In part 1 of this article:
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):
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:
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 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.