New York Marathon Analytics: What We Learned From The 2016 Results

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The New York City Marathon is the biggest amateur race of its kind and an attractive for pro athletes as well. After first held in 1970 with 127 runners, nowadays over 50,000 athletes from around the globe finish this extraordinary event. The large amount of finishers also makes this race great for some data analytics.

After first doing this kind of analysis with the results of the Chicago Marathon 2016 I continue this type of work with the biggest marathon there is. For that purpose I extracted the entire results for all finishers of this years New York City Marathon. I saved the data in a structured matter into a database that makes it easy to query and calculate some statistics out of it.

General Insights

Some of the general insights I’ve collected are shown in the infographic below:

New York Marathon 2016: General Insights Infographic

New York Marathon 2016: General Insights Infographic

Runner Demographics

As seen in many other races the amount of women is increasing year over year. In the New York Marathon women made up for less than 20% of the finishers until the early 90s. This years edition featured the highest amount of women finishers in the event with almost 42%. However it’s still lower than the 46% in this years Chicago Marathon.

The vast majority of the runners is between 25 and 64 years old. Only 3.5% is under 25 and not even two out of hundred is 65 or older. Despite the fact that a lot of fast marathoners are older than 30, I’m surprised that not more young amateur athletes take the challenge of running the 26.2 mile journey in this beautiful city. Also in the New York Marathon female runners are a few years younger in average with 39 years compared to 42.3 years of male runners.

Country Performance

It’s not very surprising that Ethiopia and Kenya had the fastest runners this year. Both countries are known for their prime endurance running athletes, winning races all over the world. More surprising to me is the great average performance by the 57 Slovenian athletes. They ended up just behind the two African countries in third place after being even one rank higher in the Chicago Marathon this year. I honestly don’t know why Slovenia has such fast runners. Maybe it’s just a coincidence that they managed to deliver great team efforts in the Chicago and New York Marathons in 2016. It looks like running has become a big deal in Slovenia in recent years. According to a comment on Reddit, 1% of the population runs a 10k or more. Also most runners seem to be men which has an impact on the average finish time.

The fastest country from Asia is Israel in 28th place if we don’t count Hong Kong and Macau that are actually a part of China. In 22nd place out of the 89 countries that had at least five runners was the first South American country, Columbia. US runners averaged a finish time of 4:43:49 and ranked in the last third of the country list. Bottom-placed were the 26 athletes from Thailand with an average time of 5:38:29.


The fact that the amount of US runners is much lower compared to the Chicago Marathon doesn’t surprise me much. The New York Marathon is not only the largest of its kind but also the one that everybody knows and lots of runners from around the world dream of finish it once in their running life.

I was already surprised when looking at the Chicago Marathon results that only a small percentage of runners run the second half faster than the first 13.1 miles. This was even more the case in the New York race: Only about one of 20 runners was able to speed up in the second half. As a marathoner myself I know how tough the second half can be but I also know that when I start out on an easy pace the second half is much less suffering compared to heading out too fast. I’ve never run the New York Marathon but the second half seems to be much tougher than the first with several bridges that are basically just hills.
What do you think?

In general the New York Marathon seems to be slower than the event in Chicago. Also only 26% ran the marathon in less than four hours compared to almost 31% in Chicago.

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Age Group Data

I again had a closer look into the age groups of both men and women. The distribution of men athletes on the different age groups shows a smooth Gaussian like curve that peaks in the 40-44 group. The age group distribution curve for women is quite a bit different and shows lots or runners in the age groups between 25-44. The oldest runner this year was Bertha McGruder from New York. She finished the race in 7:30:15 and is 88 years of age! Respect for that Bertha!

Often the age group 35-39 is the fastest in marathons. In this years New York Marathon the fast athletes were a bit younger. The fastest age group for men was 25-29 and for women 20-24. In the men’s race the average finish time in the three age groups for runners aged 20 to 39 is within only 2.5 seconds. In contrast to the Chicago Marathon in all age groups men were faster than women. But also in the New York Marathon fast women tend to be younger compared to the men finish time.

New York Marathon 2016: Men Age Groups Overview

New York Marathon 2016: Men Age Groups Overview

New York Marathon 2016: Women Age Groups Overview

New York Marathon 2016: Women Age Groups Overview

Pace Development

A slowdown in the last section of marathons is quite normal. As stated before only 5.6% of the runners were faster in the second half. But the difference between the pace in the first sections compared to the last miles when it’s getting tough isn’t as big as in the Chicago Marathon. Also the winner Ghirmay Ghebreslassie was a few seconds slower in the last two 5k segments. I guess this is mostly due to the small elevation changes towards the end of the race. Another explanation might be that he is still very young with his 21 years and not as experienced as most other pro athletes. He is the youngest winner of the New York Marathon ever.

New York Marathon 2016: Pace Development over Race Distance

New York Marathon 2016: Pace Development over Race Distance


Performance By States

For all runners from the United States I’ve calculated the average finish time for each state with more than 100 runners. The fastest state was Wisconsin with 4:17:25 the slowest and only one with an average finish time of more than five hours were the 921 runners from Florida. I’m not sure what we can read out of this data but it seems that states from the south of the country tend to be a bit slower. Maybe this is due to the different climate when training compared to New York in early November.

New York Marathon 2016: US States Performance

New York Marathon 2016: US States Performance

So that’s it for my data analysis of this years New York Marathon. Since the running season in the northern hemisphere is pretty much over now as far as bigger events, I will go back and work on some data from past races. I’m interested in the Boston Marathon and the Olympic Marathon from this year. These are definitely the most competitive races and I hope to learn something from the results of pro athletes or ambitious amateurs.

What analytics from which races are you interested in? Let us know in the comments section!

Happy running

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4 comments on “New York Marathon Analytics: What We Learned From The 2016 Results”

  • Jeff Rigby says:

    A couple thoughts on why so few run negative splits: I’ve run the NYC marathon 4 times, including this year. The last 13.1 are a lot tougher than the first half. There’s just a lot more elevation (3 bridges and almost a mile on 5th ave). Also, the race starts with a huge uphill and downhill on the Verrazano. I would bet that most people run mile 2 at a much faster pace than any other mile.

  • Mohammad says:

    Hi Nikita,

    It’s great that you have pulled entire race data in a database, I was on of negative splitter, Bib# 8463


  • Mai says:

    Thank you for such a great breakdown of this year’s race. The second half of the NYC marathon is definitely tougher than the first half due to more hills and an increase in elevation. I was one of those few that ran a negative split so your statistic made my day. 🙂

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