The Aging Athlete (3) – Women’s 100m

Photo courtesy Dan Slovitt

The previous post summarized and trended some of the 100m data (2013-2016) from mastersrankings.com I have begun analyzing. We will look in more depth at the Men’s results soon, but first let’s explore some of the finer points of all the available performances in the Women’s 100m. For a refresher on how the data is gathered and processed, please go here.

As pointed out in the previous post, 8,626 Women’s 100m “best annual” performances are included in this analysis (772 wind assisted performances not included). An issue for Masters is how to encourage greater female participation. Unfortunately, women participate in our sport at the Masters level in far fewer numbers than men. Hopefully, sprint role models such as Karla Del Grande, Carol Layfayette-Boyd, Irene Obera and other top women’s competitors can encourage more women to do the training, don a pair of spikes, and try a race or two..

This chart graphs the W100m World Record against the average performance from mastersrankings.com. I have indicated in red the averages for W80 and W85, but since there really aren’t a lot of performances in these age-groups, I have not included W80 and W85 in my calculations. (They are there more as indications rather than  absolute data points) You can see the graph in full size by clicking

W100m for blogpost

The average 100m for all women is shown in the green curve, and the women’s World Record is shown in the mauve. Both curves take a noticeable climb upwards beginning at W70.  Also note the figures below the curves. These are the 5-year average declines in performance vs the previous age-group, for world records and all-Women averages. We will discuss these average declines in more detail from the table below, but for now here are a couple of points to note 1) both in the all-Women average and the WR, the declines moderate in intensity at W65, then increase again at W70. 2) The WR seems to indicate that this moderation occurs again at W75 (though the all-Women does NOT).

The table below takes the average 5-year decline and converts that to an estimated ANNUAL decline for the WR (column #3) and all-women’s average (column #4). You often hear Masters performance declines about 1% per year. Well, not exactly. Up to and including W50, the annual decline (as shown for both the WR and all-women’s average) is usually somewhere between 4/10 and 7/10 of a per cent. But beginning at W55, the all-women’s decline rapidly increases past 1% annually (at W65 it is 0.95% so pretty close to 1%) or greater. And for W65 onward, the all-women’s average slow down is more pronounced than that indicated by the WR. It would seem the very top athletes age differently than the “norm”.

Women's 100 Metres

Age - GroupWorld RecordAvg "Best" PerformanceWR Avg Decline/YearAvg "Best" Avg Decline/Year
W3510.74
14.89N/AN/A
W4011.0915.190.65%0.41%
W4511.3415.510.45%0.42%
W5011.6716.070.58%0.72%
W5512.8017.131.94%1.32%
W6013.6318.201.30%1.26%
W6513.9119.070.41%0.95%
W7014.7320.681.18%
1.69%
W7515.0322.480.41%
1.74%
W8016.81excl (not enough data)2.37%
N/A
W8519.83excl (not enough data)3.59%
N/A

 

 

We can look at this another way. As shown in the table below, the all-women’s average 100m time was 37-38% slower than the WR up to W50. For W55 and W60, perhaps surprisingly, the differential dropped to 33%, but from then on the differential escalates rapidly to 50%.

World Record 100mAverage 100mDifference (%)
W35 10.74

14.89
38.60%
W40 11.09
15.19
37.00%
W45 11.34
15.51
36.79%
W50 11.67
16.07
37.68%
W55 12.80
17.13
33.80%
W 60 13.63
18.20
33.56%
W 65 13.91
19.07
37.07%
W 70 14.73
20.68
40.39%
W 75 15.03
22.48
49.56%

My concluding thoughts? Up to and including W50, your performance likley will decline less than 1% per year. After that expect more than 1% per year.

And as women hit the W65 age-group, unless you are an elite or near elite, you shouldn’t be terribly disappointed if your Age Grade percentage slips a little. This would seem to be likely for the “average” or near average female sprinter. So carry on!

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The Aging Athlete (2) – 100m

How do Sprinters age? When does the aging effect on our speed really begin to increase its decline? Is the timing different women vs men? It’s probably neither consistent nor smooth. I’ve often heard it said that we, on average, lose 1% a year. Is this speed? Or endurance? Or both? I don’t know, but let’s see what the data appears to say.

I’m going to start with the 100m, which should be a good reflection of how “speed” declines with age.  I am using the duncanSCORE data base which contains 8,626 Women’s 100m entries up to and including W75 and 21,540 Men’s 100m performances (from mastersrankings.com for the years 2013-2016) used to calculate our scores and percentiles (if you are interested, you can get the details of the data processing here). Beyond W75  and M85 the numbers of performances are too sparse to use in our analysis.

The above graph may be a bit small to view properly. Here it is in full size Average    

What we see are some differences Women (in green) vs Men (in blue). Both genders decline in performance (vs the previous age group) generally about the same rate until “55”. This is shown in the tabular section under the graph lines and is labeled “% to Prev” (the percentage decline vs the previous age-group).   At that stage Women slow down 6.6% vs W50s, while M55s are averagely 3.99% slower than M50s.

The Men’s slowdown continues to accelerate (reaching 6.74% slower than the previous age group at M65), but then a sort of miracle happens! See where the blue line flattens a bit? The Men’s rate of decline (3.07%) is less then half the previous, but then accelerates much faster at M75 onward.

Women get their “mini miracle” to happen at W65. There the speed decline does indeed decline (to 4.74% from 6.29%), From there as you can see in the graph, the line begins its 45 degree upward slope. The Men’s roughly 45 degree slope commences at M75.

That’s it in a nutshell. I hope I haven’t bored you to tears, because the next posting will look at the Women’s 100m in more detail.

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If you are new here and this is all a little confusing and really don’t get what I’m talking about, please have a quick read that will explain the concepts behind the duncanSCORE

Delving Into The Aging Athlete (1)

We all age. That is if we are lucky enough to live so long. And supposedly, we all age at different rates. Probably that is true, but the data showing that is surprisingly light. Tracking aging is particularly important (and interesting!) for Masters athletes.

I am hoping to be able to provide some basic information on how we age as track and field athletes. Unfortunately, what we won’t be able to quantify is the slowdown by individual year ie how much is a 49 year old slower (on average) than a 48 year old. One day I hope enough data will be available for that.

And how does it differ Men versus Women? Or maybe it doesn’t? We will definitely look into that.

And perhaps in the not too distant future, we can look at how top performers tend to do over advancing years versus the average Master. I’m very interested in that.

We will start with the 100m, men and women, using 4 years of mastersrankings.com data (2013-2016). Then we will move on to some other events.

But first 100m. Stay tuned.

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Similar Thinking

Picture from One Flew Over the Cuckoo’s Nest (1975), distributed by United Artists

Sometimes it feels a bit lonely. Like being in the wilderness and shouting alone. To the best of my knowledge, creating and using a “standardized score” for Masters Athletics has not been done before. Previously, I outlined the reasons that led me on this path.

While I might think it’s pretty darn logical to wonder how you rank within your peer age group, traditional age grading has been in use for 30 years or so, and even if its creation is often misunderstood, and one of the most common ways it is used not approved, it nonetheless is ingrained in Masters athletics. It’s why I term the duncanSCORE an alternative. Not a replacement.

And that’s why it’s interesting and a breath of fresh air to find others who share much of my thinking. A young athlete (Alexis Spinetta) has put up on her blog a very good critique of age grading and done some pretty cute calculations to derive aging tables for marathoners. Check out their thinking here … http://agegradecalculator.comabout.php. 

16 years of finishers’ times for the Austin, Texas marathon have been combined and analyzed into some pretty interesting statistics. And try out your marathon finishing time in their calculator to see how you compare against your age cohort from Austin finishers, and all other finishers.

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