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

Which is faster: the Cervelo P3C or the Cervelo P2T?

by Andrew R. Coggan, Ph.D.

Over on another forum, I mentioned in passing that I had previously field-tested both a Cervelo P2T (Cervelo's track version of their original P2k) and Cervelo P3C (also the track version), and found that the latter was measurably faster. Another poster expressed a bit of surprise at this result, so I thought I would share the data here.

To compare the two frames, I used exactly the same procedures as described in this prior article:

http://www.trainingandracingwithapowermeter.com/2010/04/which-is-faster-cervelo-p2t-or-javelin.html

except that I used a different saddle, front brake, helmet, wheels, and tires in this more recent round of experiments. The P2T and P3C were, however, fitted with the same components, i.e., the only thing that differed was the frameset. Furthermore, my position on the two bikes was identical (facilitated, again, by their equivalent geometries).

The results of this testing are shown in Figure 1 below, which illustrates my power vs. speed relationship (corrected for slight differences in air density) when riding the two bikes. (Note that for the sake of clarity, I have chosen to start both the X and the Y axis at a positive value, and not at zero.)

Figure 1. Power versus speed when riding a Cervelo P2T vs. a Cervelo P3C.

As can be seen in the figure, I required slightly, but nonetheless measurably, less power when riding the P3C, especially at higher speeds where wind resistance becomes progressively more important. For example, to ride at 13.89 m/s (50 km/h) on the P3C I would need to produce "only" 395 W, versus 402 W when riding the P2T, corresponding to a reduction in my CdA from 0.220 to 0.212 m^2. While this 7 W (1.7%) difference in power requirement/0.008 m^2 difference in CdA may seem small, anyone who has trained/raced with a power meter and/or done any field testing using one will realize that it is not. In terms of a time differential, using the P3C instead of a P2T would save me 0.85 s/km, or 2.55 s in a 3 km pursuit or 34 s in a 40 km TT.

Note that the above measurements were made under very low wind conditions, i.e., at/near 0 deg of yaw. Since modern aero frames are designed to especially effective at the yaw angles typically encountered in competition, the above is likely an underestimate of the difference that would be observed under non-calm conditions. Indeed, Tom Anhalt has previously reported a slightly larger difference than the above when comparing identically-equipped Cervelo P2k and P3C time trial bikes in mildy breezy weather.

Using WKO+'s "Fast Find" to evaluate TT pacing

by Hunter Allen

Teaching a subject always challenges you and even though I have now been teaching the power training principles for 7 years, I always enjoy teaching seminars and really enjoy teaching them abroad. Bringing new knowledge to a place and to a group of people that really, really want it is just a joy. I had that experience in late April, when I traveled to New Zealand. I taught three separate seminars, one in Auckland to coaches and athletes and then two in Christ Church on the south island to the elite coaches of Bike NZ and then to the top coaches of Tri NZ as well. None other than six different former Olympians were in attendance at the seminars and two former World Champions as well, which was quite an honor to present to such an esteemed audience.

In each seminar, I always learn something new and these were no exception. Putting great minds in a room always causes some great ideas to spring forth. One coach, Silas Cullen, had a particularly good idea that I’ll share with you. I was teaching how to use the “Fast Find” feature in WKO+ software and using it to find “matches” burned in a race. In using the Fast Find, you put the “leading edge” wattage on the watts that you want to find and the software selects a wattage over that number and continues to select it until you specify a wattage number to de-select it in the “trailing edge”. In this manner, you can identify hard efforts and see just exactly when you might have “cracked” in a race or training. After teaching about Multi-File Range Analysis and demonstrating how to overlay time trial files on top of each other, Silas asked me if there was a way to identify a certain wattage in a file that you stayed under. We were discussing the importance of pacing in a time trial and how so many athletes ride too hard in the beginning of a time trial. So Silas wondered if you could identify how well an athlete did at staying under a certain wattage. Bells started going off in my head and I thought, “well, we can identify periods of time over a certain wattage, why can’t we just reverse the leading and trailing wattage numbers in “Fast Find” and then use that?” However, that’s not how the “Fast Find” feature was designed to be used, so we need to ask the question, when did the athlete exceed a certain wattage and for how long? In the time trial below I gave the athlete a specific wattage parameter to hold for the event and asked him to hold between 300 and 330 watts for the TT. By placing the leading edge on 300 watts and making the consecutive samples 10 (since we really only want to see significant time over 330) and then the trailing edge on 330 with 10 consecutive samples as well, this then instantly finds all periods of time in which he did not hold to that wattage pacing guideline. In this case, it was only three times of which two were in the beginning of the TT when he got up to speed and then at the end when he pushed for the finish line.

Figure 1. Example of using "Fast Find" in WKO+ to identify periods when athlete exceeded their wattage goal.

While this might not be anything new to you, it’s a nice way of finding those outliers in a time trial that might have contributed to poor pacing. In this case, our athlete did a superb job of pacing. Use “Fast Find” to find your outliers!

Demands of the individual pursuit, part 3

by Andrew R. Coggan, Ph.D.

In this series of articles I have combined the use of a conceptual model of pursuit performance (i.e., the pursuit performance "teeter totter") with a mathematical model of the physics of cycling (7) to assess the relative importance of various factors in determining success in this particular event. The overall approach was described in part 1 (http://www.trainingandracingwithapowermeter.com/2010/04/demands-of-individual-pursuit-part-1.html), whereas the physical and physiological determinants of pursuit performance were discussed in part 2 (http://www.trainingandracingwithapowermeter.com/2010/05/demands-of-individual-pursuit-part-2.html). The role played by various technical factors is considered below.

Determinants of pursuit performance: technical factors

Unlike the physical and physiological factors discussed previously, it is more difficult to determine the precise impact of variations in technical factors, i.e., an individual's technique or skill, in determining pursuit performance. This is because it is much more difficult (if not impossible) objectively quantify such factors versus the physical and physiological determinants of performance. Nonetheless, it should be apparent from the previous discussion that, e.g., a pursuiter's starting technique, per se, plays a very small role in their final overall time (Table 4). By starting technique, I am referring to precisely how an individual achieves a particular power output while accelerating up to speed, their coincidence anticipation timing (i.e., their ability to synchronize their movements with the count-down clock/starting gate), when they choose to sit down during the first lap, etc. Of course, this is not to say that pursuiters should not practice their start with regularity, as such races are not infrequently decided by 0.1 s or less. Clearly, however, a high degree of skill in starting is far less important to a pursuiter than, e.g., a kilometer or 500 m specialist (or even a team pursuiter), and this fact should be reflected in the relative emphasis placed upon start practice in a pursuit cyclist's training program.

Table 4. Time changes resulting from variations in technical determinants of pursuit performance.

In comparison to the individual's starting technique, their skill at riding as low as possible on the track can have a large impact upon their performance. For example, riding just 20 cm (~8 in) up from the measurement line will cost a pursuiter almost 0.1 s per lap on a 250 m track, or 1.1-1.3 s (0.5%) overall. Thus, riders should (and of course do) strive to ride as low on the track as possible when in the turns, especially when competing against a closely-matched opponent or when it is important to achieve the lowest possible time (e.g., qualifying, record attempt). On the other hand, knowing precisely how much time is gained/lost based on the line taken in the turns may provide the rider with the confidence needed to wisely "play it safe" in other situations, to avoid hitting a sponge and potentially crashing. Similarly, understanding the quantitative effect of other possible lines on the track (e.g., swinging wide in the straights) can help an athlete and/or coach choose an optimal path for a given track (and rider).

The third, and probably most important, aspect of skill or technique that influences a rider's pursuit time in the context of the physical and physiological factors previously discussed is pacing strategy. Contrary to what is believed by some, perfectly constant split times do not appear to be ideal (although such a pacing strategy is not far from optimal). Rather, modeling of pursuit performance based on physical and physiological information (6,8) suggests that a slightly faster overall time results when a peak velocity greater than the average steady-state velocity is achieved during the 2nd lap[2], with a very slight slowing (i.e., less than 1 s/km; corresponding to a gradual decline in power of ~15%) occurring thereafter. This conclusion is consistent with actual practice (9). I suspect that this somewhat counterintuitive strategy is the result of two factors: 1) by starting at a pace or effort slightly greater than that which can be sustained, utilization of your full anaerobic capacity is assured, and 2) any kinetic energy you have when you cross the finish line is "wasted", in the sense that it can no longer be used to improve performance (8). This latter concept seems to be especially important in the kilometer (and probably even more so in the 500 m), but apparently also applies to the pursuit.

On the other hand, it is far more common for pursuiters to start out too rapidly than too slowly, even at the elite level. This was evident in, e.g., the recent 2010 World Championships, in which the highly anticipated showdown between Jack Bobridge of Australia and Taylor Phinney of the United States for the gold medal failed to materialize after Bobridge qualified in 3rd place. Based on the official kilometer split times, it is clear that Bobridge started out consideraly faster than Phinney or the eventual silver medalist, Jesse Sergent of New Zealand, then slowed down far more than is ideal during the latter stages of the race (Figure 5). In contrast, the 4th place qualifer, Alexander Serov of Russia, started out more conservatively than the other three, but then accelerated too much during his 2nd kilometer in an apparent attempt to make up time.

Figure 5. Kilometer split times for the top four men during qualifying at the 2010 World Championships.

While in many cases riders must aim for a particular time that they believe is necessary to win even if this means risking "blowing up", it is nonetheless natural to wonder how the results of this recent competition might have been altered overall if Bobridge (or Serov) had paced himself differently in qualifying.

The negative consequences of starting too rapidly during a pursuit are largely due to the impact that this has upon the rider's physiology, i.e., the reduction in power that results from premature fatigue. However, even if a rider is sufficiently well-trained so as to largely withstand these negative physiological consequences, they may still go slower as result of a differences in the physics, i.e., an increase in aerodynamic drag resulting from the higher peak speed. This is illustrated in Figure 6 below, which shows the speed and power of a female track cyclist during the qualifying round and during the final round of a recent U.S. national championships.

Figure 6. Effect of pacing on 3 km pursuit performance when overall average power is equivalent.

The rider whose data are shown was considered an inside favorite to win the jersey, having shattered their personal best 3 km time in training just a few weeks beforehand. Nerves got the better of them in qualifying, however, and they started out too fast and then "died a thousand deaths" during the final kilometer (to the point that they could not dismount from their bicycle, but had to be lifted off by their handlers). Had the competition been run under current rules, this mistake may not have proved too costly, as they still posted the 2nd fastest time in qualifying. At the time, however, the 2nd and 3rd (and 1st and 4th) fastest riders still met in a semi-final round, meaning that they had to face another athlete who at the time was ranked #1 in the pursuit by the UCI and who just a few weeks later placed 7th at the World Championships. Fortunately for the rider in question, they were able to adjust their pacing strategy and went on to win nationals in a time 2 s faster than they recorded in qualifying, despite the fact that their average power differed by only 3 W, i.e., by less than 1%.

Determinants of pursuit performance: role of individual differences

To illustrate the various points made in this series of articles, I have assumed nominal values for parameters used to perform the modeling. It is unlikely, however, that the characteristics of any given individual will perfectly match these assumed values, i.e., the room for individual differences exists even if performance itself is equivalent. In particular, although the relative distribution of power is unlikely to vary significantly between athletes (as evidenced by the similar relative distributions shown in Figure 1 for 4 km and 3 km events), the absolute power required to achieve a certain time can vary. Likewise, athletes may differ in terms of the exact combination of aerobic and anaerobic energy production yielding a particular power output. An example of the latter is discussed on pages 247-248 of Training and Racing with a Power Meter. The point that I would like to emphasize here is that the systematic approach to analyzing pursuit performance that I have described can be used to identify and then capitalize upon such individual differences to optimize a given rider's preparation and training. In other words, it is the process that I have presented, rather than the exact outcome, that is most important.

Summary and conclusions

To summarize this series of articles, I believe that it most appropriate to simply repeat the synopsis that I provided at the outset:

"The individual pursuit: a deceptively simple event favoring specialists who possess superior aerobic fitness coupled with a high anaerobic capacity, excellent aerodynamics, and specific technical skills.”

[2]In my experience, it is actually the 2nd half of the 1st lap that is most important, as it is during this part of the race that riders are most likely to "overcook it", i.e., to continue to accelerate beyond their goal pace. Because half-lap splits are not recorded as often as full laps splits, however, most coaches and athletes believe that it is the 2nd lap that is critical.

Demands of the individual pursuit, part 2

by Andrew R. Coggan, Ph.D.

In part 1 of this article:

http://www.trainingandracingwithapowermeter.com/2010/04/demands-of-individual-pursuit-part-1.html

I presented a conceptual model that I refer to as the pursuit performance "teeter totter", and described how it could be used in conjunction with a mathematical model of the physics of cycling to assess the relative importance of various physical, physiological, and technical factors in pursuiting. Before describing the results of these analyses, however, I believe it is worth providing additional detail re. the mathematical model of Martin et al. (7):

Figure 2. Mathematical model of the physics of cycling of Martin et al. (7) .

As described previously, this model has been shown to accurately and precisely describe the physics of cycling under both steady-state and highly non-steady state conditions (e.g., maximal one lap effort on a velodrome from a standing start). This is illustrated in Figure 3 below, which compares the modeled versus directly-measured speed of an elite female cyclist performing a 3 km pursuit:

Figure 3. Model-predicted vs. directly-measured speed of a cyclist performing a 3 km pursuit.

As can be seen in the figure, there is a very close correspondence between the speed at any moment as calculated from the model and that actually measured during the race. This makes it possible to accurately and quantitatively predict the effect of changes in the physical and physiological determinants of pursuit performance shown in Figure 1. The results of analyses are presented below.

Determinants of pursuit performance: physical factors

By "physical factors" I refer to the sources of resistance to forward motion that a pursuit cyclist must overcome, which include drivetrain (and bearing) friction, rolling resistance, inertia (changes in kinetic energy), and aerodynamic drag. Using the model of Martin et al. (7) and the nominal athlete/equipment data and competition conditions presented in the previous article, it is possible to calculate the absolute and relative power requirements of world class pursuit performance, as shown in Figure 3 below:

Figure 3. Power requirements of world class pursuit performance.

As can be seen in the figure, overcoming aerodynamic drag requires by far the most power, with the other factors being much less important. This, of course, is not all that surprising, and explains the widespread use by pursuiters of positions and equipment chosen with aerodynamics in mind. What such an analysis permits, however, is the ability to make such decisions in a quantitatively-informed manner. This is perhaps best illustrated by considering the absolute and relative time savings during a pursuit that would result from an equivalent (e.g., 5%) change (reduction) in any of these factors, as shown in Table 2 below:

Table 2. Time saved as a result of 5% changes in physical determinants of pursuit performance.

Of course, an equivalent change in any of these variables may not always be readily achievable, and pursuit performance will be reduced to some degree by any and all improvements that can be made. Nonetheless, this information can be quite useful when considering situations where a trade-off does exist, e.g., when deciding whether to use wider, better rolling, but less aerodynamic tires versus narrower, poorer rolling, but more aerodynamic tires, or when deciding to invest money into specially-treated chains and chainrings that are designed to reduce drivetrain friction versus on a trip to a wind tunnel. Such information can also be very helpful when making decisions re. the preparation of athletes themselves - for example, although changes in stored kinetic energy represent the second-most important energy "sink" during a pursuit, any time savings resulting from having the athlete attempt to reduce body mass may be easily outweighed by a reduction in their absolute power output, as even relatively large changes in total mass have very little impact on pursuit time. While clearly such decisions can only be made on an individual, i.e., case-by-case, basis, the approach described here can be used to do so in a cogent fashion, instead of basing such decisions on tradition, intuition, etc.

Determinants of pursuit performance: physiological factors

As described in part 1 of this article, scientific research into the physiological characterstics of successful pursuit cyclists indicates that both aerobic power and anaerobic power, but not neuromuscular power, are important determinants of success in the pursuit. Consistent with these conclusions, the modeling approach I have used predicts that a 5% improvement in aerobic power output would reduce a rider's time by 1.4%, whereas a comparable increase in neuromuscular power would reduce their time by only 0.1% (Table 3). On the other hand, a similar increase in anaerobic capacity would improve their performance by 0.3%.

Table 3. Time saved as a result of 5% changes in physiological determinants of pursuit performance.

In this analysis, the cyclist is essentially being viewed as simply a motor, with no consideration given to the actual metabolic requirement of generating the required power output. This, of course, is determined by the individual's thermodynamic efficiency, i.e., the ratio between the rate of energy production and utilization in the form of ATP and the rate of external work production while cycling. Since an improvement in gross cycling efficiency would enhance a rider's power output regardless of the energy system supplying the ATP, an equivalent improvement in efficiency would have a cumlative impact upon an athlete's pursuit time. Specifically, a 5% improvement in gross efficiency would reduce the 4 km time of a world class male pursuit cyclist by 4.6 s (1.7%) and the 3 km time of a world class female pursuit cyclist by 3.7 s (1.7%). While an improvement in cycling efficiency of this magnitude is much greater than is realistically achievable, this observation may help explain the tendency of elite pursuit cyclists to perform very high volumes of training, despite the short duration of their event. This is because the skeletal muscle characterstic most closely associated with cycling efficiency, i.e., fiber type/myosin expression, likely only significantly changes in response to a very high training load (and/or maturation of the individual).

In part 3 of this series of articles, I will discuss the effects of changes in the technical determinants of pursuit performance (i.e., the fulcrum of the pursuit performance "teeter totter"), as well as the role of individual differences in how a given individual achieves a particular level of performance.