Data science and women's cycling

Amelia Barber has written a post that combines her loves of women’s cycling and data science (link).

Her post focuses on on the demographics of the elite women’s road teams (46 teams registered with the UCI). For the post, Amelia scraped raw rider data (August 2019) from the Union Cycliste Internationale (UCI) website (link).

The code Amelia used for the post is shared by her in a GitHub repository (link). The analysis and plots were done in R and the interactive plots were made using Plotly.

I see Amelia’s work as a great example of open sharing and a desire to make much more public women’s performances.

Some yeas ago, I was involved in a project to film the final stages of women’s road races. At the time, there was very little, if any, multi-camera coverage of women’s races and the aim of the project was to see if we could make finishes much more authentic in training and competition. This was in pre-drone days and we manged with appropriate permissions to fly remote model aircraft, with video cameras, to track the final stages of races and training.

The footage obtained started to transform performance and it led to many conversations across the sport about positioning, techniques and tactics.

I am looking forward to Amelia opening up these conversations too. I am keen also to see where her work in R, ggplot2 and Plotly will take her.

Photo Credit

Peleton (BBC Sport, Twitter)


Part Two of the post was published on 25 August 2019 (link)


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