The Aging Curve(s) Jerry Meyer, Central Maryland YMCA Masters (CMYM)

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1 The Aging Curve(s) Jerry Meyer, Central Maryland YMCA Masters (CMYM) Even the well-publicized benefits of Masters Swimming cannot prevent us from eventually slowing down as we get older. While some find that depressing, most of us in the advancing categories have figured out that we can still win by beating the curve. That is, slow down less rapidly than is normal for our age group. But what is the norm? Being of an analytical bent (I have a Ph.D. in Physics, and currently work to develop new types of semiconductor lasers and detectors for the infrared), I wanted to quantify my swimming progress by comparing with the Aging Curve. But a quick internet search failed to turn up a curve I was satisfied with, so I decided to generate my own. Since the USMS web site compiles Top 10 Times for each event and age category extending back to the early days of Masters Swimming in the 1970s, that seemed a good place to start. In what follows I will concentrate on backstroke, since that is my specialty (I came to Masters Swimming three years ago as a self-taught exercise swimmer, and am now a good but not exceptional back stroker for my age. My freestyle and other strokes are mediocre at best). Figures 1 and 2 plot Men's National Top-10 times for two events ( and 200 backstroke) as a function of age. The times slow down with age, as one expects, and there is also a substantial nationwide improvement if the times are taken from 2010 as compared to any of the earlier years (several are given, in 5-year increments, going back to 1985). Time (seconds) Men -Yard Backstroke 1995 Horizontal Figure 1

2 Time/yds (seconds) Men 200-Yard Backstroke Horizontal Figure 2 The cyan points are my own times, which show that 20 years ago my 200 backstroke would have been nationally ranked. One could also surmise that if I can just hold the same time until age 65 I will be at the national qualifying level (defined as 10% higher than 10th place), and by age 70 will be in the top 10. So all I have to do is hang on, right? But the picture changes entirely if we plot the times vs. age vertically rather than horizontally, as shown in Figs. 3 and 4. By this I mean that the times follow a given group of swimmers, say born between 1926 and 19, as they age. So in 2010 they were in the age group, in 2005 they were in the group, etc., back to 1975 when they were in the -49 group. Plotting that way we find that the older cohorts (born 19 or before) are steadily slowing with time as expected. But the younger ones are hardly slowing down at all, and in several cases they are actually speeding up as they age! So the target is moving so fast that even if I maintain my times until age 70, they will still probably not beat the NQT cut-off for the current crop of ageless swimmers in my group. Projecting from this trend, Methuselah s records for the age group are still safe for now but it s only a matter of time Clearly this acceleration rather than deceleration with age represents a ratcheting up of conditioning and training regimens rather than an evolutionary upgrade of the human body. They also call into question whether even the Horizontal Top 10 data in Figs. 1 and 2 really address our quest for a reliable Aging Curve. One concern in particular is that the sampling size for each group decreases dramatically with increasing age. For example, the USMS Event Ranking web site lists 133 swimmers in the -49 age group who swam the 200 Backstroke in (the latest complete 1-year period). So Top 10 includes only the 7.5% of elite swimmers at the top of the heap. By contrast, only 23 swimmers in the group swam the same event, which

3 means that nearly half of all participants ended up in the Top 10 (an illustration of the adage that sometimes just showing up is most of the battle). The comparison of elite younger swimmers to average older swimmers is clearly unfair, and could lead to an artificial overestimation of the rate at which older individual swimmer tend to slow down. We conclude that the Aging Curves in Figures 1 and 2 are distorted, unless the specific objective is to track what it will take to break into the Top 10. Men -Yard Backstroke Vertical Time (seconds) H Figure Men 200-Yard Backstroke Vertical Time/yds (seconds) H

4 Figure 4 We can fix much of this distortion simply by using the Record Times for each age group rather than the 10 th best. We will return to that strategy below, since it should in principle provide the best indicator of what the human body is capable of, given an optimal coalescence of longevity, genetics, and training. But of course most of us are not blessed with ideal genetics, and probably do not train like a National Champion either. So it seemed worthwhile to also track how the more typical Masters Swimmer ages. This median athlete will tend to be much healthier and fitter than the general population, but does not necessarily benefit from a miracle of birth. To this end we start over using the USMS lists of all swimmers in each age group who swam a given event in a given year. Figure 5 plots the results of this procedure for both -yard (blue points) and 200-yard (red) Men s Backstroke events in the period, and also the -yard results for (the earliest time period available, green points). Here the times are broken down to every year rather than in 5-year groupings as in Figs The curves in the figure are mathematical fits that smooth the scattered data points (see Methods below) Backstroke Median Male Swimmer Time/yds (seconds) Yards Yards 09/10 03/ Figure 5 A first surprising observation is that the times for the -yard event are actually slightly faster than the results, although the small difference may be no more a statistical fluctuation. A check of the Top 10 times from these same data sets shows them to be consistent with those in Fig. 1, in that the more recent Top 10 times are significantly faster. So we have identified a real difference in the yearly evolution of Top 10 Times vs. Median Times. Whereas the elite swimmers are getting faster all the time, the times for average Masters swimmers are about the same as 6 years ago, or they may even be a little slower. This trend may in fact be an indirect consequence of the increasing popularity of Masters Swimming during this

5 same time period. Whereas 76 swimmers in the age category and 17 swimmers in the group competed the -yard backstroke in , those numbers increased each subsequent year and by they had more than doubled. We can speculate that as the sport grows in popularity, a larger fraction of the increasing number of participants have no exceptional ability but nonetheless enjoy the competition. While this democratization of the sport may dilute the median performance, it is certainly a positive trend that should be encouraged. Actually, it is reassuring that the curves for and are so similar, since this increases our confidence that the chosen procedure is robust, and that the results are not likely to depend very much on which specific time period we choose to analyze. We also note from a closer examination of the 200-yard times in Fig. 5 that they are slowing down at a slightly faster pace than the -yard times. In Figs. 6 and 7, we replot the results in a form that makes them easier to use as Aging Curves. The smooth curves in each figure represent the increase of the median swimmer s time relative to the median time at age. As an example, the median time for the -yard event (Fig. 6) doubles between age and age 87, whereas for the 200-yard event (Fig. 7) it doubles a little sooner, at age 85. Despite the popular mythology that younger people are better at sprints while age is best suited for distance, the actual trends for running as well as swimming go the other way. Figures 6 and 7 also show the Top 10 and U.S. record times, again normalized to the performance at age. Whereas the Top 10 results start out below the median curve and then move above in the older range (due to the smaller sampling size for those ages as discussed above), the U.S. Record results consistently remain somewhat below the Median. This appears to reflect a real difference in aging trends between elite vs. more typical athletes Men -Yard Backstroke Top10 Time/Time(Age ) Median U.S. Record Figure 6

6 Men 200-Yard Backstroke Top10 Time/Time(Age ) Median U.S.. Record Figure 7 These figures give us the choice of comparing with the typical trends for either elite or median swimmers. Whenever we beat the curve, we are justified in concluding that maybe age has not exacted so great a toll after all. Of course the results here apply strictly only to Male Backstroke swimmers, although to a first approximation the results for other genders and events will probably not differ so greatly. Methods To obtain the curves in Fig. 5, the points in the plot were first inverted to obtain speed. A second-order polynomial was then used to perform a least squares fit to each data set. Finally, the fits were re-inverted to provide smooth plots of time as a function of age. Although a few times are available for swimmers over 90, those were ignored here because the sample size was small and the results tended to be erratic. Several Aging Curves for Masters Swimming times have been published previously, although I was not aware of most of them until the analysis described above was already complete. Most are based on National Championship or Record times rather than medians or averages, with one exception noted below. Tanaka et al., J. Appl. Physiol. 82, 846 (1997), Age and gender interactions in physiological functional capacity: insight from swimming performance. Times vs. age based on Top 10 -m and 10-m Freestyle times in USMS LCM Championships.

7 Tanaka and Seales, J. Appl. Physiol , (2008), Endurance exercise performance in Masters athletes: age-associated changes and underlying physiological mechanisms. Discusses some of the body chemistry and physics that are most affected by aging. Bongard et al., Age 29, 77 (2007), Effects of age and gender on physical performance. Fits distance vs. age for the one-hour postal swim. Their methodology is closely related to the median approach described above, since they fit distance vs. time (speed), use a second-order polynomial, and fit to data for all swimmers rather than just the record holders or other elite. Besides being a different event, the biggest difference is that they weight all participants equally, whereas I weighted the age groups equally. That study obtained a less rapid decline with age, which may reflect either a real difference in the events or a self-selection of who tends to participate in different types of event. Amersham Age Adjusted Formula, based on the Finnish Compression Factor - Devised in Finland in See the web site watermarks.ws/age%20adjustment.txt LMSC of Virginia Times Ratings, based on SCY, SCM, and LCM National Records They fit the times for age groups through and each event to an exponential curve. See the web site vaswim.org/record_curves/time_ratings.shtml All of these studies show up in a google search for Masters Swimming aging curve. Regrettably, I have not attempted any comprehensive documentation or summary of previous work.

A REVIEW OF AGE ADJUSTMENT FOR MASTERS SWIMMERS

A REVIEW OF AGE ADJUSTMENT FOR MASTERS SWIMMERS A REVIEW OF ADJUSTMENT FOR MASTERS SWIMMERS Written by Alan Rowson Copyright 2013 Alan Rowson Last Saved on 28-Apr-13 page 1 of 10 INTRODUCTION In late 2011 and early 2012, in conjunction with Anthony