The Tour de France Femmes! This is the first time that I've had enough data to prepare the same pre-race analysis that I would normally do for the men's tour - Yay! As usual, I will disclaim that I don't write about cycling - I look at numbers and draw pictures of them. As cyclists we know that the length of a stage is only part of the story; adding an analysis of the volume of climbing adds another dimension to the grandeur and suffering. Cycling Tips have published a very detailed stage by stage breakdown which you can read here.
Have a look at my post on Free the Data to learn about why I'm sharing the source data I collect and prepare, and how you can access it to make your own stuff.
As always: remember that you can click on each of the charts and lines and datapoints for more information. You should also know that while you can view on your phone, the larger the screen, the better the view.
Finally - just like I do for the Giro Donne, I'll be publishing daily update charts across the duration of the event - enjoy!
A comparison of distance and climbing
This chart compares the climbing metres of each stage with the distance travelled. The size of the circle is a ratio of vertical over distance to reflect average metres climbed per kilometre (larger circles being 'steeper'). The daily update version of this chart will accumulate winners names over the course of the tour.
Stage by stage
A comparison to last year
This is the first year of the Tour de France Femme, so I don't have comparative data. It's also very different to the men's race, so not much point in comparing there.
I'm reliant on a third party for the existence and accuracy of this data. I evaluate the quality of the data and sources I rely on prior to publishing.
Typically this post triggers a range of suggestions on how I could improve the accuracy of distance travelled or metres climbed, much of which is usually centred on some sort of weighted analysis of pro cyclist Strava files. This is theoretically feasible with one glaring problem. One of the important dimensions of data quality is Timeliness. This can be summarised as:
Is the data required available at the point in time when I need to perform my analysis?
The answer in respect to the harvesting of those Strava files is "no". This method clearly requires a statistically significant sample of pro cyclists to ride the road and post their files so I can collect them, analyse them and write about them. This is problematic if I wish to write about the route they will ride prior to their riding it. If it wasn't for time, everything would happen at once.
My data source for the 2022 Tour is based on the data published via the partnership between the Tour de France and Strava, on the basis that this is widely available to more readers of bikechart (most of you are from the UK, US and Australia)
Title image: Le Tour Femmes / A.S.O.