Demands of the individual pursuit, part 1

(Based in part on a presentation given to the Pan American Sports Organization in 2005.)

by Andrew R. Coggan, Ph.D.

"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.”

The individual pursuit is one of track cycling’s classic events, having been regularly contested in the early 1900s and having been included in every World Championship since their inception in 1946[1] and every Olympic Games between 1964 and 2008 inclusive. As the name implies, the event is raced pursuit-style (i.e., against an opponent starting on the opposite side of the track) over a distance of 4 km for men (5 km for professionals until 1992) and 3 km for women, and requires elite athletes approximately 3.5-4.5 min to complete. As such, it is comparable in duration to, e.g., the 1500 m in athletics (track and field) or the 400 m in swimming, and similar to these events requires extremely high levels of both aerobic and anaerobic fitness. Performance in the individual pursuit is also significantly influenced by other traits or talents of the athlete (e.g., ability to minimize aerodynamic drag while still maintaining a power output requiring ~110% of VO2max) as well as by physical factors that may or may not be within the athlete’s control (e.g., rolling resistance). In this series of articles I review these and other determinants of pursuit performance, first based on the published scientific literature and then using a conceptual model that integrates the physiological, physical, and technical aspects of this deceptively simple event. The information provided will hopefully prove to be of interest to athletes participating in the individual pursuit and/or their coaches as well as to other exercise physiologists and sports scientists. For information on the team pursuit, readers are referred to previous articles by Broker et al. (1) and Schumacher and Mueller (2).

[1]The first World Championship in track cycling was actually held in 1939, but the competition was interrupted by the outbreak of World War II and no champions were named.

Determinants of pursuit performance: physiological characteristics of elite pursuit cyclists

As might be predicted based on the event's duration, the pursuit is a predominantly aerobic competition. Specifically, it has been estimated that during a 4 km pursuit ~85% of total energy is produced via aerobic metabolism, with only ~15% coming from anaerobic sources (3,8). A slightly larger contribution from anaerobic energy supply would be expected for the shorter 3 km race contested by women (or masters riders), but the difference is unlikely to be too great, in part because of the smaller muscle mass and thus lower absolute anaerobic capacity of most women. Given the above, it is not surprising that the physiological characteristics of elite pursuiters (3,7) resemble those of elite road time-trialists (4), with both being characterized by a high VO2max and especially a high lactate threshold. On the other hand, the maximal power of elite pursuiters is quite unexceptional (3,5,7). In fact, one study (5) found that pursuiters were not different from completely untrained individuals in this regard.

Although maximal power may be unimportant to pursuit performance, anaerobic capacity clearly does play a role. Specifically, Olds et al. (7) found that variations in anaerobic capacity within the range observed in the group of athletes they studied could account for up to a 4% difference in 4k pursuit time. Similarly, a multiple regression model using the same data set (3) identified VO2max, power at LT, and anaerobic capacity as the three most important predictors of pursuit time.

Interestingly, this same regression model (3) failed to identify cycling efficiency as an independent predictor of performance, even though efficiency is widely recognized (e.g., 2) as influencing steady-state cycling power, and "first principles" modeling (7) using the same data indicated that variations in efficiency could account for even more variation in performance than variations in VO2max. This could be because efficiency is probably highly correlated with other parameters included in the model (i.e., VO2max, LT), and therefore provides no independent information. Alternatively, it is possible that the laboratory test of efficiency (in which cadence progressively increased from 85 to 120 rpm), while demonstrating differences between athletes, failed to accurately reflect differences in their on-the-bike function.

Determinants of pursuit performance: the pursuit performance "teeter totter"

As described above, one way of gaining insight into the demands of a particular athletic competition is to examine the physiological characteristics of those who excel in that event. This approach, however, does not provide truly quantitative information upon which to base decisions about, e.g., the design of an appropriate training program. Furthermore, it does not address the importance of factors other than the athlete's physiology, for example the role of physical factors such as aerodynamic drag or the individual's technical skill. Thus, to fully understand the demands of the individual pursuit, I believe that it is helpful to consider the conceptual model shown in Figure 1 below:

Figure 1. The pursuit performance "teeter totter"

In this conceptual model, physical factors acting to slow the cyclist down are shown as acting upon the left side of a child's "teeter totter" (or see-saw), whereas physiological factors contributing to their ability to generate power and hence go faster are shown as acting upon the right side. The individual's actual performance time is determined by the point at which these two "masses" act to balance each other, i.e., by the exact position of the fulcrum at the bottom representing the athlete's technical skill. The size of the font used to list the factors shown within the two masses and the fulcrum represents their relative importance, based on mathematical modeling of pursuit performance as described below.

Mathematical modeling of pursuit performance

To assess the quantitative importance of the various factors shown in Figure 1, I used a physics-based mathematical model of the power requirements of cycling (9) to model the performance of a hypothetical world class male or female pursuit cyclist. This mathematical model has previously been validated under both steady-state (9) and non-steady-state (10) conditions, and has been shown to predict power and/or speed with a high degree of accuracy. The specific characteristics of the representative athletes (see Table 1 below) were chosen such that their pursuit times would approximate those required to win at the 2005 World Championships, which were held at the ADT Event Center velodrome in Carson, CA. Performances on this track were chosen as the "benchmark" in part because of greater certainty as to the exact air density and rolling resistance of the surface versus those at other, faster velodromes. Values for height and weight were simply assumed, from which CdA was estimated using the equations of Heil (11). The power required to achieve the given performance times were then calculated from the model and cross-validated by comparison to actual data.

Table 1. Nominal characteristics of world class pursuiters used in modeling

With the above model in hand, the relative importance of the various factors shown in Figure 1 was determined by examining the change in pursuit time resulting from an equivalent change in any of the parameters listed. The results of these analyses will be described in parts 2 and 3 of this article.

References

1. Broker JP, Kyle CR, Burke ER. Racing power requirements of the 4000-m individual and team pursuits. Med Sci Sports Exerc 1999; 31:1677-1685.

2. Schumacher YO, Mueller P. The 4000-m team pursuit world record: theoretical and practical aspects. Med Sci Sports Exerc 2002; 34:1029-1036.

3. Craig NP, Norton KI, Bourdon PC, Woolford SM, Stanef T, Squires B, Olds TS, Conyers RAJ, Walsh CBV. Aerobic and anaerobic indices contributing to track endurance cycling performance. Eur J Appl Physiol 1993; 67:150-158.

4. Coyle EF, Feltner ME, Kautz SA, Hamilton MT, Montain SJ, Baylor AM, Abraham LD, Petrek GW. Physiological and biomechanical factors associated with elite endurance cycling performance. Med Sci Sports Exerc 1991; 23:93-107.

5. Davies CR, Sandstrom ER. Maximal mechanical power output and capacity of cyclists and young adults. Eur J Appl Physiol 1989; 58:838-844.

6. de Konig JJ, Bobbert MF, Foster C. Determination of the optimal pacing strategy in track cycling with an energy flow model. J Sci Med Sport 1999: 2; 266-277.

7. Olds TS, Norton KI, Craig NP. Mathematical model of cycling performance. J Appl Physiol 1993; 75:730-737.

8. van Ingen Schenau GJ, JJ de Konig, de Groot G. The distribution of anaerobic energy in 1000 and 4000 meter cycling bouts. Int J Sports Med 1992; 13:447-451.

9. Martin JC, Milliken DL, Cobb JE, McFadden KL, Coggan AR. Validation of a mathematical model for road cycling power. J Appl Biomech 1998; 14:276-291.

10. Martin JC, Gardner AS, Barras M, Martin DT. Modeling sprint cycling using field-derived parameter and forward integration. Med Sci Sports Exerc 2006; 38:592-597.

11. Heil DP. Body mass scaling of projected frontal area in competitive cyclists. Eur J Appl Physiol 2001; 85:358-366.

12. Wilberg RB, Pratt J. A survey of race profiles of cyclists in the pursuit and kilo track events. Can. J. Sports Sci. 1988; 13:208-213.

Ventoux as Stage 20...are they serious?

(Reprinted with permission from the October 2009 issue of ROAD magazine: http://bluetoad.com/publication/?i=21569)

by Hunter Allen - I am sure that when the race director of the Tour de France, Christian Prudhomme proposed that the next to last day of Le Tour finish on the summit of Mt. Ventoux, he got some quizzical looks, opposition and downright questions of his sanity. From a logistical standpoint alone, the stage would be tough for the riders, with a big transfer afterward to Paris and the fact that it would be going against the normal time trial which usually makes up the penultimate stage, I am sure that Monsieur Prudhomme received quite the opposition to this idea. However much it was, he got it passed through the committee and the race route was announced to even more disbelief from the riders and the team directors. Mt. Ventoux? On the penultimate day? Are they serious? Finally that day came for the riders, and it was Stage 20. Yes, Stage 20 and still hard to believe. Most of us have no idea what it takes to do a 21 day stage race, the level of soreness in the legs or mental effort needed to keep on pushing each day to get to the front, or how tired you get of eating, much less doing a massive mountain top finish on the 20th stage! Chris Anker Sorenson does though, and as a domestic on the Saxo Bank team, he had some serious responsibilities in getting the Schleck brothers to the front, setting tempo on the lower slopes of important climbs and helping bring bottles to his teammates throughout the race. Chris used his SRM power meter for each of the stages and shared those files with us on http://www.trainingpeaks.com/. A big thanks goes out to SRM, Team Saxo Bank and all the people involved in bringing those files to us to review and examine. Let’s take an in depth look at that infamous Mt. Ventoux stage and see what Chris had to do for his team and for himself to just finish the stage.

Figure 1. Chris Anker Sorenson's power data from stage 20 of the 2009 Tour de France.

With just over 100 miles to race, four categorized climbs on the profile BEFORE Mt. Ventoux, and over 11,000 feet of climbing, Chris burned over 4600 calories for the day and averaged 21.1 mph for the stage! (See Figure 1 above). His normalized power (power he would do if he pedaled smoothly and steadily for the whole stage) was 309 watts for nearly 5 hours of racing, and his best 20 minute average power was 398 watts as he set tempo at the base of Mt. Ventoux. This pace setting effort in the beginning of the climb was not only an incredible effort at 398 watts, but it also was his BEST 20 minute effort for the entire Tour! When I see that an athlete does his very best effort at the end of a long stage race, then that tells me that he has improved his ability to recover over the duration of the stage race, he has taken good care of his body during the race, he has the ability to ‘rise’ up to the demands and needs of the team and lastly….he underperformed at the beginning of the race. In this case, Chris most likely underperformed at the beginning of the Tour because he knew that he was going to have to work for the team in the latter parts of the race, (however remember that Saxo bank had the yellow jersey (Cancellara) in the early stages of the race as well) so Chris really had to meter out his efforts without overdoing it and therefore just didn’t get the chance early in the race to show us what he could do if he went all out for a stage victory.

When we examine the power file for Stage 20 further, one important thing becomes clear as well and this might provide a clue to why Chris was able to put out his very best 20 minute power on this penultimate stage. As I have stated in the past, the road racers that win the most pedal only about 83% or less of the time of the race. The best road racers have learned how to pace their energy expenditures throughout the stage and put out effort only when absolutely necessary. In other words, the best road racers are lazy and the best stage racers…even lazier! In Stage 20, Chris only pedals 80% of the time (see Figure 2 below), and that means nearly an hour of the stage he spent coasting and not pedaling, which is the mark of a good road racer and also gives us some insight in why he could do a huge effort on that stage. With the cumulative effects of resting more than most other riders in each stage, one can see that just by resting more in the peloton over a 21 day race can give you a huge advantage near the end of the race.

Figure 2. Distribution of cadence for the entire stage.

The next most interesting thing about Chris’ power file is the Mt. Ventoux climb itself. First off, at the beginning of the climb, you can see his power fluctuating highly in the first nine minutes (see Figure 3 below) and this was because he was sitting on his teammate’s wheel- Nicki Sorenson- as Nicki was the first one on the team to set the pace. After Nicki was done with his turn at the front, it’s up to Chris, so he nails it at his limit and pushes the pace harder than he has done in ALL the previous stages for over 5minutes at 418 watts! This was Chris’ 2nd best 5 minute effort of the entire tour with his absolute best 5 minute effort happening just before...at the base of Mt. Ventoux! So within the span of about 20 minutes, Chris had done his best 5 minute wattage, his 2nd best 5 minute wattage AND his best 20 minute wattage for the entire 21 stages!!!! Clearly, an amazing effort for Chris and this really shows his potential for stage racing and for future success.

Figure 3. The start of the Ventoux climb.

The next thing that I find highly fascinating in the climb up Mt. Ventoux is Chris’ cadence on the climb. When he was following Nicki and then when he took a pull at the front, which was a total of 17 minutes of effort at over 400 watts and an average heart rate of 181 bpm, his cadence was relatively high. During these 17 minutes, Chris’ cadence was right at 100 rpm and clearly when Chris has to go ‘full gas’, then he needs to keep his cadence right near that 100 rpm mark to produce the most amount of watts. After these first 17 minutes (when teammate Andy Schleck started to attack!), then Chris’ job was over and he immediately dialed back the intensity to a more do-able wattage of 345 watts (roughly 90% of his FTP) and dropped his cadence to 80 rpm (see Figure 4 below). Initially you might think, well…it got steeper so he could only do 80 rpm, but in reality the steepness of the climb didn’t change at all when we examine the elevation data from his downloaded power file. This really highlights the roll that cadence plays in the production of power at your absolute limit. In the case of Chris Sorenson, there is a 20% difference in cadence (from 100 rpm to 80 rpm) which causes a 15% difference in wattage that he can produce. That 15% wattage difference isn’t just any 15% difference, but it’s THE uppermost watts that he can produce, which counts as a very significant amount of effort and the difference between being able to do your job for the team or not.

Figure 4. Power and cadence during the first 17 minutes of the climb vs. the last 45 minutes.

Clearly, this is just a snapshot of the toughest 21 day stage race in the world, but it also goes to show just how hard these riders can continue to ride day after day. One begins to wonder at what point (how many days?) would all the riders begin to slow down significantly and eventually ride at a speed of 14mph for the entire stage? Making the hardest mountain top finish on the penultimate stage made for an exciting stage, huge spectacle and great battle, even if in the end the general classification didn’t change turned out to be a great idea for this year’s Tour. Congrats to Chris and all the riders on the Saxo bank for a very impressive Tour! I am sure I won’t get any thanks from the riders in this years’ TDF, but I commend Monsieur Prudhomme for taking such a bold step and making this stage such a memorable one. I hope he will continue with other surprises for 2010!

Does drafting benefit the leading rider?

by Andrew R. Coggan, Ph.D. - Based on aerodynamic theory, the power that a cyclist needs to produce to ride at any particular speed should be lower when one or more additional riders are drafting closely behind. This is because the “bow wave” of air in front of the trailing rider(s) helps to fill in the zone of reduced pressure that normally exists in the leading rider’s wake, thus reducing the leader’s aerodynamic drag. While this effect is widely recognized in auto racing circles (especially NASCAR), it has long been held that cyclists do not travel fast enough and/or in close enough proximity to each other for the effect to be measurable. Purely by chance, however, in 2007 I happened to collect some powermeter data on an indoor track that suggest that this may not be true. Despite considerable searching I have not encountered similar findings discussed or presented elsewhere, and so I would like to share them here.

Figure 1 below shows, in blue, the power-vs.-speed relationship for an elite female pursuit cyclist when riding on the ADT Event Center velodrome in Carson, CA. These data were collected using an SRM Professional track crank during 12 x 1 km flying efforts performed at varying speeds to determine Crr and CdA on the track, and hence aid in equipment selection and pacing strategy. The results have been corrected for 1) minor variations in starting and ending speeds and thus in stored kinetic energy and 2) frictional power losses in the drive train (assuming an efficiency of 97.5%). The cyclist was in full race kit (i.e., race wheels, skinsuit, shoe covers, aerodynamic helmet), and the velodrome was empty except for one other cyclist who was performing identical efforts on the opposite side of the track (more on this below).

Figure 1. Power vs. speed relationship when riding solo.

As expected/as can be seen in the figure, the power-vs.-speed relationship was well-fitted (i.e., R^2 = 0.9998) by an equation of the form:

Y = 3.22X + 0.1146X^3

which corresponds to an apparent (i.e., uncorrected for increased normal force in the turns) Crr of 0.0043 and a CdA of 0.198 m^2. Taking into consideration the increase in normal force, the former would equate to an actual Crr of ~0.003, which is consistent with the results of subsequent straight-line tests conducted on smooth asphalt using identical procedures, which yielded a Crr of 0.0032±0.0003 for these tires (i.e., VeloFlex Record clinchers with Michelin latex tubes) when inflated to the same pressure as used on the track (i.e., 115 psi). On the other hand, the value for CdA obtained during the field tests agrees exactly with that measured (over 0 to 10 deg of yaw) in the Oran W. Nicks Low Speed Wind Tunnel at Texas A&M University just two weeks previously. As such, these data are in keeping with the results of Martin et al. (Med Sci Sports Exerc 2006; 20:592-597), who reported excellent congruence (average difference = -0.001±0.002 m^2) between field test- and wind tunnel-derived measurements of CdA in five out of six subjects. (CdA in their other subject inexplicably differed by almost 10%, strongly suggesting, e.g., an inadvertent difference in clothing.)

The formal testing described above was conducted on the second day of a multi-day training camp. On the third day, the cyclist in question performed a workout that included 4 x 3 km flying efforts with a goal pace of 13.5 m/s (i.e., 3:42 for 3 km). They followed the same warm-up and used precisely the same equipment, position, and tire pressure as the previous day; air density (measured trackside using a Brunton ADC Pro) was also identical. Unlike the previous day, however, throughout these efforts the second rider mentioned above drafted very closely behind the “test subject”, as shown in Figure 2 below.

Figure 2. Second rider drafting closely behind pursuit cyclist whose power data form the basis of this report.

This was not a planned experiment, but simply reflected the desire of the drafting rider for an easier, but still high speed/high cadence, workout. Interestingly, however, the presence of this second rider seemingly reduced the pursuiter’s power requirement, as shown in Figure 3 below.

Figure 3. Power vs. speed relationship when being drafted.

Specifically, their power during the 4 x 3 km flying efforts was, on average, 9 (range 3 to 15) W lower (P=0.024 by one-tailed t test) than expected based on their power-vs.-speed relationship established the day before. To put it another way, having a rider drafting closely behind them apparently lowered their CdA by 3.2%, i.e., from 0.198 to 0.192 m^2. In terms of time saved, this would permit them to cover 3 km (e.g., in a team pursuit) ~1.5 s faster than riding alone, even if the following rider(s) never even “pulled through”.

As stated at the outset and as reiterated in the paragraph above, this was not an intentional experiment, and so it is possible that other factors explain the small, but nonetheless apparently measurable, reduction in the leading rider’s power when another rider was drafting. For example, it is possible that the presence of another rider on the track during the formal testing disturbed the air sufficiently to influence the power-vs.-speed relationship shown in Figure 1. Care was taken, however, to synchronize the two riders’ efforts so as to maintain approximately one-half lap (i.e., ~125 m) separation between them at all times. Furthermore, based on the reports of others if anything the presence of another rider on the opposite side of the track should have reduced, not increased, the power that the pursuit rider had to generate. Finally, as mentioned previously the CdA calculated from these data agrees exactly with that determined via wind tunnel testing. Thus, this explanation seems unlikely.

Possibly a more plausible scenario would be that having the two cyclists riding together at high speed created more of a counterclockwise rotation of air than when the riders were on opposite sides of the track, i.e., on the second occasion the two riders were effectively drafting 250 m behind themselves (vs. 125 m behind each other). Indeed, it would only require a self-generated “tailwind” of 0.15 m/s to explain the observed difference in power, and standing in the infield at ADT I have measured wind speeds of >2 m/s when many riders are on the track simultaneously, e.g., during pre-event warmup. Arguing against this possibility, however, is the lack of any perceptible flow of air when the two cyclists were riding together (except immediately following their passing), as well as the fact that close inspection of the powermeter data failed to reveal any trends over time as one might expect if the riders were truly causing the air to start to swirl inside the building. In any case, I believe that these observations are intriguing, and I encourage anyone who agrees to undertake more formal theoretical or experimental studies of the phenomenon on their own.

A brief history of training and racing with a power meter

by Andrew R. Coggan, Ph.D. - People have been competing against each other on bicycles since at least 1868, when the Englishman James Moore won a 1.2 km event held in Parc de Saint-Cloud, Paris. The cycle ergometer has been around nearly as long, i.e., ever since 1896, when Elisée Bouny attached a mechanical brake to the rear wheel of an ordinary bicycle that was elevated off the ground to quantify the power output of racing cyclists. It wasn’t until the development of the SRM (and also the Balboa Instruments PowerPacer and the LOOK Max One) in the mid- to late-1980s, however, that it became possible to routinely measure a cyclist’s power when riding outdoors.

This new technology was rapidly adopted by forward-thinking professional cyclists (e.g., Greg Lemond), cycling coaches (e.g., Francesco Conconi, Paul Koechli), sports scientists (e.g., Drs. Jeff Broker, J.T. Kearney, Dave Martin, and Jim Martin), national (e.g., German, Australian, U.S.) federations, and trade teams (e.g., track cycling’s Team EDS). Among the “firsts” that occurred during this time were the first use of power meters by an entire team during a stage race (by the U.S. National team at the Tour du Pont in 1994), the first use of a power meter during a mountain bike race (at a test event held on the Atlanta Olympic mountain bike course in 1995), and the first use of power data to characterize the demands of the individual and team pursuit (by the German cycling federation and also as part of USA Cycling’s Project 96). The Australian Institute of Sport also collected extensive power data on their athletes in training and in competition from the early- to mid-1990s onward and especially during the lead-up to the Sydney Olympics in 2000. Thus, by the end of the previous century power meter use had already gained significant popularity at the elite level. The high cost of the SRM, however, prevented its use from spreading much beyond these circles. Furthermore, because those with the most experience using power meters were in the business of winning races, not freely sharing their hard-earned knowledge, at the time there was very little, if any, publicly-available information about how to best utilize power data when training cyclists.

The above situation began to change rather rapidly after the Tune Corporation introduced the original PowerTap hub in 1999. Prior to its introduction, they loaned one to the well-known cycling coach and author Joe Friel for a few months, who based on his experience produced a small manual called simply Training with Power that was included with each new PowerTap. In it he explained the similarities and differences between training with a power meter and training with a heart rate monitor, and laid out a system by which a cyclists’s efforts over various durations were referenced to their actual performance ability, i.e., their power output. To my knowledge, this was the first widely-distributed resource that not only specifically addressed the question of training with a power meter, but also “elevated” power data to a position of primacy over heart rate – previously, power data had been used primarily as a way of quantifying the demands of various events (especially on the track), as a way of controlling the intensity of intervals, and/or as a way of placing heart rate data (which was still considered the primary metric) in better context. However, a comprehensive approach to utilizing power meter data had yet to be developed, or at least shared with the world at large.

The introduction of a less-expensive alternative to the SRM helped indirectly foster another important development, which was the creation, in the summer of 2001, of the “wattage” online discussion forum, which was initially hosted on Topica.com and then later moved to Google Groups. The brainchild of Kwan Low, this forum quickly became an important nexus where power meter users could share their ideas, experiences, etc. This included yours truly, who first used an SRM for a few months in 1996, then became a pilot user for PowerTap in 1999. This experience, coupled with the free-flow of information on the wattage list and a chance conversation with a cycling coach in the infield of the velodrome in Trexlertown, PA, stimulated me to try to develop a logical approach to power-based training that was grounded in sound physiological principles. Initially, this merely consisted of a series of power-based training levels anchored to what I decided to call “functional threshold power”, which I first described on the wattage list in the fall of 2001. This was followed, however, by other analytical tools, i.e., by normalized power, intensity factor, training stress score, power profiling, and quadrant analysis in 2003 and by the Performance Manager approach in 2004 (although not revealed until 2006).

Another seminal event in the very short history of power-based training was a meeting devoted to the topic that was held in conjunction with the US Professional Championship/Liberty Classic in Philadelphia in the summer of 2002. Organized by Sam Callan, the Director of Coaching Education for USA Cycling, the three speakers were Dr. Allen Lim, Dean Golich, and myself. Allen, whose dissertation research at the University of Colorado revolved around power meter use, presented an overview of how he felt power data best fit within the context of preparing cyclists for competition, especially the concept of power as a measure of applied stress and heart rate and perceived exertion as important measures of the resultant physiological strain. He also described his experience using the PowerTap as director and coach of the women’s Celestial Seasonings professional cycling team, including using it to conduct field tests to determine aerodynamic drag. Somewhat similarly, Dean shared his experience, garnered first as a physiologist for USA Cycling during the early 1990s and subsequently as a coach for Carmichael Training Systems, in using SRM data in the preparation of elite athletes such as World Champions Mari Holden and Alison Dunlap. In particular, Dean described how he used power data to target specific energy systems in training, and emphasized the usefulness of power data in determining when an athlete had “overreached” enough to induce improvements in performance. He also advocated the use of block training (i.e., multiple hard days in a row) not only for its effectiveness, but also for its time-efficiency. In doing so, he presaged one of the most important ways in which widespread use of power meters has seemingly changed the way many amateur cyclists tend to train, that is, with less total volume but with more emphasis on specific, structured efforts. Finally, calling upon my academic background I discussed issues such as the limitations of heart rate as a measure of exercise intensity (due to, e.g., cardiac drift), the cardiovascular and metabolic ramifications of the highly variable (“stochastic”) nature of power when cycling outdoors, the average effective pedal force-circumferential pedal velocity relationship and how this relates to fiber type recruitment and fatigue, the Monod/Scherrer critical power paradigm, etc. I finished my talk with a list of the top 10 things that I felt I had learned as a result of training and racing with a power meter, the last three of which were:

3) specificity

2) SPECIFICITY

1) SPECIFICITY!

Only about 30 people were present in the overheated Philadelphia conference room in which this meeting took place, such that its impact could well have proved to be relatively inconsequential. Among the attendees, however, were Hunter Allen, a professional cyclist-turned-coach, and Kevin Williams, one of his clients and a skilled computer programmer. After listening to the speakers discuss various ways in which power meter data could be manipulated to advantage, they recognized the need for more sophisticated software that would make such calculations easier and more accessible to everyday users, and over lunch that day they quite literally sketched out their plans to produce such a program on the back of a napkin. After further development, Hunter invited me to join the effort, with the final result, a program called CyclingPeaks (now TrainingPeaks WKO+), being released in the fall of 2003. This actually made it the 2nd such aftermarket program for analyzing power meter data (after Paul Koechli’s PowerCoach, which was released in 1996), but Hunter and Kevin’s program achieved much greater popularity due to its features, ease-of-use, lower price, and the fact that it ran under the most popular operating system, i.e., Windows. (A Windows-based alternative to the original SRM DOS software had previously been developed as part of Project 96, but was not sold to the general public.) In turn, the success of CyclingPeaks/WKO+ inspired the development of a number of other quite similar commercial and open-source programs (e.g., RaceDay, Golden Cheetah, SportsTracks. As well, our (i.e., Hunter and my) association led to the development of many of the analytical tools listed two paragraphs above, as well as our co-authoring of the book Training and Racing with a Power Meter, the 1st edition of which was published in 2006 and the 2nd in 2010.

Yet another consequence of the confluence of events represented by the Philadelphia meeting was the decision by Sam Callan to offer a special certification in power-based training to coaches licensed by USA Cycling. Due to the burgeoning popularity of such devices among both coaches and athletes alike, this program quickly grew from its inception in 2005 to where approximately 100 coaches now attend these two day seminars every year.

Today, power meter use has grown to the point that there are approximately a half-dozen such commercial devices on the market, and a number of enlightened professional teams (e.g., Garmin-Transitions, Cervelo TestTeam) equip all of their riders with one and hire consultants who specialize in interpreting the data they provide. Even at the local level, up to a third of the riders in some fields can be seen racing with a power meter. Indeed, in December of 2009 VeloNews listed the growth and development of training with power as the 3rd most significant story of the last decade. Thus, approximately 20 y after Uli Schoberer’s development of the SRM (and a little over 100 y after the invention of the stationary cycle ergometer), the use of power data in the preparation of racing cyclists can truly be said to have become mainstream.

The author would like to thank Sam Callan, Dean Golich, and Dr. Jim Martin for their feedback during the preparation of this article.

The "other" quad...

(Reprinted with permission from the March 2010 issue of ROAD magazine: http://bluetoad.com/publication/?i=31609)

by Hunter Allen - Let’s think about those leg muscles and all that they do, especially the quadriceps muscles (the big ones on top of your upper leg). The ‘quads’ tend to be the leg muscles that get the most work and also the most sore, and the quads contribute a significant amount of work toward propelling you forward on a bicycle as they are the muscles that help to push the pedal downward on each stroke. As you know, sometimes you have to contract your quads more forcefully when your cadence is slower and when you are going up a steep hill for example and other times you don’t have to push down very forcefully at all, but your cadence is very quick. The quadriceps, along with the rest of the lower body muscles, need to be able to contract forcefully and slowly and also contract lightly and quickly in order for you to become a successful cyclist. To me, it’s another one of the great things that makes cycling so challenging: You need to have the ability to pedal both hard and slow, along with easy and fast. The best cyclists can do a bit of each and while even the best cyclists have strength in one or the other, they also train these skills as well to improve the weaker of the two skills. You see, the rider that feels more comfortable mashing a bigger gear most likely has more ‘fast’ twitch muscle fibers (type II), whereas the rider that likes to ‘spin’ typically uses more slow twitch fibers (type I) and this is important because if you event is going to require you to pedal hard and slow, but in training you always pedal easy and quick, then you might not be ready for your event.

This is where the ‘other’ quad comes into play. That other quad is called Quadrant Analysis. Quadrant Analysis is a tool that allows you to understand whether or not you are indeed pedaling correctly for your given event. What does ‘correct’ pedaling mean? Well, thinking back to this idea that “training to the specific demands of the sport is paramount to succeeding in that sport” and of course you would agree that practicing basketball all day is not going to help you win a 100 mile road race, and the same applies within the sport as well. Riding at a cadence of 100 rpm for 3 hours is not going to prepare you well for a race that is going demand that you ride at 80 rpm for 2 hours and then 100rpm for the last hour. You just simply are not training specifically for the demands of the event. This is where Quadrant Analysis comes into play.

Scientific studies using a variety of techniques have found that threshold power (FTP) represents not only a threshold in terms of the power that an athlete can sustain, but also somewhat of a threshold in terms of fast-twitch fiber recruitment. To state it another way: When pedaling at a typical self-selected cadence, functional threshold power appears to occur at the power (and thus force) at which significant fast-twitch fiber recruitment first begins. Thus, not only does cardiovascular fitness play a role in your success, but so does your neuromuscular function. Neuromuscular function sounds complicated, but it simply means how fast you can contract a muscle, how strongly you can contract it, and how long you can keep it contracted before relaxing it again. Even though no commercial power meter has the ability to directly measure the forces applied to the pedals, it’s possible to derive the average (over 360 degrees) effective (tangential to the cranks) pedal force (both legs combined) or AEPF from the power and cadence data. (Refer to page 132- Training and Racing with a Power Meter for more info). One must also understand the relationship to velocity in order to really get a better understanding of this and circumferential pedal velocity (how fast the pedal moves around the circle it makes) or CPV can also be derived from cadence and crank length.

What does this mean to you as a cyclist? Well, it means that with your power meter and Quadrant Analysis, you can make sure that you are indeed training properly for the cardiovascular AND neuromuscular demands of your event. Enough of this physiology speak; let’s examine some different Quadrant Analysis plots so you can understand how to apply this in your own training. The first plot is a plot showing you what a typical criterium would look like. Quadrant Analysis is incorporated into the newest version of the WKO+ software-Version 3.0 and is available online at http://www.trainingpeaks.com/. Figure 1 (below) shows how most of this race was spent in quadrant II (QII; high force, slow pedaling) and quadrant I (QI; high force, fast pedaling) and this is characteristic of a criterium in which the rider has to keep a high cadence to respond quickly to changes in speed, along with hard sprints which come relatively often either out of turns, for premes or for hard attacks.

Figure 1. Quadrant analysis of a typical criterium.

Quadrant Analysis is useful to first gain an understanding of just what the plots represent and then compare them to each other and to training. Let’s examine another race, this time the Tour De France stage win by Markus Burghardt in 2008 (Figure 2 below).

Figure 2. Quadrant Analysis of Markus Burghardt's Tour de France stage win in 2008.

In this Quadrant Analysis plot, we see an incredible amount of time, over 72%, in quadrant IV (QIV), which is very surprising since Markus is quite a large rider and one would assume he has a higher percentage of type II muscle fibers and he might self-select his cadence at a lower cadence with higher force. However, one explanation might be that this demonstrates how incredibly fatigued his muscles are (stage 18 after all!) and that he had to pedal with a higher cadence in order to produce the same wattage. The more fatigued we are, the less strength we have in the type II muscle fibers and therefore we have to shift our recruitment to more type I muscle fibers in order to achieve the same FTP. Another explanation might be that he simply is very good at conserving muscular strength and has learned over the years of training and racing that if he keeps his cadence over 95rpm, then he’ll have more energy in the finale since type II (fast twitch) muscles require more glycogen and the more he conserves the better he’ll be towards the end. Lastly, the red colored points in this plot are the points from the three climbs on this stage and shift to QII, as he has to produce a higher force at a slower cadence in order to get over the climbs near his FTP. Points in QII require a tremendous amount of glycogen, so time spent in QII is very ‘expensive’ so to speak from an energy standpoint, but at the same time a necessary ‘expense’ if he Markus was to stay with his breakaway companion.

Let’s examine one more power file from yet a different discipline and then we’ll take a look at how important it is to do Multi-File Quadrant Analyses as well. In this example, let’s take a look at a flat time trial.

Figure 3. Quadrant Analysis of a flat time trial.

A flat time trial produces another interesting and unique plot. In Figure 3 above, the yellow points that are following the ‘threshold curved lines’ demonstrate that instead of wildly fluctuating watts and cadence, his wattage stayed nearly constant (just below, at or just above FTP), while only his force and cadence changed. He focused his effort and maintained within a narrow range his wattage, so narrow that when you examine the percentage of time spent in QII and QIV, we find that he spent almost identical time in each. This does make sense as he was trying to maintain his FTP for the entire time and was limited by his cardiovascular system, but could still change the AEPF and CPV while maintaining his threshold power. This Quadrant Analysis plot also helps us to see that a time trial demands hardly any time in QI (maybe only the start and turn-around) and a flat time trial is characterized by an enormous amount of solid, steady hard effort, which is very obvious to anyone that has done a flat time trial, but on the other hand if you are only doing group rides and mass-start races, you might not be ready for the constant neuromuscular demand that a time trial takes.

Now that you have a good understanding about the different quadrants, the different shapes of plots, let’s compare two different workouts together. The purpose of this is very similar to how you might want to use this yourself. Let’s compare a training ride to a race and see if the neuromuscular demands are similar. If they are, then great! That means the athlete is training in the correct quadrant and should be well suited for the racing demands. If not, then the athlete needs to figure out a way to train in the same quadrant(s) as the races in order to better handle the neuromuscular demands of racing. In this example, let’s compare a group ride in which the athlete did, including a series of hills in preparation for an upcoming hilly road race against the actual race which had a steep hill in it.

Figure 4. Multi-file Quadrant Analysis of a hilly training session vs. a hilly race.

In Figure 4 above, we see the race in red and the training ride in yellow and right away we can see that they do not match each other. The training ride had a lot more time in QII and Quadrant III (QIII) then the race did. The yellow points in QII represent the hard hills in the group ride and it appears that the hills in the group ride demand a lot higher forces than the hills in the race, which are represented by the red points in QI. So, this means that the hills in the race were more like sprints (high force and fast cadence), whereas the hills in the group ride were more like…well…hard steep hills (high force and slower cadence). Note another significant difference as well, which is that the amount of time in QIV for the race (35%) versus for the group ride (20%), and this 15% difference is certainly significant as pedaling fast and not too hard is a critical skill to have in mass start races as you have to be able to match speed changes in order to stay on the wheels in front of you. In this example, the group training ride did not match up well with the actual race the athlete was training for, so my recommendation would be to skip his group training ride and do a ride more closely aligned with the upcoming race.

In our last example, let’s compare a criterium to a micro-burst workout. A micro-burst workout is one in which you ride at 150% of FTP for 15 seconds “ON” and then at 50% of FTP for 15 seconds “OFF” and you continue to repeat this for a period of ten minutes, often 3-6 sets of them. The micro-burst workout is a great one to do in preparation for a criterium or any type of ‘bursty’ cycling event, including cyclo-cross. The workout can be done on either an indoor trainer or outdoors on the road. When we create a multi-file Quadrant Analysis using the WKO+ version 3.0 software, we see right away that the micro-burst workout (yellow points) has a significant portion of time in QI and so does the criterium (red points).

Figure 5. Multi-file Quadrant Analysis of a micro-burst workout vs. a criterium.

This is where the two workouts are similar and the “ON” portion of the micro-burst workout matches up well with the criterium, so that would be a nice example of training specifically for the demands of the event. However, most of the criterium is actually in the QIV, which is low force, and fast pedaling and most of the points in QIV would be criteriums, but the rest period (OFF) in the micro-burst workout is in QIII, which is low force but slow pedaling. So, the athlete in the off period needed to maintain a higher cadence in the “OFF” period than he actually did in order to even better simulate the criterium. I would suggest that both the “ON” and “OFF” periods, the cadence should be in the 90rpm or higher range. Besides this, I would say that this workout was a good example of trying to match up the demands of a criterium with a workout based on power.

In conclusion, it’s not just your cardiovascular output (FTP) that determines your success as a cyclist. It’s also your neuromuscular output or how you create the watts that also determine your ability to succeed. Each of us has strengths and weaknesses related to how we prefer to create the watts. Some like to pedal at a faster cadence and some of us prefer to use a slower cadence but push harder on the pedals and while neither is necessarily better or worse than the other, certain races and terrain demand more of one than another. The key for you to understand is that when you train, you must train specifically for that event which has the unique demands so that you will be ready for those demands. If you need to be able to go up a 15% hill and do it in your 23 tooth cog, then you had better make sure you train in QII enough to be ready for that much muscular strength. If you are going to do a time trial, then it’s important that you are ready for a sustained hard effort in QII and QIV, without any ‘recovery breaks’ in QIII. These are just some of the examples of how important understanding the neuromuscular demands are in racing and training and as you go into your 2010 season, it makes good sense to get a clear picture of the exact demands you’ll need to meet in order to win!

Which is faster: the Cervelo P2T or the Javelin Arcole?

(First posted to the internet in 2005.)

by Andrew R. Coggan, Ph.D. - Faced with the question of which of these two frames to use for the pursuit at master track nationals in 2004, I opted for the Cervelo. I did so because 1) unlike the Javelin, it is a true track frame with horizontal fork ends, thus making gear selection much easier, and 2) this particular P2T had a winning “pedigree”. However, after an unexpectedly poor performance at nationals due in part to higher-than-expected aerodynamic drag, I began to wonder if perhaps I had made the wrong choice. In the fall of that year I therefore conducted a series of field experiments using my powermeter to see if I could discern any difference in aerodynamic drag characteristics between the two frames. The results of this study are described in this report.

Methods

Data collection

To compare the two frames, I used an SRM Professional track crank to measure the power required to propel them at steady speeds ranging from ~20 to ~50 km/h. I used these data, along with measurements of barometric pressure and air temperature (to calculate air density), to determine my effective frontal area (i.e., CdA in m^2; product of coefficient of drag, Cd, and frontal area, A) on each bicycle (see Data Analysis). These tests were performed on a ~1 km segment of a very flat, smooth, asphalt road (i.e., Centaur Road in Wildwood, MO). The influence of wind was minimized by 1) using the westernmost portion of this road, which is sheltered by dense woods, 2) collecting data immediately after sunrise and only on days when wind speeds were minimal (i.e., less than 0.5 m/s), and 3) performing 6-9 “runs” in both the easterly and westerly directions and in a random order. With this approach, I was able to estimate my CdA to within ±2%, or with approximately the same degree of precision as can be achieved when testing a pedaling rider in a wind tunnel.

I tested both the Cervelo and the Javelin using two different positions, i.e., once with a 17 cm drop from the saddle to the elbow pads of the aerobars, and once with a 20 cm drop. These distances, along with saddle height, saddle setback, distance from saddle to end of elbow bars, etc., were all confirmed by careful measurement. Fortunately, the two frames had essentially the same “reach” and “stack” (i.e., length of top tube forward of, and height of the top of the headset above, the bottom bracket, respectively). Thus, after positioning my saddle in the same location relative to the bottom bracket on each, all that I needed to do to ensure that my position was the same on both of them was to simply transfer the same handlebars and stem from one to the other.

During all trials, I wore the same technical fabric T-shirt, skinsuit, socks, shoes, shoe covers, and Troxel Radius II helmet. Furthermore, in addition to using the same handlebars (Oval Concept A700) and stem on each bike, the following components were also kept the same:

Fork: Cervelo Chord

Front brake/brake lever: Shimano Ultegra/Tektro 4.0

Front wheel: Zipp 404 with Veloflex Record tubular inflated to 125 psi

Rear wheel: Hed track disk with Tufo S3 tubular inflated to 135 psi

Crank/bottom bracket: SRM Professional track model/generic square taper sealed bearing

Pedals: Speedplay X-2Saddle: Avocet O2 Air

Thus, the only things that differed between tests were 1) the frame, 2) the seatpost, and 3) the chainring and cog used (i.e., 53x13 on Cervelo, 50x14 on Javelin). Different seatposts had to be used because of the Cervelo’s proprietary aerodynamic design, whereas different gearing was used to keep the distance between the trailing edge of the seat tube cut-out and the rear tire the same (i.e., 1.0 cm) on both bikes. Although in theory this could have resulted in a difference between the two bicycles in drivetrain efficiency, any such difference was considered likely to be insignificant and less important than standardizing the frame-tire gap. Finally, to make the comparison the two bikes as easy to interpret as possible, I used electrician’s tape to seal over openings in the Javelin frame normally used for internal routing of brake and shift cables (however, I chose not to saw off the front derailleur hanger!).

Data analysis

Following completion of each set of trials, data were downloaded from the SRM handlebar computer into TrainingPeaks WKO+ (http://home.trainingpeaks.com/wko-desktop-software/analysis-software-for-training-files.aspx) for subsequent analysis. The average speed and power during each run was first determined, taking care to ensure that the speed was identical at the starting and ending points of each run (to eliminate variations in stored kinetic energy). These directly-measured power data were then adjusted downward by 2.5% to account for frictional losses in the drivetrain. This value was assumed based on the results of published scientific studies as well as extensive comparisons of this specific SRM crank to other power-measuring devices that sense power at the rear wheel (i.e., PowerTap, Velodyne). The power and speed data were then analyzed by fitting them to a curvilinear regression of the form:

Y = aX + bX^3

where Y is the power (in W) and X is the speed (in m/s). As such, the constants a and b represent rolling resistance (in N) and the product of one-half times the air density (in g/mL) times CdA (in m^2), respectively. Air density was calculated based on air temperature and barometric pressure at the time of each trial as reported online from the nearby (~2 km) Spirit of St. Louis Airport in Chesterfield, MO. Furthermore, the absence of any significant local variations in temperature was confirmed by comparing those reported from the airport to those measured on-site using the SRM handlebar computer. To check for possible gradient- or wind-effects, data from easterly and westerly trials were first analyzed separately; however, no significant trends in the data were apparent. Data from both easterly and westerly trials were therefore pooled for further analysis, yielding a single estimate of CdA per day/condition (bicycle set-up).

Results

Data from a representative series of runs are shown in Figure 1, whereas the overall results are shown in Table 1, both of which are shown below.

Figure 1. Power vs. speed relationship from a representative series of measurements.

Table 1. Complete results.

As displayed graphically in the figure and as demonstrated by the low standard errors of the estimate shown in the table, the model provided a very close fit to the experimental results. Furthermore, in both positions my CdA was lower when riding the Cervelo than when riding the Javelin, although the magnitude of this difference was only slightly greater than the uncertainty of the measurement.

Discussion

It must be emphasized from the outset that the difference in CdA between the Javelin and Cervelo was so small that it could have been entirely due to chance alone. On the other hand, the fact that the magnitude of the difference was consistent across the two positions, as well as the fact that it was apparently possible to detect a difference between the two positions in the first place, suggests that the difference in CdA between the two bikes may in fact be real. Although small, a difference of the magnitude observed (i.e., ~0.005 m^2) is potentially quite significant in competition, as at normal racing speeds it would result in a time differential of ~0.5 s/km, e.g., ~1.5 s in a 3 km pursuit or ~20 s in a 40k TT.

While the data suggest (but do not prove) that the Cervelo has less drag than the Javelin, the reason why there might be such a difference is not immediately clear. Obviously, however, the difference must lie in the frame (and/or seatpost) itself, since all of the other components were the same. It is therefore interesting to compare and contrast the specific design features of the two frames, even though it is impossible to draw any definitive conclusions.

The P2T is the track version of Cervelo’s ubiquitous P2k time trial/triathlon frame, which in turn is the successor to their original P2. As such, it features down and seat tubes with Cervelo’s now-familiar NACA (or NACA-derived/inspired) profiles, i.e., tubes with a relatively high aspect (i.e., chord-to-thickness, or depth-to-width) ratios, rounded leading edges, and sharp trailing edges. The down and seat tubes of the Javelin Arcole, on the other hand, are not only slightly wider, but are blunter on their leading and especially their trailing edges. According to the designer, John Cobb, these tube profiles were specifically chosen to minimize drag at typical yaw angles encountered during “real world” cycling (and in fact the tube profiles resemble those found on other aerodynamic frames designed by Cobb, e.g., the Trek TTT). Thus, one possible explanation for the present results is simply that the test conditions favored the Cervelo over the Javelin because they were, of necessity, conducted when there was minimal wind. Alternatively, it is also possible that the Javelin’s wider tubes themselves accounted for the apparent difference in CdA between the two frames. This interpretation is consistent with the fact that, based on the results of competitive time trials using a powermeter, my CdA appears higher when riding either the Cervelo or the Javelin compared to the Hooker Cat. 1 frame that I used previously. The down and seat tubes on the non-UCI-legal Hooker were even narrower than those found on the Cervelo, although other features of the Hooker (especially their proprietary handlebars) could also explain this apparent difference.

Finally, it also possible that some other difference between the Cervelo and Javelin explains the apparent difference in CdA that was observed. For example, as previously mentioned the Cervelo was tested using their proprietary aerodynamic seatpost, whereas the Javelin was tested using a narrow (25.0 mm) but round Selcof Bi-Position seatpost. On the other hand, the Cervelo P2T (and its road counterpart, the P2k) has round seatstays, whereas the seat stays of the Javelin Arcole have a flat/oval profile for most of their length (changing to round near the drop-outs). Again, whether these or other less-obvious differences between the two frames account for the apparent difference in CdA – assuming, again, that it is real – cannot be determined from the present results.

Power vs. duration: the "critical power" concept

(First posted to the internet in 2002.)

by Andrew R. Coggan, Ph.D. - A lengthy, but hopefully informative and useful, post...

There have been a number of equations presented in the scientific literature describing human power output as a function of time, some derived from first principles modeling based on the underlying physiology, and some simply derived empirically. One of the simplest and most robust, though, is the original "critical power" concept of Monod, first proposed around 1960. Various formulations of this have been presented, but the original equation is a hyperbolic of the form:

t = W'/(W(dot) - W(dot)cp)

where t = time to exhaustion, W(dot)cp is the work rate (i.e., power)asymptote, and W' represents the degree of curvature of the relationship.In this form, time is the dependent variable, being determined by the rate of doing work, i.e., power output (W(dot)) in relation to the individual's critical power (W(dot)cp).

While this is logical (i.e., how long you can go is determined by how hard you are going relative to your own ability), actually fitting data to such a curvilinear relationship isn't esp. convenient. Hence, it is common to rearrange the equation to yield a linear equivalent, i.e.,:

Wlim = W' + W(dot)cp * t

In this expression, Wlim is the total amount of work accomplished during a given exercise bout and is the product of W(dot) and t (i.e.,since power = work/time, work = power * time). The above equation is of the form y = mx + b (or y = a +bx, if you prefer), with the slope of the line (i.e., W(dot)cp) being a person's"critical power".

Conceptually, critical power is a power that can be sustained "for a very long time without fatigue", and is "an inherent characteristic of the aerobic energy supply system". On a practical basis, critical power has been shown to correlate very closely with power at lactate threshold, although it may in fact be significantly (both statistically speaking, and from an athlete's perspective) higher depending on exactly how critical power and lactate threshold are measured/determined. On the other hand, the y-intercept of this relationship, W', represents a fixed amount of work that can be accomplished during an exercise task to fatigue, but is non-renewable. Conceptually, this parameter reflects anaerobic capacity (not power), i.e., the total amount of energy that can be liberated from non-aerobic energy sources, i.e., from breakdown of high energy phosphate stores (ATP and PCr) and via production and accumulation of lactate. Support for this interpretation comes from experiments showing a close correlation between W' and total work performed during an all-out 30 second exercise test (i.e., a Wingate test), and between W' and maximal accumulated oxygen deficit. Moreover, critical power has been shown to be influenced by interventions that would be expected to affect aerobic energy production, e.g., hypoxia, whereas W' is not. Conversely, interventions expected to influence anaerobic capacity, such as creatine loading or resistance training (in untrained persons), have been shown to alter W' without changing critical power. Finally, whereas most of the research involving critical power has been performed using cycle ergometry (due to the convenience with which power can be measured), this conceptual formulation has been shown to closely describe performance in other sports, as well as the performance or function of isolated muscles/muscle groups.

The critical power concept is not without its limitation...in particular, it tends to greatly overestimate the maximal power that can be generated for only a few seconds, and it predicts that there should be a power output below which fatigue will never occur. In addition, the exact values obtained for W' and critical power depend in part on the testing protocol, e.g., the exact combination of powers and durations used to define the curve, how fatigue is defined, etc. Nonetheless, despite its simplicity this equation describes the power vs duration curve quite well over a wide range of exercise intensities/durations,i.e., from perhaps 20 seconds out to several hours.

Relevant to the purposes and interests of this group, I think the critical power concept is useful for two reasons. One, it provides a very good conceptual framework for understanding the most basic factors determining exercise performance/power output (i.e., anaerobic and aerobic energy production), and how the contribution of each varies as a function of time. Two, actually measuring critical power is well within the capacity of anyone who owns a power meter (and understands a little bit about math), and thus provides a means of quantifying changes in fitness beyond just even "I was able to sustain X watts for Y seconds!". In other words, determination of critical power allows one to determine whether it is chances in anaerobic capacity or aerobic function (lactate threshold)accounting for any changes in performance, w/o requiring a trip to a laboratory or invasive measurements (blood sampling). This would be especially true for people using the SRM software, since it allows you to "extract"the necessary power and duration data from regular training and racing. (Although it should be noted that W' declines in direct proportion to W(dot) during the period of time immediately before the effort...as demonstrated by how hard it is to sprint very fast at the end of a TT!).