The Aging Athlete (7) – Women’s 1500m

Photo courtesy Doug Shaggy Smith

In the previous post we reviewed the data on the Men’s 1500m. Let’s do the same with Women. (For those who are new to this blog and site, and/or would like a “refresher/reminder” click here to see where the data comes from and how it is calculated.)

Like the Men’s 1500m (with Bernard Legat holding the M35 and M40 WRs), the Women’s early (W35-W45) world records are held by former Olympic/IAAF World Championships medalists (Maricica Puica from Romania W35 [1985] and Yekatarina Podkopayeva from the former Soviet Union, W40 and W45, [from 1994 and 1998.] These records are very much out of line with the age-groups that follow.

This  chart tracks the Women’s 1500m WR (in green) against the average best time for 1500m runners (in blue) as they age. Times have been converted to seconds.

(Blow the chart up to full size by clicking here Womens1500m  As you can see, the WR is fairly flat from W35-W45, but takes a significant uptick at W50. Now look at the pink bars toward the bottom of the chart. They track the percentage slower the average 1500m Woman is than the WR. The W35-W45 age-groups have an astounding 43%-45% difference. But at W50, the difference seems to come back to reality … a 34% difference, which continues to W55, and then further grows through the advancing age groups.

How do the various percentile* groups compare over time with the World Record? Like we did with the Men’s 1500m, we have broken out the 90th percentile (ie the fastest 10% of the world’s 1500m runners in each age-group) and tracked how they fare against the World record. After we get by the W35-W45 ages, the top 10% of runners’ performances  seem to decline at similar rates to the WR, since their racing bests tend to be a fairly consistent 10% – 12% slower than the WR (until W75 where it dips). We’ve done the same with the 75th percentile (the top 25% of 1500m Women racers), and this group, too, generally hold their “gap” with the WR until about W70.

So let’s summarize how those who run the 1500m age. The table below tells you, on average, what you can expect the performance decline will be in your 1500m per year.

Women's 1500m Trends and Average Annual Decline in Performance

Age-GroupWorld RecordAvg WR Decline in Performance/YearAvg 90th PC Decline in Performance/YearAvg 75th PC Decline in Performance/YearAvg Women's 1500 (50th PC) Decline in Performance/Year












































Let’s discount what the WR declines look like. For the 90th, 75th, and 50th (the overall Women’s average), the annual performance declines are very similar ( 1/2% – 3/4% per year) until W50. At that point, the data says,the faster you are, the less (percentage-wise) you will decline. Right through all the age-groups from then on, the 90th percentile is a little less than the 75th, which is a bit less than the 50th.

The bottom line? As simplistic as it sounds, and hopefully without sounding condescending, get as fast as you can as early as you can … because you know what? You will likely keep it longer.

What percentile is your 1500m? To find out just click below, select your age-group from the drop down menu, select event (e.g. 1500m), and then enter your time in minutes, seconds, and hundredths of seconds. Then click the green “Ok … Done … Go” and see your standing.

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  • * It is important to realize that when I refer to “90th and 75th percentiles” these are statistical reference points and do not necessarily refer to actual performances or averages of actual performances.

The Aging Athlete (6) – Men’s 1500m

Photo Courtesy Doug Shaggy Smith

Based upon the previous post, we have learned that 1500m Male runners’ performance after age 35, on average, tends to decline about 1/2% per year until M50. Then the erosion in time increases to about 1% annually until age 60. From there it jumps to over 1% a year.

Comparing the “average” male’s declining times versus the declines seen in the World Record (which form the basis of the age-grading curve) is a bit of a two-step process. Bernard Legat holds the the WRs for M35 (3:32.51 at age 36) and M40 (3:41.87 at age 40), followed by the UK’s Anthony Whiteman (M45), and David Heath (M50) and Australia’s Keith Bateman (M55) and the US’s Nolan Shaheed (M60).

It’s interesting to track the “gap” between Mr. Average and the WR. All through the M30s, M40s, and M50s, the “gap” (as measured by the percent the average is slower than the WR) is a pretty consistent 33%. From M60 on the average man slows down at an increasing rate vs the Legats of the world so that by age 85 we are 63% slower. You can see this in the chart below.

Click    Mens1500m                          to blow it up to full size and look at the pinkish bars on the bottom of the chart. They track in percentage terms how much slower the average is than the WR.

But let’s dig a bit deeper. Let’s look at the 90th percentile*. These are the top 10% of 1500m runners in the world in their age group. The 90th percentile for M55 1500 is about 4:37.65. How well does the 90th percentile of 1500m runners (essentially those who could possibly medal at an Outdoor World Championship … see here how I arrived at that conclusion) fare? Well blow up that chart again.    Mens1500m

Below the pink bars tracking Mr. Average’s greater times than the WR you will see a line called “90th PC Diff (%)” … the percentage slower than the WR of the 90th percentile runner across the age-groups. Note how consistent it is right up to M85 … 11%-14% slower right through time. The top 10% runners really are different! Right through until their mid 80s they hold their relative performance versus the absolute best in the world.

So if the 90th percentile pretty well tracks the WR decline in performance, where does the “royal jelly” in endurance begin to slip? On the chart, just below the 90th percentile you can find the 75th percentile, which is about 5:25.59 for M60. The 75th percentile is about what it takes to qualify for a World Championship final in the 1500. For the 75th percentile, the difference vs the world record also tracks pretty darn consistently until M60. From there the percentage behind the WR increases by 2 points or so every age group. The royal jelly is seeping out. It is somewhere around here at M60 that tracking the WR begins to no longer truly reflect “everyman’s” changing performance in an endurance event through time.

Where does that leave us? I suggest you find out exactly where YOU are. What percentile is your 1500m? To find out just just click below, select your age-group from the drop down menu, select event (e.g. 1500m), and then enter your time in minutes, seconds, and hundredths of seconds. Then click the green “Ok … Done … Go” and see your standing.

Track DE

Once you know your percentile, peruse the following table. It gives the average ANNUAL decline in performance over time for the 90th percentile, the 75th, and the 50th (the average man). Your standing is probably close to one of those, so you should be able to roughly establish what kind of decline in your 1500m time you can expect over the next few years. (One proviso. I suspect that you will decline less in the first year or two of an age group, and then probably a greater percentage loss as you get to the latter years of the age-group.) After age 60 you probably should not be upset if your age grade no longer is holding with previous years. You likely are maintaining your standing among your non-elite peers.

Men's 1500m Trends and Average Annual Decline in Performance

Age-GroupWorld RecordAvg WR Decline in Performance/YearAvg 90th PC Decline in Performance/YearAvg 75th PC Decline in Performance/YearAvg Male 1500 (50th PC) Decline in Performance/Year

Good luck!

Next up more information on Women’s 1500m.

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  • * It is important to realize that when I refer to “90th and 75th percentiles” these are statistical reference points and do not necessarily refer to actual performances or averages of actual performances.

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 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

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.

And you can, of course, easily see how you rate versus your age-group peers by checking out your duncanSCORE. Simply click here , enter your age-group and time, and you will see how you score against all in your age-group, not just against the elite of the elite.


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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 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 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
W8016.81excl (not enough data)2.37%
W8519.83excl (not enough data)3.59%



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

W40 11.09
W45 11.34
W50 11.67
W55 12.80
W 60 13.63
W 65 13.91
W 70 14.73
W 75 15.03

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!

And you can, of course, easily see how you rate versus your age-group peers by checking out your duncanSCORE. Simply click on “Track DE”, enter your age-group and time, and you will see how you score against all in your age-group, not just against the elite of the elite.


Track DE

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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.

And don’t forget if you’d like automatic notification for new blog posts, just click on the follow button  at the bottom right of your screen OR if you are on Facebook become a duncanSCORE friend here

Karla Kontinued

Karla W65 400 Final Malaga Rob JeromePhoto courtesy of Rob Jerome

Now, I think, is an appropriate time to update what’s been happening at the World Champs in Malaga for Canada’s W65 Sprinter Karla Del Grande. I wrote a few weeks ago about her newly established record times, and compared the results duncanSCORE vs Age Grading. You can refresh your memory here.

Karla set a hot Malaga on fire. If you check the results, they list her gold medal time of 14.04 in the 100m, and her 28.83 gold medal winning time as world records. These performances are actually slightly slower to times posted earlier this season, and the applications for these times to be world records are pending. Karla ran a 100m in 13.91 at the NACAC (North/Central America/Caribbean) meet and a 200m in 28.53 at the Canadian Masters Championships. In winning the gold in the 400m in Malaga, she just missed equally the existing WR. Karla ran 68.22.

For the record, this Outdoor season Karla set 2 W65 World Records (pending) in the 100m (13.91) and 200m (28.53), and just missed (0.01 seconds,1:06.22) equaling the World Record in the 400m.

Also for the record, those times have Age Grades of 96.84% (100m), 98.13% (200m), and 96.89% (400m). AG rates her 200m as the best, and 100m and 400 near equal.

The duncanSCORE evaluates these performances slightly differently. 967/97 percentile (100m), identical 967/97 percentile for the 200m, and 952/95 percentile for the 400m. That’s an equal performance in the 100m and 200m and and a 400m about 2% less.

As well, these new W65 world records are rated by AG as inferior to her W60 world records from 5 years ago. Karla’s duncanSCORES on the other hand rate them significantly superior.

I’m biased I admit. But I think the dSCORES are a better evaluation.

And don’t forget, you can get updates on the blog posts automatically by just clicking on the “follow” button on the bottom of your screen.

The Karla Konundrum

Karla in the 200m at Porto Alegre WC (2013) – Photo courtesy Doug “Shaggy” Smith

This post talks to how “numbers” can play tricks on you. Sometimes you need to be careful. Age Grade percentages versus duncanSCORE percentiles can sometimes be eerily similar despite the fact they are comparing to different “realities” (in the case of AG against the theoretical best possible performance, dSCORE against your peers across the globe). Let’s do a deep dive on these numbers.

Recently, Canada’s superstar women’s sprinter, Karla Del Grande set 2  new (pending) World records (100m and 200m) for W65 at the Canadian outdoor championships. She currently holds those Outdoor World records for W60. She also came very close to a new WR in the 400 (3/100 shy). This was her first National championship in a new age-group (W65).

The Hytek scoring system spits out these Age Grades (latest 5 year Age Grades based on 2014-2015) for the 100m as 96.49%, for the 200 as 99.16%, and the 400m as 96.96%. All exceptionally high AGs, as you would expect. But note, the 100m is her “poorest” AG, just oh so slightly inferior to her 400m AG (despite the 100 being a WR and 400 not!) The 200m AG is over 2 1/2 pts better than the 100m. And here are her AGs for her W60 100m and 200m world records … 100m – 98.83 and 200m 100.64. So AG says her most recent performances are not as good as 5 years ago. (But in AG’s defence, there is certainly more to come as Karla builds and heads to Malaga for the 2018 WC).

Maybe you’re asking what are her duncanSCORES?  I’m glad you’re asking.

Her 13.96 W65 100m converts to a 966 duncanSCORE (97 percentile). The 28.53 200 converts to an almost identical 967 SCORE (97 percentile). The 400m also rates incredibly high – 952 SCORE, 95 percentile.

The duncanSCORES rate her 100 and 200 performances as identical. The 400, 1 1/2% inferior. AG says the 100m was the poorest performance, the 200 the best. And the 400 2.37% worse than the 200. Her W60 world records in the 100 and 200 also rate near identical scores (954/95 percentile and 953/95 percentile).

So the 2 grading systems yield differing results. AG says the 200 is Karla’s best event, and as of right now, her 2 new WRs are much less than her W60 records. dSCORES indicate that her 100 and 200 are equally her best, and that versus her peers, in 5 years she has improved significantly.

You choose!

But definitely run the duncanSCORE (duncanSCORE) and enter some of your past performances. See what it says about how you are doing (versus your peers in the same age group) then, ,versus now. How are you faring?

Postscript: The week after her new 100m and 200m WR, Karla ran in an invitational Masters 100m  at the NACAC (North and Central American, Caribbean) Championships. She ran 13.91! Primed for Malaga

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

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