#tbt – Your Share of Hell

This one, from another, more temperate February 51 years ago, is especially for all my friends in New England who are training for a spring marathon:

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#tbt – the First Woman To Run a Marathon (wasn’t Kathrine Switzer)

As we all know, the first woman to gain official entry into a marathon (albeit unbeknownst to race organizers) was Kathrine Switzer at Boston in 1967. But she certainly wasn’t the first woman to run 26.2 miles – Roberta Gibb had covered the same course unofficially just the year before. So who was the first woman to run a marathon?

51 years ago, in December, 1963, Merry Lepper became the first US woman to finish a 26.2 mile marathon. Merry, a 20-year-old community college student, entered the Western Hemisphere Marathon in Culver City, Nevada along with her friend, Lyn Carmen. Lyn dropped out, but Merry completed the race in 3:37:07, an unofficial world record.

Merry’s run was the cover story in the Feb. 1964 Long Distance Log:

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At that time, women weren’t allowed as official entrants in sanctioned marathons, so Merry’s time was not included in the official results sent in by race organizers:

However, they did mention the efforts of “Mary” and “Lynn” in a sidebar:

Arlene Pieper, on Pikes Peak before the race

A few years earlier, in 1959, Arlene Pieper had entered and finished the Pikes Peak Marathon. However, that course was almost 2 miles short of the standard 26.2 mile distance. Personally, I think racing up Pikes Peak makes up for that. For that matter, some reports say that the 1963 Culver City course may have been a bit short.

Merry Lepper’s time beat the unofficial women’s marathon record of 3:40:22 set by Violet Piercy of Great Britain on October 3, 1926 on the Polytechnic Marathon course (though Piercy did not run on the same day as the race).

Stamatis Rovithi may have been first woman to run a marathon when she ran the Marathon-to-Athens course a month before the 1896 Olympics. There are reports that say a woman named Melpomene tried to enter the actual Olympic Marathon that year, and when organizers did not let her in, she jumped in at the start anyhow and made it to the finish in about 4:30, an hour and a half after the winner. However, other reports credit that run to Rovithi (sometimes on the day following the Olympic race).

Of course, it wouldn’t surprise me if a woman had run 26 miles or more sometime before then. If Ada Anderson could walk 2,700 quarter miles in 2,700 quarter hours in 1879, 24 miles a day for over 28 days, it’s not unlikely that some woman somewhere ran 26.

Anyhow, through the efforts of these women, along with Grace Butcher, Katherine Heard, and many others, marathons finally began accepting women as official entrants in the early 1970’s. Today, about 43% of the people who run marathons in the US are women, and if recent trends continue, by 2020 there may be just as many women running marathons as men.

#tbt 1977 – Boston Marathon’s First Official Wheelchair Division

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In 1970, Eugene Roberts became the first person to complete the Boston Marathon in a wheelchair as an unofficial, but recognized, entrant. Bob Hall was the first wheelchair racer to get an official finisher’s certificate, in 1975.

Two years later, multiple racers met in Hopkinton to race as a separate division for the first time, and to compete for the National Wheelchair Championship. Also that year, Sharon Rahn became the first woman wheelchair racer to enter, and finish, the race.

Average Finish Times by Age and Gender for 2014 Boston Marathon

(This is the 17th post in a series that started here)

While I was on vacation, a reader asked about the average finish times for each age group at the Boston Marathon, divided by gender. Here’s the data:

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Here’s a chart of the data:

(Quick reminder – this data set does not include wheelchair and handbike racers and is missing a few randomly scattered results due to imperfections in the process of obtaining the data.)

Got any questions? Your answer could be next!

5K splits for Men and Women at the 2013 New York Marathon

(This is the 16th post in a series that started here)

After my post comparing 5K splits for women and men at the 2014 Boston Marathon, one reader asked to see comparable data from other years. Unfortunately, the 2013 data set is incomplete, and the BAA doesn’t post 5K splits from years prior to 2013, so 2014 is all we’ve got.

I was able to get 5K split data from the New York and Chicago Marathons for 2013. Gender information wasn’t readily available in the Chicago results, but I was able to create a chart comparing the results from New York for men and women.

As I did for Boston, I divided the results up into half-hour groups, shifting the women’s groups to adjust for the difference in average finish times between men and women (about 25 minutes):

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The difference between men and women at New York isn’t as distinct as it was in the Boston data, but you can still see that, on average, women seem more likely to be able to pick up the pace at the end of the race than men.

As fun as all this is, you need to keep in mind that these are average results from a wide variety of runners. They have some illustrative value, but they say very little about any individual competitor.

Let’s go back to Boston 2014 for an example to illustrate that point. The average man ran about 3:51, while the average woman ran 4:12-ish. To pick an individual totally at random, I ran a 3:47, which made me slightly faster than the average male. In spite of that, well over 4500 women finished ahead of me.

There’s some optimal pacing strategy, whatever it might be. Most people fail to hit it. On average, men might tend to go out too fast, while the average woman might save a little too much for the end, but individual differences far outweigh any predictive value this might have.

One thing for sure; whether or not you run an optimal race, I know which type of pacing is more fun.

Next post: Boston Marathon finish times by age group and gender.

Marathon 5K Split Distribution by Age

(This is the 15th post in a series that started here)

Here’s another look at the 5K splits for Boston 2014, this time divided up by age:

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And here are the splits for Chicago 2013:

We saw earlier that when we divide up runners by finishing time, the faster runners tend to manage their effort so they have little energy left to accelerate at the end.

When we divide runners by age, it appears that the younger the runner, the more they seem to have left in the tank to expend by speeding up over the last 2K. The older runners don’t pick up the pace as much, if at all.

Even without the data to confirm it, I’m willing to bet that there’s a positive correlation between age and marathon experience. There are always exceptions –  27-year-olds with 15 marathons under their belt, and 60-year-olds running their first marathon – but customarily, the older you are, the more marathons you’ve run.

We generally assume that people learn through experience. Should we assume that experience teaches runners what the fastest runners seem to know, that they do better when they run more aggressively, using up energy earlier instead of saving it for the sprint to the finish?

Or that experience mellows older runners, making them less competitive, less likely to push as hard as possible at the end.

I know a lot of older runners. I’m betting on the former.

We’ll take another look at gender differences in my next post in this series.

Do Women Marathoners Push Themselves As Hard As Men?

(This is the 14th post in a series that started here)

Studies, including a recent one of mine, show that on average, women tend to pace themselves more evenly than men when running marathons.

We know that splits tend to get more positive as runners’ finish times go up and that the average female runner is slower than the average male, so the raw numbers may understate this trend. Using split scores to mitigate the differences in finish times between women and men bears this out.

Since conventional wisdom says runners should strive for even splits in order to run their best marathon, it’s been said that this means that women are better at marathon pacing than men.

It’s also been hypothesized that women don’t push themselves as hard as men, which makes it easier for them to run more even splits, but keeps them from achieving finish times as fast as they might otherwise.

Perhaps a closer look at the data will tell us more.

In two recent posts, I studied 5K splits from the latest Boston, New York, and Chicago marathons. I discovered that as runners’ finish times got faster, their pace, relative to their previous splits, slowed over the last 7K and especially over the last 2K.

I decided to take one of those races (Boston 2014) and break the data down by gender.

This chart shows how the average 5K splits for men and women compare to the overall average. It also compares the splits for the runners with the most even half-marathon splits from each group.

 

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The chart shows that women’s splits at the end of the race are faster, compared to their earlier splits, than men’s. Since the average woman is slower than the average man, we expected that.

By only selecting runners with even splits, the differences between the shape of the lines for women and men were reduced, but the women’s line still trends comparatively faster.

The next chart tries to eliminate the differences due to speed by dividing the average splits for men and women into half-hour groups by finishing time.

In almost every case, even though the runners are grouped by equal finish times, the women’s splits at the end of the race are faster, relative to their earlier splits, than the men’s. That is, the women’s splits take on the shape of slower runners, though the finish times for each group of men and the women are the same.

Since men are faster than women on average, by comparing men and women using equal finish times, the women in each group are faster, relative to all other women, then the men are to other men. That means the direct comparison might tend to understate the difference in split trends.

This chart shows that the distribution of finish times for men and women are roughly the same, except that the men’s times run about 20 minutes faster.

So in this chart, I divided the men up into half-hour groups that are 20 minutes faster that the women’s groups.

As predicted, this chart makes the difference in split trends between men and women more distinct.

What’s it all mean?

In my original 5K split post, I guessed that slower runners weren’t pushing themselves quite as close to their potential as the faster runners, leaving them with more energy left after the Newton hills to speed up and look good for their finish line photos.

Since women’s splits take on the shape of slower runners when compared to men’s, even when the data is manipulated to minimize the effect of differences in speed, does this mean that women aren’t pushing themselves as hard as men?

Maybe, maybe not.

If men are pushing themselves harder, does that mean that they’re getting better results than comparable women?

Maybe, maybe not.

If you disagree, what’s your alternative explanation? Let me know what you think and maybe we can figure out if the data supports it.

We’ll divide up 5K splits by age in my next post.

Another Precinct Heard From: 5K Split Distribution at the 2013 Chicago Marathon

(This is the 13th post in a series that started here)

After my last post in this series, I obtained the 5K splits for the 2013 Chicago Marathon. Chicago is a notoriously flat course, as shown in this Boston.com graphic:

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I wanted to see if the trends in runners’ paces over the last 2K on the flatter course were consistent with what we saw at Boston and New York.

Here’s the 5K pace chart for Chicago:

Once again, the faster runners were relatively slow over the last 2K, while slower runners picked up their pace.

The new chart reveals another pattern: as the courses get easier, more groups run faster over the final 2K.  At Boston, the first group to pick up the pace at the end were the 4:00-4:29 runners. At Chicago, the 3:00-3:29 group managed the feat. And at New York, which most people think falls somewhere in between Boston and Chicago in difficulty, the 3:30-3:59 group was the first to speed up at the end.

I look at the difference between 5K splits for men and women in my next post.

5K Split Distribution at the 2014 Boston Marathon

(This is the 12th post in a series that started here)

When I looked at the 5K splits from the 2014 Boston Marathon, I mostly saw what I expected to see.

I calculated the average 5K splits for the 2014 race both as a whole and broken down into specific groups of runners. This chart shows those 5K splits converted to a minute-per-mile pace:

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Here’s the elevation map for the Boston course:

The Newton hills start at about 28K and end near the 33K mark. They don’t line up perfectly with the 5K splits so their effect is muted some, but you can still see from the 30K and 35K splits how the hills slow everyone down. Splitting up the data by finish time shows that the hills hit the slower runners harder.

Then comes something I found interesting. Once you get through the hills, the next 7K through Brookline is pretty easy, trending mostly flat or downhill. Overall, the average runner was able to take advantage of the terrain and pick up the pace. Perhaps unsurprisingly, the “Even splits” group did a particularly good job of making up time lost in the hills.

A closer look shows that the overall effect was created by the extreme groups – the very fastest runners in the race and the slowest. The middle of the pack averaged even slower after the hills than they did through Newton, though the course is much easier. Even the runners finishing between 2:30 and 3:00, a pretty fast group, lost ground.

The last 2K was even more surprising to me.

That part of the course has its challenges (the bump over the Mass Pike, the Mass Ave. underpass, and the climb up Hereford St.) but they’re nothing compared to what came earlier in the race. Countering that, almost everyone who runs a marathon gets a burst of energy when their goal finally comes within reach. And at Boston, the thickest packs of spectators start just past the 40K mark, in Kenmore Square. The roar of the crowd is relentless from Kenmore on in.

I expected to see splits dropping across the board. But the trend was clear. As runners’ finish times got faster, their times over the last 2K got slower (not in an absolute sense, but relative to their previous splits). 2:30 runners still finished faster than 5:30 runners, but the 2:30 runners slowed down while the 5:30 runners sped up. The fastest runners were dramatically (for them) slower over the last 2K.

Even the “Even splits” group lost ground after 40K, while the “Most positive” group made up more than anyone.

Maybe there’s an element of “oh well, I’m not winning” in the sub-2:20 group, but that doesn’t apply to the 3:30 marathoners, and they still slowed down.

It’s not just Boston. The 5K splits for New York show the same pattern:

though the elevation profile is different:

To run your fastest race, you want to “leave it all out on the course”. If you have the energy to pick up the pace as you approach the finish, you probably could have run faster earlier on and ended up with a better finish time.

But there’s a fine line between running your best race and going out too fast. As we know, most runners run positive splits. Marathoning is hard.

The 5K charts show that in general, slower runners drop off from their initial pace sooner than faster runners.

For the slowest runners, part of the drop may be because, on average, they aren’t pushing themselves quite as close to their potential as the faster runners. That does leave the slower runners with more energy left after the Newton hills to speed up and look good for their finish line photos.

Mid-packers don’t give in as easily, holding on to their initial pace farther into the race. Actually, mid-packers might be better served by taking it a little easier from the start; on average, they’re not able to take advantage of the easier terrain after the hills.

Even the fastest runners have difficulty getting the first 40K just right, so they’re tired, but not too tired, for the last 2K. Apparently the best runners believe that if you’re going to make a mistake in pacing, it’s best to err slightly on the optimistic side and risk running out of gas a little early, just as long as you don’t get too sanguine in the early miles.

It sounds funny to say that the fastest runners “went out too fast” over the first 40K, but I think that’s what’s happening.

Notes:

• There is a wide variance within any of the groups of runners. While the average runner in a group may have a certain split pattern, many individuals within the group will have entirely different splits.
• I used split scores to determine who belonged in the “Even splits” and “Most Positive” groups. The first and second half splits for the 310 runners in the Even splits group were within ±.2% of each other. The Most Positive group was made up of about 300 runners whose second half was at least 20% slower than their first.
• I looked into creating a “Most Negative” group. There weren’t any runners with 20% negative splits, and only about 100 with splits that were more than 2% negative. The average 5K splits for those runners were badly skewed by a few runners with individual splits way out of line with the rest of their race, perhaps because of portapotty breaks or other temporary issues, so I decided not to chart them.

More on this topic in my next post.

 

Plotting Split Scores

(This is the 11th post in a series that started here)

Last time, we defined a “split score” as a runner’s raw split divided by their finish time.

Split scores work great for individual runners, easily showing us that a 5-hour marathoner’s 10 minute split is smaller, relatively, than the same raw split would be for a 3-hour runner.

That’s all well and good, but split scores will only matter to anyone if we can use them to gain new insights about runners – in general, or when we group them by time, age, gender, or in other ways.

The core of any potential usefulness for split scores is their ability to make comparisons between runners of different abilities more meaningful – to say, “All else being equal, here’s how the 5 hour marathoners compare to the 3 hour marathoners.”

The math is easy, and the logic behind it makes superficial sense. What I’m not sure of is what, if anything we gain by doing this. Because the main difference between 5 hour and 3 hour marathoners IS their finish time.

Whatever. Get to the point, Charbonneau. Show us some split scores. Maybe we’ll see something.

Here’s finish time vs. split score for Boston 2014:

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And to refresh your memory, here’s finish time vs. raw splits:

You can’t compare the two directly, but you can see how the general shape of the split score scattergram hews closer to the horizontal axis.

If you plot raw splits vs. split score, you can see that the relationship is non-linear:

As it should be, since another way of looking at split scores is that if you make a right triangle by plotting splits versus finish times, the split score is the tangent of the angle between the hypotenuse of that triangle and the side represented by the finish time.

In spite of that, on the next chart, I plotted the moving averages and 4^th order polynomials for the two data sets, scaling the raw split data so the linear regression for the two sets of data overlaid each other as much as possible. That choice is entirely arbitrary, and is probably more misleading than it is revealing since the relationship between the two data sets isn’t linear, but it does show something about how the two sets of curves change relative to one another:

Let’s try comparing men vs. women. Here’s raw splits vs finish times, divided by gender:

And here’s the same chart, with split scores replacing raw splits on the Y axis:

The use of split scores seems to accentuate the spread of values for any specific finishing time, which also increases the difference between men’s splits and women’s splits. Perhaps because split scores remove another source of autocorrelation error by factoring out finish time?

This chart tries to summarize the differences:

Men have faster average finish times, but larger average splits, whether you measure raw splits or split scores. The difference is slightly greater for split scores. Note that the average split divided by the average finish time is not the same as the average split score.

There’s certainly a difference between raw splits and split scores. Does that difference help us find any better answers for our questions? What do you think? I still don’t know, but I did find one use for split scores in my next post.