Armstrong J, Sullivan A, Perry GM. Performance of non-binary athletes in mass-participation running events.

The full article can be found here.


Tell us more about yourself and the author team.

I am a mathematician, and my usual area is financial mathematics, but I’ve also been a keen coach and volunteer in my local running club for many years. My marathon PB is 2:43. Alice is a sociologist who is an expert in data collection and is particularly interested in data on sex and gender. She was Chair of her running club for many years, and her marathon PB is 3:48. George is an athletics coach, sports businessman and writer. His marathon PB is 2:48.

What is the story behind your study?

The relative importance of sex and gender identity on outcomes is an area of real political significance and is the subject of a great deal of debate. However, much of the discussion is highly theoretical, and there has been surprisingly little empirical research in this area.

One of the reasons for this is the lack of data. In the UK, the trans population can be estimated to be about 0.5%, but even this primary figure is uncertain due to problems with the question asked on the UK census. Because of these low numbers, unless a study is specifically designed to look at the trans population, it will generally be impossible to separate sex and gender identity in the statistics.

Because we are all interested in running and data, we realized that the non-binary category in road races provided a novel opportunity for analysis. The vast numbers of runners participating in mass participation road races gave us access to a big enough data set to examine the effects of sex and gender identity.

In your own words, what did you find?

We found that there is a sex gap in race times between athletes who identify as non-binary and that there is no evidence that the gap between males and females is less for athletes who identify as non-binary.

What was the main challenge you faced in your study?

New York Road Runners only records whether a runner is male, female or non-binary. As a result, our data set did not contain the sex of non-binary athletes, yet we wanted to test the effects of sex!

Fortunately, we could look at previous race results for the athletes to see if they revealed their sex, and if that failed, we could use their given names to come up with a probability model for the athlete’s sex. This meant we had to devise a mathematical technique that would allow us to measure the effect of sex even when sex is not known with certainty.

If there is one takeaway message from your study, what would it be?

Gender identity is clearly essential to many people, but nevertheless, sex matters. If we want to understand trans experiences, data collection on both sex and gender identity is critical.

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