Friday, April 30, 2010

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.


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.

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