The Chicago Marathon, 2017

An Analysis of Participation & Performance at the 40th Bank of America Chicago Marathon, 2017.

TLDR;

  • We examine the race records of more than 44,000 runners from the 2017 Chicago Marathon.
  • Who were the participants from this year’s race? Where were they from? How old were they? Was ot their first marathon or had they run before?
  • We also examine how gender, age, experience, and ability influence performance (finish-times, pacing, hitting the wall) at this year’s 40th anniversary event.

On Your Marks, Get Set, …

Sunday, October 8th, 2017, began with a crisp chill in the air, but the weather quickly warmed up as runners of the 40th Chicago Marathon made their way through the 42.2km (26.2 mile) course. This year Galen Rupp topped the men’s field, with a time of 2:09:58, and the first American to win the event since 2002. Meanwhile, Ethiopia’s Tirunesh Dibaba secured the women’s title in 2:18:31.

How did the other 44,000+ runners fair? What follows is an initial analysis of the data generated by Chicago’s intrepid runners as they ran, jogged, and walked their way through crowd-lined streets. The race data available includes 5km split-times, as well as gender and age information. In addition, separate data that we have collected over the years (>2.5m race-records from more than 100 marathons) makes it feasible to match runners across multiple races to identify whether a particular runner is a new marathoner (first-timer) or a regular racer (repeater). This means that we can consider how gender, age, ability, and experience come to influence how race day unfolded for Chicago’s runners this year.

It’s the Taking Part…

This year more than 44,000 runners crossed the line, with just over 51% male and 48% female. Chicago is one of the largest events of its kind in the world and one of the most balanced in terms of its male/female participation.

These runners lined up to represent 131 different countries around the world with Mexico, China, and Canada providing just about 1,000 or more particpants each to the field on the day; the chart above shows the number of men and women for the top-20 countries (by participation levels) excluding the United States.

In the graph above we see the age distribution for men and women. Notice how there is a greater proportion of young females (20–35 years old) than similarly aged males. For example, approximately 37% of females are aged 35 or younger, compared to just 23% of males.

In the 30–35 years-old range there is a noticeable decline in the percentage of female runners for a period of five years or so, and beyond the 40 years-old mark the percentage of males consistently exceeds that of females. This decline in female runners in their early 30s is not unique to Chicago. It may be due to lifestyle factors that hinder female runners from participation during their 30’s, for example. Fortunately it appears to be a short-lived break, at least for many.

First-Timers vs. Old-Timers

We can also divide runners up in to those who appear to be completing their first marathon (first-timers) and those that have completed multiple marathons (repeaters). Strictly speaking, our ability to identify first-timers and repeaters is limited to the runners in our larger dataset of >2.5m marathon race-records. If we have no previous record for a runner then they are considered to be a first-timer. Otherwise, they are a repeater. These classifications are not perfect, because even this larger data-set is obviously incomplete and many runners will have raced in marathons that are absent. In other words. we will tend to over-estimate the number of first-timers and under-estimate repeaters, but hopefully not by too much, and in any event it should be good enough for the trend analysis that follows.

In Chicago this year, a majority of young runners (30 years old) were first-time marathoners. As might be expected this percentage decreases steadily with increasing age but it is encouraging to still see plenty of apparent first-timers starting later in life. Females are more likely to be first-timers than males across all ages.

Try, Try, Try Again

For repeaters, the number of marathons completed increases steadily with age: a typical 30 year-old in Chicago this year was running their 3rd marathon; a 50 year-old male runner was probably on their 5th; while a 50 year-old female was running their 4th marathon, on average. Once again, there is a difference between the repeat rates of men and women. Men run more marathons than women for all ages, and we can also see how the rate of increase of repeats for women tends to slow in their mid-30s, before recovering again in their late-40s.

Finishing Strong

Now that we have a sense of who the runners are, and their experience levels, let’s take a look at their finish-times and, later, other aspects of their performance. The chart below shows the number of male (blue, above the line) and female (red, below the line) runners who finished at various finish-times; the height of each bar corresponds to the number of runners cross the line at that time. We can see very small numbers crossing the line for the fastest finish-times (shortly after 120 minutes) followed by a sharp increase in finishers from about 180 and 210 minutes, for men and women respectively.

The most popular finish-time for men is just before the 240 minute mark; in fact there is a series of spikes immediately before this landmark time. This time is also popular for women but their most popular times occur around the 300-minute mark. The ‘spikey’ nature of this chart reflects how runners, in Chicago and elsewhere, tend to target various discrete landmark goal-times, such as 180, 210, 240, 300 minutes, and also intermediate times such as 195, 225, 255 minutes etc. The finisher-rate through these times is irregular, favoring the minutes directly before a landmark time, when more runners are seen to finish, compared to the minutes directly after, when fewer runners finish.

The Wisdom of Age

As we might expect age has a significant influence on average finish-times. As the chart below shows, when we get older we also get slower, but only after our mid- to late-30s. For example, 20 year-old men have an average finish-time of about 280 minutes. By the mid-30s this comes down to about 260 minutes, but as these runners leave their 40s their times gradually slow into the 260 minutes again, and continue to slow from there.

Experience helps. When we compare the average finish-times of first-timers versus repeat runners we see the same pattern with age, but with first-timers significantly slower than repeaters across all ages. For example, female first-timers in their mid-30s have an average finish-time of more than 300 minutes, compared with less than 290 minutes for females of the same age who are not first-timers. The same holds for men and this experience dividend persists across all ages.

Good-for-Age

It‘s not all bad news for older runners. Clearly age and experience are connected, older runners are less likely to be first-timers and have tended to run more marathons. To better understand the relationship between age, experience and finish-time we can consider an age-independent measure of finish-time, which allows us to compare the finish-times of runners across different age ranges. One way to do this is to use the famous Boston Qualifier (BQ) times. These are gender-based and age-based finish-times which runners need to satisfy in order to be eligible to qualify for the Boston Marathon; whether or not a runner actually qualifies depends on a further selection process base on application numbers. For example, at the time of writing (October 2017) male runners in the 35–39 years old age bracket need to finish no later than 3 hours and 10 minutes (190 minutes) to qualify. For similarly aged women the qualification time is 220 minutes.

The point is that using these BQ thresholds we can evaluate how runners in different age-groups are performing, relatively to one another, by calculating the percentage of runners achieving their BQ time within these groups. In the first two bar charts above we plot these BQ rates for men and women, separating first-timers from repeaters, to get a sense of how experience helps. Not surprisingly experience helps a lot: for men and women, the BQ rate for those with repeat marathons is always better than the BQ rates for first-timers, across all ages. But getting older also tends to help because, as we can see in the trend-lines in the 3rd (bottom) graph, as age increase so too does the average BQ rate, for men and women, first-timers and repeaters.

A Race of Two Halves

It’s time to move from finish-times, and the end of the marathon, to pacing during the marathon. Since we have access to 5km split-times, it is natural to look at the average pace for men and women across each of the 5km segments. To make it easier to compare runners with different finish-times, it is more natural to look at relative paces for these segments; each relative pace (RP) denotes the degree to which the runner is runner faster or slower than their average race-pace. For example, a relative pace of 0.9 for the first 5km means that the runner has run this segment 10% faster than their average pace (for the race as a whole).

In general, male and female runners adopt broadly similar pacing strategies. The first 5k segment tends to be the fastest (RP = 0.9) as, on average, runners start off quickly, and then their pace gradually slows until the short (2.2km) final segment, when runners manage to speed-up somewhat. While men and women both start out fast, women tend to moderate their early pacing more efficiently than men. As a result they slow down less than men during the latter stages of the race, and they speed-up more for the final segment. For men and women the 35–40k segment tends to the the slowest of the race and, along with the fast first segment, this indicates a relative pacing range for men from 0.9 to 1.2 and for women from 0.9 to 1.14. While this pacing profile is not unusual, certainly for relative flat courses, the pacing range in Chicago is large. For example, at the Berlin Marathon, a not dissimilar course to Chicago, runners start only about 5% faster on average and slow by only 10% for an average pacing range of 15% in total.

When we look at the pacing of those runners who achieve a BQ time (by definition, those runners who are running races that are considered very good for their age) we see a much more even pacing profile, and one that is almost identical for men and women. BQ runners still tend to start fast, but only about 3% faster than their mean race pace, and they still finish more slowly, but not as slow as regular runners; overall the pacing range for BQ runners is only about 10% compared to 25–30% for typical runners.

Doing the Splits

For most runners then, the Chicago marathon is a race of two halves, a speedy first-half, followed by a much slower second, plus a short final sprint to the finish. The relative difference between the first and second half is commonly used as an indicator of pacing discipline. Thus, a relative split of 0.1 (a positive split) means that the first half is 10% faster than the second while a relative split of -0.1 (a negative split) means that the first half is 10% slower than the second.

As the graph below shows, very few of Chicago’s runners, approximately 5%, managed a negative split (relative split < 0) and with little difference between men and women. However, a significant difference exists when it comes to positive splits (relative split > 0). Men tend to run fewer small positive splits (relative split < 0.2) and more large positive splits (relative splits > 0.2), compare to women.

For example, just under 2% of women run a 10% positive split (relative split = 0.1) compared to less than 1.5% of men. In contrast, for 40% positive splits (relative split = 0.4), signalling a very significany slow-down in the second half of the race, there are 3-times as many men (0.4%) than women (0.15%).

About the Wall

Large positive splits are likely to correspond to runners hitting the dreaded wall. For the purpose of this analysis we determine that a runner hits the wall if their relative split is greater than 0.33. In other words, they must slow by at least 33% in the second half of the race; that’s on average during the second half, and they may slow by a lot more during some significant portion of the second half, later in the race, for example.

In the graph below we compare how often men and women hit the wall, based on this 33% threshold, and by finish-time. We see a very significant difference between men and women in Chicago this year. Men are significantly more likely to hit the wall than women across all finish-times. Generally speaking, the likelihood of hitting the wall increases with finish-time and peaks, for men at around 340 minutes. A full 30% of men finishing around 340 minutes hit the wall, compared to less than 10% of women.

Once again there is some good news for runners as they age, especially for men. The rates at which we hit the wall gradually fall as we age (see graph below). In part this is due to marathon experience but a similar effect is seen, but not shown, for first-timers and repeat marathoners, so the decline is at least in part due to age alone. The effect is more pronounced for men than for women, but then men hit the wall much more frequently than women, and thus there is a far greater capacity for improvement for men; in fact the rate of hitting the wall tends to increase somewhat for women from their 50's. Regardless, women continue to enjoy far few incidents of hitting the wall than men, for all ages.

Conclusions

The Chicago marathon is one of the largest and oldest big-city marathons in the world. It is also one of the most diverse, attracting runners of all ages, from across the world, fast and slow, novices and veterans, and with near-equal participation from men and women. This year’s 40th anniversary event was no different with more than 40,000 runners from more than 130 countries. Hopefully this analysis helps to shed to some light on how the event unfolded for these participants this year.

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