bmx-video-men-final) of the men’s Olympic final to see just how big and gnarly this track truly is!

One of the first things we tested in Chula Vista was the effort of the riders down the first straight away. I wanted to see how much time was spent pedaling vs. not pedaling (in the air mainly) and also how many watts they were able to put out coming down the start ramp and then coming out of the first turn. These were critical areas in the track and probably held the keys to success in BMX. The next thing we tested were full race laps with each athlete by themselves. This way we could see their fatigue resistance throughout the entire course without interference from other riders. Lastly, we did a few mock races to compare an actual race to the previous tests.

When I observed the riders during the tests themselves, it was really clear to me that they have quite a few “micro-rest” periods in each straightaway. So much time was spent in the air between the jumps that I knew this could be a critical component to an athlete’s ability to prevent fatigue near the end of the race. “Floating” over the jumps and relaxing in the air is definitely not a skill that all the Olympic hopefuls had and the riders that made the Olympic team clearly had this wired. Each race in fact was a series of “micro-bursts” and micro-rests. Another thing that I noticed was that the riders that were able to “corner-start” (that is, had the ability to replicate their effort leaving the starting gate while exiting the first turn) really had an advantage down the second straightway. If the rider could produce nearly the same watts they did off the starting gate, but now do it while rolling and exiting the first berm, it made a big difference.

When I looked at the data, I was blown away by some of the wattages that these athletes were putting out at the start and then down the first straight and exiting each turn. Another thing that blew me away was that not only were the best athletes putting out over 1800 W exiting the first turn, but they were pedaling at over 160 rpm and the best were over 180 rpm! Right away, it was obvious from the technical demands of the track and their physical performance that these BMXers were highly skilled and elite athletes. There was not a slacker in the bunch, and this was clearly not a sport for sissies.

I asked Andy to take a look at this data to see if he could see the same things that I was observing in analyzing the BMX power data and my observations at the track. I knew that if he put some thought and math into the data, that we might even learn more…..

ARC: When Hunter asked me to see what I could make of the power meter data mentioned above, the first question that came to mind was just how much of a decrease in power actually occurred during the race. In particular, I was curious as to how the fatigability of these athletes compared to other data that are available, e.g., to published standards for the fatigue index measured as part of the original Wingate test, and/or to the large amount of data we have collected on road and track cyclists since the power profiling was developed in 2003. This question, however, could not completely answered by simply looking at the “raw” power data. This is because single-speed BMX bicycles are typically fitted with low gears, e.g., 50-55 gear-inches, to help the rider get the “hole shot”, i.e., to maximize the rider’s ability to rapidly accelerate away from the starting gate and thus beat their competitors to the first jump or turn. As a consequence, for most of the race a BMX cyclist’s cadence is much higher than is optimal in terms of power output – for the men at the training camp, for example, peak cadence during the time trials was typically over 170 rpm. When combined with the long crank arms such riders often use, this means that their circumferential pedal velocity, and hence muscle shortening velocity, was extremely high, which in and of itself would tend to limit their power production later in the race. In other words, power might be lower at the end than at the start of the race not due to muscle fatigue per se, but simply due to the difference in cadence.

Fortunately, the “gate start” and “first straight” efforts that Hunter had the riders perform provided me with a way of correcting the data for the effect described above. I did so by using these multiple, but very brief (i.e., 4-6 pedal stroke), efforts to reconstruct each rider’s average effective pedal force (AEPF) – circumferential pedal velocity (CPV) relationship, as shown in Figure 1 below:

Figure 1: Relationship between average effective pedal force and circumferential pedal velocity from a representative athlete.

Based on this relationship, it was possible to calculate each rider’s:

1) maximal AEPF (AEPFmax), which is the Y intercept of the fitted line shown above;

2) maximal CPV (CPVmax), which is the X intercept of the fitted line shown above;

3) maximal power (Pmax), which is equal to 0.25 x AEPFmax x CPVmax; and

4) the CPV at which Pmax is produced (CPVopt), which is equal to 0.5 x CPVmax.

More importantly, these data allowed me to express the AEPF at any time during a full-lap effort as a percentage of the maximal AEPF (and hence power) that the rider could produce at their cadence, and hence CPV, at that time. By doing so, it was possible to determine the rider’s fatigability independent of changes in their pedaling rate. (Note that although all of the SRM handlebar computers were set to record data at 0.5 s intervals, in practice this actually means that all data are based on individual pedal cycles. This is because the SRM averages data over a full pedal revolution before calculating power, cadence, etc., and none of the riders ever pedaled fast enough to complete two full pedal revolutions in 0.5 s, i.e., in no case did the measured cadence exceed 240 rpm.)

Having determined that the above approach was feasible, I chose to analyze the data from the one or two solo full-lap efforts that each rider performed, on the assumption that these data would be more reflective of their “pure” physical abilities (i.e., skeletal muscle characteristics, fitness, motor control) than the data from the mock races, where interactions with other riders might occur. An example of the results of these analyses is shown below:

Figure 2: Average effective pedal force (AEPF) expressed as a percentage of circumferential pedal velocity (CPV)-specific maxima as a function of time during two TTs by a representative athlete.

As can be seen in the figure, the rider in question spent much of their time not actually pedaling, and even when they did pedal they did not and/or could not always do so very forcefully/powerfully. However, their pattern of force (and hence power, since the data are expressed relative to CPV) application was very consistent during the two time trial efforts, something that was true of the other riders as well. This indicated to me that the consistent variation in force/power must have to do with the placement of turns, jumps, etc., on the course, but since I was not present during the data collection I had to ask Hunter to fill me in on such details.

HA: The Super Cross course starts with a mammoth, 30 foot tall starting ramp that had up to a 53 percent drop at the steepest section, and then into a short flat section leading to the first double jump which was 40 feet from peak to peak. The riders coming off the starting ramp could only pedal so much before reaching a critical velocity and having to prepare for this first double jump. This first double jump was so intimidating that even World Champions had to ride it quite a few times before getting their nerve up to jump it. Next came another double jump into a long “tabletop” jump in which the riders couldn’t pedal over, and then lead into the first turn. Coming out of the first turn at over 35 miles per hour, the riders had to “corner-start” and pedal hard for a few pedal strokes in order to hit the next “step-up” jump, which peaked at 25 feet tall. This was followed by a smaller double jump, after which they had to jump over the women’s course berm, leaping a massive chasm onto the men’s course and second berm. If the rider did not have a solid “corner-start”, then that could play out badly at the end of the straight when they had to leap the chasm. The third straight was characterized by almost continual jumps, so much so that it was only possible to get in one or two pedal strokes and the final straight had two more jumps in it with a flat sprint to the finish.

ARC: Once I understood why these cyclists were or were not pedaling at certain times, I decided to focus on their force/power during the last two or three pedal strokes, as an indicator of how much they fatigued during each TT. These data, along with data derived from the force-velocity relationship previously described, are shown for four athletes in the table below. These individuals were chosen for comparison because although they were all very similar in terms of performance in the unfatigued state (i.e., data shown in the first four columns), the first two failed to make the U.S. Olympic team, whereas the last two won the Silver and Bronze medals, respectively.

Table 1. Force-velocity relationship during cycling, maximal power, and power at end of TT for four athletes.

Upon examining these data, what became evident is that the more successful athletes (i.e., th last two) were able to maintain a higher relative (to CPV) power output during the latter portion of the race, i.e., they exhibited less fatigue.

The question then arises as to what might account for the greater fatigue resistance (lesser fatigability) of the cyclists C and D compared to riders A and B. One possibility, of course, is a difference in fitness/conditioning, and indeed other men at the camp seemed to be somewhat lacking in this respect. This did not, though, appear to be true for cyclists A and B. Another possibility is an inherent difference in muscle fiber type, i.e., it is possible that cyclists A and B fatigued more rapidly because they had a higher percentage of type II, or fast-twitch, muscle fibers. As shown in Table 1, however, the slope of the AEPF-CPV relationship was similar in all four men (and indeed across all of those tested, including a rider who recorded what to my knowledge is the highest-ever 5 s human power output of 25.2 W/kg), suggesting that they were all also similar with respect to muscle fiber type (a higher percentage of type II fibers would be associated with a shallower slope of the AEPF-CPV line, i.e., force and hence power would fall off less rapidly with increases in CPV and hence muscle shortening velocity).

If not fitness or fiber type, what does explain the difference in fatigability between these otherwise very closely-matched athletes? As it turns out, it appears that cyclists A and B fatigued more than cyclists C and D simply because they pedaled more. Specifically, based on the SRM data cyclists A and B completed 27 and 34 pedal revolutions during their TTs, whereas cyclists C and D pedaled only 20 and 18 times, respectively. This is not because cyclists A and B used markedly lower gearing, as they did not – rather, the higher number of pedal revolutions was apparently the result of their attempting to generate power at times when it was difficult, or even impossible (i.e., when air-borne), to do so. In contrast, cyclists C and D pedaled less often, but when they did they did so with maximum effectiveness, e.g., the three pedal revolutions at ~100% of velocity-specific force and hence power performed 7-8 s into the race shown in Figure 2. The remainder of the time, they apparently “rested” their legs as much as possible by “floating” over the jumps, etc.

HA: To summarize, then, here are some important lessons to be learned from these data:

1. Pedal less, win more. While this is well known in road racing circles, it is not so well known in BMX. As described by Andy above, however, we found that the guys who made the Olympic team pedaled less than those that did not make the team.
2. 100% "fast twitchers" may not make the best BMXers, at least for the SX. In particular, the guy that cracked out the biggest numbers in terms of Pmax (which is measured over a single revolution) and maximal 5 s power also did not make the team.
3. 
   Fatigue resistance matters. Again as described above, the Olympians fatigued the least over the duration of the race. They were able to make the most of their "micro-rest" periods, along with superior fatigue resistance gained via conditioning. On the hand, riders who did not make the team fatigued more, either simply as a result of pedaling too much (see point #1 above) or (in the case of other riders whose data are not shown) due to lack of fitness. 

We sincerely thank Dr. Steve Johnson at USA Cycling for permission to share these data and Mike Day (Silver Medalist, 2008 Olympic Games) and Donny Robinson (Bronze Medalist, 2008 Olympic Games) for allowing themselves to be identified in this article.

Envelopes, stamps, and books...oh my!

by Hunter Allen

The story of the books…..

Andy and I have really enjoyed writing this 2nd edition of Training and Racing with a Power Meter and we have had great fun autographing the first batch of them for you!

Here’s some photos documenting their journey to your doorstep and a little commentary as well.

First we began with the envelopes and placing address labels on them all!

Thomas and Susannah Allen putting address labels on all the envelopes and organizing them by state:

Lots of envelopes:

Next was the geography lesson. Seeing that this could make a great ‘teaching lesson’ for my kids, we got some maps and some little flags and pins and put them in all the locations that the book went to:

While we were waiting on the books to arrive in Virginia, they arrived in St. Louis, Missouri for Andy to begin signing them all! That’s a lotta boxes containing a lotta books!

Luckily Andy had been training for just this moment and had been tapering to make sure his TSB would be positive to autograph them all in one sitting and then crate them back up and ship them to me in Virginia.

Once they arrived in Virginia, the onus was on me, my wife Kate and my office manager Becky to get them all signed , stuffed in envelopes and sent to you!

And then they all went to the good old US post office to be shipped out. It took us 4 days and we had to go in waves of 50 books in order to not ‘overwhelm’ the post office in our little Virginia town, but they are all on the way to you!!!

We hope you enjoy them! Many thanks to the good folks at VeloPress - Renee Jardine, Kara Mannix, Jen Soule, Jessica Jones, and Dave Trendler - who have helped make it all happen!!

Hunter and Andy