What on earth does this have to do with saddle height? Well, any numerical rule like this would have to be a rule of thumb. Lemond mayIt should never have observed that it worked forbeen presented as a lot ofreductive rule. I don't know exactly how the people he worked with, orrule was derived. Perhaps Lemond observed that it may have beenwas close to how they set their saddle heights. The problem is that he was working withmost of his colleagues, who were professional road cyclists, who overwhelmingly tended to be under age 40 and of European descent. 220 - age was actually based on an empirical study, whereas weset their saddle heights. We don't know how consistently he measured saddle height. We don't know the quality of records that Lemond kept.
If you did not know your maximum HR So, and iteven if that rule was an important training parameter, but it wasn't easy to measure directly, then what you could do is this. Take the average max HR for your age. Set your training zones by it. Then empirically observe how each zone feels. If you can't sustain your thresholdderived, then your max HR is probably lower than averagethere are issues of measurement error and generalizability at minimum.
Similarly, Lemond's or any other rule of thumb can be usedI would use rules like this as a starting point for your saddle height. After that, you need to see how it works in practice, and you should consider iteratively setting your saddle height based on observation. In this answer, I overviewed some empirical tests that bike fitters use for saddle height and saddle fore-aft position. SoThose tests can potentially be done by laypeople, although some of them need you would startto enlist a friend (the ones with your rule of thumb-derived height, then test itsmart trainers being especially useful). If the explanation doesn't make sense to see if it was OKyou, and keep observing and refining asthen you continue cyclingprobably want to seek a professional bike fitter.
ThusIn sum, regardless of how dated Lemond's formula is, it shouldn't have been used to prescribe an exact saddle height. Lemond isn't a scientistIf it was presented as an exact formula, so he quite understandably might notthat was an error.
How was that formula derived?
Reportedly, there is at least some explanation in Lemond's Complete Book of Bicycling, which I don't have grasped thisaccess to. This isn'tThis blog post says that the research behind the formula was actually done by a slight on himDr. Ginet and by former French pro racer Cyrille Guimard.
It was the product of research from Dr. Ginet, and supervised by Cyrille Guimard, who determined the ideal leg extension for maximum efficiency and power output while cycling, and made famous by the legendary cyclist, and 3 time Tour de France rider, Greg Lemond. Basically the ideal optimal saddle height is approximately 96% of full leg extension. The chart numbers come from a calculation of the inseam multiplied by .883 and then measured along the the angle of the seat tube from the center of the bottom bracket to the top of the saddle and take into consideration average shoe and cleat thicknesses.
ForIf the recordaccount is accurate (which means that the book described the process accurately and that the blogger summarized it accurately, max HRkeeping in mind that neither of them are scientists), then it sounds like Ginet did an empirical study. It was later used by Guimard and Lemond. However, that study would have been done in the late 1970s or 80s. The human body is notcomplex. Our knowledge of kinematics was definitely less advanced then, and there is likely still a useful training parameter; if training by heart ratelot we don't know about the body. For example, you're better off establishing your threshold HRthere is still no clear, definitive, empirically-based rule for setting crank length.
Footnote 1: Specifically, for 220 - ageTherefore, the intercept is 220rule might be simultaneously dated and β =still informative -1(but not absolute).
TheFootnote 1: The coefficients in that formula and probably most alternate formulae are rounded off so that they can be remembered more easily. This article tried to track down the original source for 220 - age. It's probably a 1971 article by Fox and Haskell, which showed a scatterplot and had this quote:
That strongly implies to me that theyThis study measured the max HR for a sample of people and plotted it versus age. They may have fit a linear regression model and then rounded off the coefficients for easier recall. When the parent article's authors approximated the datapoints from visual estimation and fit a linear model, they got predicted HRmax = 215.4 - (0.9147 * Age). For those familiar with regression, the intercept is 215.4 and the slope for age is -0.9147. By comparison, the 220 - age formula sets the intercept at 220 and the slope at -1.
TheLinear regression may not always be explicitly described as producing a conditional mean (in this case, that means the average max HR given age) in learning materials, but it is that. For example, go to the Wikipedia description and search for "conditional mean". Thus, the formula should always have been interpreted as an estimate for the average max HR for a given age. You can calculate a prediction error for any linear model fit to any dataset, and the paper says the prediction error was over 10 bpm in that study. However, once the formula got into practical use by lay users, misinterpretation was inevitable.