Feature Photo Courtesy Berlin Marathon / (c) SCC Events, Sebastian Wells
Earlier this month, an article was published in the research journal Scientific Reports which examined differences in pacing patterns between men and women at the Berlin Marathon. The main conclusion was that “male runners [were] approximately twice as likely overall […] to experience catastrophic deceleration.”
I learned of this article when someone tagged me in a Reddit post from r/Science about the research. The person who tagged me wondered if there was something wrong with the source data, because the gender distribution didn’t seem consistent with the current gender distribution at the Berlin Marathon. The source data actually checks out, because the Berlin Marathon used to have far fewer women than it does today. The paper uses results from 1999-2025, so it includes a period when a very small percentage of finishers were women.
But when I read the article, something else jumped out at me. They made the additional claim that among “competitive runners,” who they identified as finishers who completed a marathon in under three hours, men were six times more likely than women to “hit the wall.” This just struck me as odd, so I decided to take a closer look.
I went back to the source, collected the results of the 1999-2025 Berlin Marathons (including splits), and did some analysis of my own. I’ll get to the data below, but first let’s think about what’s wrong with their methodology.
Flaws in the Research Study’s Methodology
Their general conclusion (women tend to pace better than men) checks out with what I’ve seen at other races (including the Chicago Marathon and the Philadelphia Marathon). But the notion that fast men are six times more likely to blow up than fast women just didn’t pass the smell test for me. And I think it boils down to two key flaws in their methodology.
First, they classify runners into performance groups by finish time. The same finish times are used for both groups: < 3:00, 3:00 to 3:30, 3:30 to 4:00, 4:00 to 4:30, and > 4:30. These groups were labeled Competitive, Advanced, Intermediate, Recreational, and Casual respectively.
The problem is that the same threshold – sub-3:00 – was used to define both men and women who were competitive. But that threshold is not the same. Roughly 10% of (young-ish) men finish sub-3:00. The rate for women is closer to 2%. Women who are in shape to run a sub-3:00 marathon are much better athletes than men who finish at a similar time.
Second, the classification is made based on finish time. But the research question is focused on how many of those finishers experienced a catastrophic decline in pacing – defined as a 20% decline between the first half and the second half.
Let’s say you finish a marathon in 3:00. You could run two even splits (1:30 / 1:30), and someone who finishes in three hours is in three hour shape. But what would it look like to experience a 20% decline in the second half and still finish under three hours? You’d have to finish the first half in about 80 minutes followed by a second half in about 96 minutes. A 81 / 97 split would be about as slow as you could get in the first half and still break three hours.
In other words, the only women who would meet this criteria are women who go out at a 2:40-2:42 pace. That’s only a few minutes slower than the Olympic trials qualifying standard (2:37:00). The only women who would fall in to the competitive group are a) fast women who evenly paced a sub-3:00 marathon or b) a very small group of sub-elite women who went out extremely fast and blew up.
In other words, the methodology guaranteed that the number of women classified as “competitive” and experiencing a 20% slow down in the second half is by definition very small. So it’s no wonder that men demonstrated a much higher rate of blowing up.
Revised Methodology to Compare Apples to Apples
To fix this, I’d suggest two key revisions to the methodology.
First, categorize runners based on the time it took them to complete the first half. This is a good indication of what shape the runner thinks they are in and what time they are planning to hit. If two runners both complete the first half in 1:29, they are likely aiming for ~3:00. But if one blows up, their finish time might be 3:15 compared to the other runner who finishes in 2:59.
Second, categorize runners using gender and age appropriate times. For young runners, I used the same categories for men and created corresponding bins for women: < 3:25, 3:25 to 4:00, 4:00 to 4:30, 4:30 to 5:00, and > 5:00. For a more robust, age neutral categorization, I used runners Boston qualifying times and categorized them as more than 10% below BQ, within 10% of BQ, less than 10% greater than BQ, 10% to 20% above BQ, and more than 20% above their BQ.
Otherwise, I used similar methodology to the original study. I collected the results from 1999-2025, including the half marathon split and the net finish time. I calculated the percent slow down as the difference in the two divided by the time to complete the first half (i.e. 95 – 90 = 5; 5 / 90 = 5.5%; 1:30 / 1:35 splits = 5.5% slowdown). I kept the same threshold for “hitting the wall” (a 20% slowdown), but I also included categories for smaller declines and negative splits.
I excluded: a) runners with results < 2:00:00 (data integrity issues), b) runners with results > 6:15:00 (slow enough to walk), c) runners without both half marathon splits and net finish times (necessary for calculations), and d) runners identifying as nonbinary (who make up a very tiny group). This resulted in a total of about 873,000 runners. Unlike the original study, I did not eliminate duplicates from multiple years, so the sample is slightly larger than what they ended up with.
The age and gender distribution of the sample is portrayed by the visual below.
Note that there are far more men in the sample than women. Things have gotten more balanced in recent years (albeit still male-dominant). But early editions of the race only had 10-20% women. In the legend, each age (i.e. 25) represents the bottom end of the age range (i.e. 25-29).
This version of the graph norms the data to make it easier to see the relative age distribution within each gender. Women tend to be younger than men, with a larger share of runners in their 20’s and 30’s and a smaller share of runners in their 50’s and 60’s.
How Do Split Distributions Differ Between Men and Women?
With that critique and methodology out of the way, we can get to the actual results from the data. The first visual shows the overall breakdown for all men and women as a baseline. We’ll later disaggregate this by performance category.
Across the board, women are slightly more likely to negative split (14% vs 12%) and less likely to have a catastrophic slowdown (9.7% vs 17.6%). This is consistent with the “twice as likely” finding, although this could be influenced by confounding factors in the distribution of age and competitive level.
The next visual breaks the data out by the five performance categories. It also limits the results to runners under 45, because these performance categories are less appropriate for older runners.
As a general rule, men are more likely to have a catastrophic slowdown. Among faster runners, it’s slightly more than twice as often. But among slower runners, it’s slightly less than twice as likely (8.1% to 3.4%).
In both cases, there’s a huge difference between slower runners and faster runners. Among men, the slower runners are 2-3x more likely to slow down. Among women, they’re 3-4x more likely.
This final visual includes all runners, but it categorizes them according to how far above or below their Boston qualifying time they were. To account for differences caused by supershoes, finish times before 2020 are categorized using the older Boston qualifying times, while newer times since 2021 are categorized using the current Boston qualifying times.
The pattern is similar. Men in each category are about twice as likely (give or take) to blow up than women in the corresponding category. And across the board, women are slightly more likely to negative split or to run a small positive split.
Has Anything Changed Post-COVID?
Going through this analysis, I was curious if anything shifted after 2020. The last few years have seen a huge boom in running, bringing in younger and less experienced runners. It’s also seen finish times get faster across the board, thanks largely to improvements in shoe tech.
So I took the last two visuals and I added a filter to break the data down into post-COVID results (2021-2025) and pre-COVID results (1999-2019). This first visual includes younger runners under 45, classified by estimated finish time.
Among the faster runners, things look similar in both time periods. But compare the slower men (recreational / casual) between pre and post-COVID. In the earlier period, 22-25% of them blew up. In the later time period, that jumped to 32-33%. Among women, there are more minor changes between earlier and later time priods.
This visual includes all runners, classified by how their half marathon split compares to their Boston qualifying time.
Again, the fastest runners – who are on pace to beat their BQ by more than 10% – don’t see a big difference. And the men-women differential is about 2x. But the slowest men – the ones 20% or slower than their BQ – saw a big difference after COVID (21.6% -> 29.7%).
I do wonder if some of this is weather related. There have only been five races post-COVID, and one of them (2025) featured warm weather. That could have confounding implications.
Final Thoughts and Follow Ups
I would like to follow up on this with some more rigorous statistical analysis. I’d also like to tease out how much difference weather makes – and see a better baseline of data for only races with good to fair weather. But we’ll save that for later in the summer.
This week, I only had the time to do the data collection and knock out a quick analysis. But I think this clearly demonstrates that flaws in the original researchers methodology led them to the erroneous conclusion that the fastest runners had a huge differential between men and women. With my methodological tweaks, the difference is consistently about 2x – give or take a little bit.
This is a good reminder not to believe everything you read in a scholarly journal just because it is published. Based on their methodology, this was a statistically significant finding. But if common sense makes you question the validity of a finding, you should look into the methodology before emphasizing the importance of that finding.
Here’s what they had to say at the end of their article:
Perhaps the most counterintuitive finding of this investigation is the amplification of the sex risk disparity among the highest-performing athletes, revealing that superior physiological conditioning does not inoculate male runners against catastrophic pacing failures. It is often assumed that pacing stability improves linearly with performance level and experience. However, our stratified analysis reveals a paradox: while the absolute incidence of hitting the wall decreases with speed, the relative risk disparity between sexes actually widens. In the Competitive category (< 3:00 h), male runners were 6.06 times more likely to experience catastrophic deceleration than their female counterparts. This contradicts the notion that pacing errors are solely a function of inexperience. Instead, it suggests that high-performance male runners may be prone to adopting high-risk strategies—running closer to their physiological ceiling—potentially shaped by competitive pressures and the complex dynamics of decision-making under physical stress.
I don’t think the data suggests this at all. The sex-risk disparity is similar (~2x) across both competitive levels and other performance levels. So while men may be more prone to adopting high-risk strategies, high performing men are not uniquely risky (nor are high performing women uniquely conservative).
What are your thoughts on the original journal article – or on this alternative analysis of the same data? Do you have any follow up questions or hypotheses that I should test in a future analysis?