The Aging Athlete (5) ENDURANCE – 1500M

Photo courtesy of Rob Jerome

It’s time now to see what aging does to our ability to continue on at an above “normal” pace … our endurance. And the 1500m (the “metric mile”) is as good a place as any to start that analysis.

While the 1500m certainly packs a speed element into it, by far the bigger slice of success revolves around the endurance component. Look how John Walker (never the quickest thoroughbred on the course) uses his superb endurance to win the 1500m in the Montreal Olympics.

That’s all fine and good! But once we have endurance, how long do we hold on to it? And to what extent? How fast does it slip away?

First, let’s look at the trend of the 1500m as we age. This chart shows the trend of the AVERAGE “best performance” times from mastersrankings.com (2013-2016). [For new readers to this blog and the background behind this site, you can go here to read and understand how the statistics I use are pulled together.] The chart compares Men’s and Women’s 1500m averages over time. Get a blown up look at the chart by clicking here.

MW 1500m

All times are in seconds.

You will probably note, both the Men’s and Women’s curves are quite flat in the beginning. For Men, the average ANNUAL decline is somewhere around 1/2% (see the table below)  until M45 .(Note the “annual” decline I refer to is simply the arithmetic average of the loss of performance from the previous age-group divided by 5. It’s not foolish to assume the majority of that decline is in the final 2 years of the age-group, while the first 2-3 years’ performance hit is likely to be much less pronounced.)  At M50 the yearly decline doubles to 1%+ up to M60, then edges upward until it reaches over 2% at M75, then over 3% annually thereafter.

.For Women 1500m runners, the average ANNUAL slowdown begins very much like the Men. Performance slips 1/2%-3/4% every year until it hits 1% at W55. Then the decline reaches 1.8% (W60), eases back smartly at W65 (1.25+%), and then starts to increase again thereafter You can see all the details in the table below..

Age-GroupMen's Avg 1500mM Avg Decline per YearWomen's Avg 1500mW Avg Decline per Year
M-W 354:48.68
n/a5:40.28
n/a
M-W 404:55.40
0.47%
5:46.87
0.39%
M-W 455:03.32
0.54%
5:58.94
0.70%
M-W 505:18.34
0.99%
6:12.08
0.73%
M-W 555:36.20
1.12%
6:31.57
1.05%
M-W 605:58.07
1.30%
7:07.06
1.81%
M-W 656:30.39
1.80%
7:34.08
1.27%
M-W 707:03.01
1.67%
8:28.48
2.40%
M-W 757:47.97
2.13%
9:44.77
3.00%
M-W 809:00.69
3.11%
10:42.29
1.97%
M 8510:32.50
3.40%
n/an/a

To sum up on our ability to hold our endurance … we lose 1/2%-1% per year until we hit age 55. Then the next 10 years (Men to M70) our performance will decline somewhere between 1.3% and 1.8% annually. In our 70s the performance decline gets a little faster … 2%-3% per year.

You know what? That’s really not too bad at all!

Next we will go into more detail on the Men’s 1500m

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The Aging Athlete (4) – Men’s 100m

Photo courtesy Dan Slovitt

The previous posting analyzed the available statistics on Masters Women’s 100m and tried to make some sense of all the numbers. Let’s do the same with the Men’s 100m and follow where the numbers lead.

We have charted the average time for Men’s 100m by age group (in blue) versus the existing World Record (in red) for each age-group (note the M90 average is blank because the number of performances is fairly low from our reporting years 2013-2016). See full size Mens100m

One thing to note is the early Men’s WRs (M35 and M40) which are 9.92 and 9.93, held by 2 incredible Olympians (Justin Gatlin and Kim Collins). Secondly, except for the big increase between M40-M45 in the WR (and that is due to the incredibly small increase of 0.01 second in the WR for M40 by Kim Collins), the upward slope (ie the decline in performance) is gentler for the WR line than it is for the “average” male sprinter. But both lines slope rather gently (certainly more gently than Women’s) until M80 in the average line, and M90 for the WR.

As the table below shows, the average ANNUAL decline in 100m performance is very similar if you look at the WR and at the average as I have calculated from mastersrankings.com. Close, but generally we slow down a little bit more for the “average” than the WR might indicate. That is until M75 when the difference becomes much more pronounced (and perhaps this is partially due to the decreasing participation rate in the older age-groups)

Typically your performance will erode around 1/2% per year until M50, 3/4%-1% or so annually until M70, and then  1 1/2-2% a year until you hit 85.

Age-GroupWorld RecordWR Avg Annual DeclineMen's Avg 100m TimeAvg Annual Decline
M359.92n/a
12.58n/a
M409.930.02%
12.930.55%
M4510.721.59%
13.180.40%
M5010.880.30%13.590.61%
M5511.300.77%
14.130.80%
M6011.700.71%
14.820.97%
M6512.311.04%
15.811.35%
M7012.770.75%
16.300.61%
M7513.491.13%
17.551.53%
M8014.351.28%
19.442.16%
M8515.081.02%
21.552.17%
M9017.533.25%
n/an/a

So as I tried to point out in the Women’s case, even though your training is good and consistent, as you move into a different age-group, your age-grading may slowly get worse, and you may not understand why.

Here’s the answer. Not to worry. You’re typical!

I plan to investigate this with more cuts at the various percentiles to see where the changes begin to occur between “elite” and average. I’m very curious about this.

But my next project is to look at the decline over time in endurance. So next up will be the 1500.

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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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So … How This All Started

above photo courtesy of Dan Slovitt

Header photo courtesy of John MacMillan

The “beginning” for the duncanSCORE was the very late autumn of 2015.  Without going into details, I was on committees that were selecting “Athlete of the Year” in a couple of jurisdictions. My input involved athletes’ successes in terms of medals won at various championships, and records set.

Needless to say athletes in different age groups and across the track and field event spectrum were involved. Picking winner(s) was difficult. Often the discussion came back to “well Abc had an Age Grading of xx.xx% while Xyz‘s Age Grade was only xx.xx-1%”.

After much back and forth, I gave up, infuriated. Comparisons were being made across age groups and events and disciplines, and the Age Grade differences in my mind were insignificant. Knowing that the sample “sizes” for each age year and event were only ONE, and the model created included some manual “tweaking”, I knew this was a very inappropriate use of Age Grading. Believing that an Age Grade to the 1/100 was significant over a similar result from a different age group and event was ludicrous.

There had to be (and needed to be) a better way!

A few days later it started dawning on me. For the last year or so, since he had taken over the masters world rankings from Martin Gasselsberger, John Seto was beginning to amass a treasure trove of Masters’ performances. (For some history on rankings for masters, read this on Ken Stone’s masterstrack blog https://masterstrack.blog/rankings/).

Could these not be used? Perhaps they could be!

I started pulling the data for a few track events and a couple of field events from a single year for a collection of age groups. I wanted to see if it was possible, and moreover, if the results made any sense. They seemed to. I consulted with my coaches Paul Osland and Mike Sherar, and they too thought there was something there.

I pressed on. Adding more age groups and looking at 3 years of data for M50 800m. I added a few more events.

All this took me into the late spring of 2016.

I played around with different scoring systems and ideas. At the same time I started gathering more data. As many years as possible. To this point I have organized 4 years of best performance (2013, 2014, 2015, and 2016). See here the mechanics how it works

I won’t further bore you with all the problems and blind alleys I pursued. Just know I explored a lot of them before deciding on the concept you see today.  I’m a slow learner and worker, but here at last, is a “beta” look at the concept.

For a more in-depth understanding see An Introduction

 

 

Beginning to Mine the Motherlode

Well, let’s get this going. “This” being the blog part of the duncanSCORE site.

My hope is to post here weekly, with news, updates, and information on duncanSCORE, and associated material. The duncanSCORE is a new wrinkle for Masters Athletics … something different. And I suspect what I write here may be a little different, too.

There is a wealth of information accumulating about Masters track and field performances, and my goal is to be able to mine it a little, and shed more light on our unique attack on aging than has been been historically shone. I would like a few facts to get in the way of some of the “truths” that are out there.

Though likely sometimes data “heavy”, I will try and cut to the chase as quickly as possible to point out the relevant points for us. I’m very excited to be able to begin analyzing all of this incredible data being accumulated. Thanks to John Seto at mastersrankings.com, it’s a motherlode of data on our sport.

Not only do we enjoy the hard training and competing, but we all love and  appreciate the camaraderie that comes along with Masters Athletics. There’s nothing like it! But I also think that by studying the data, there is an opportunity to learn … to help us compete better, and be healthier.