Data Vis for Cycling’s Team Pursuit
During the UCI Track Cycling World Championships, which took place back in April, I spent a while thinking about how to make an arcane sporting event more exciting to more people by using good data vis to help tell the story of what’s happening.
You don’t need to know bike racing to read this article — in fact, if you don’t know bike racing but do know data visualization, then this article is for you. It’s about how to find the story to tell and how to tell it using data visualization.
The background
Let’s start at the basics. You can skip this if you know bikes. Track racing takes place on a velodrome — in this case, a 250-meter track with turns banked at 43 degrees. Riders use bikes that have a one gear and no brakes. Like in track and field, there are many different event formats: both bunch races and individual and team timed events.
One event is the team pursuit, in which a team of 4 riders works together to set the fastest possible time over 4 kilometers. This is a type of event called a time trial — and time trials are nicknamed The Race of Truth. It’s just you, the distance you must cover, and the clock.
When gold, silver, and bronze medals are on the line, teams go head to head: two teams on the track, racing against each other, simply trying to be the fastest team in 4 kilometers. It’s hard to communicate the speed and precision of this event, or the physiological ability required. Athletes turn themselves inside out to go incredibly fast for a little over 4 minutes. And, they have to do it while paying attention to what everyone else on their team is doing — and whether they’re up or down on the other team. Whether they need to go faster — and whether or not they can.
Does the broadcast communicate the excitement?
A team pursuit can make for a very gripping four minutes, but it’s not always easy to see what’s so exciting about the event. To the uninitiated, it can look like eight people riding weird bikes in circles for a few minutes.
The 2017 women’s team pursuit world championship is below. In it, USA and Australia race against each other for the title of World Champions. Winning a world championship is a huge deal — you get a gold medal, a special jersey, and you get to wear a special kit with the world championship stripes for the whole following year. It’s a win that can define a career.
These two well-matched teams raced an incredibly exciting team pursuit. The UCI’s broadcast graphics helped communicate this with on-screen graphics that visualize each lap’s time difference.
It’s a decent visualization: the green box shows the leader’s time, and the red sliver next to it shows the size of the gap to the trailing team. Lap by lap, the audience is supposed to watch this gap grow or shrink.
When Australia takes the lead, the green box flips over to their side. They’re in the lead, with a slim split back to USA.
This is a good way to compare the times of two teams that are riding on opposite sides of the track. But this visualization of the time and split data misses the opportunity to tell the real story of the event.
What’s the story?
“What’s the story?” is the question that anybody working on data visualization needs to ask.
In the case of this event, the story isn’t the difference in each lap. It’s how that difference changes throughout the event.
Using the available data from the event, I made a simple adjustment to what they show and how they show it: instead of visualization that snapshot data, I stacked them, retaining each lap split to tell the story through time instead of in instantaneous segments.
Imagine if, lap by lap, these data cascade down the side of the screen. Look at how much more of a story is told by these data — it reveals an event in which the USA jumps out to an early lead that’s quickly erased by Australia, who builds up a nice gap at 1500 meters into the event, only to lose it, regain it, lose it, and regain it again before the USA makes a stunning come-from-behind victory in the final two laps.
This story is obscured in a broadcast. Since a camera can only catch one team at a time, a viewer doesn’t always know how the two teams compare. And even if they could, it’s hard to remember what happens throughout a fast-paced four and a half minutes.
Tell the story
The job of data visualization isn’t to make pretty pictures. It’s to tell the story that the data reveal.
If you’re a data visualizer, take this article as a way to keep asking questions, to probe whether you’re showing the right data to tell the story.
If you’re somebody involved in the UCI’s broadcast team — take this as an idea for making the broadcasts even better. The data are there. The story is there. Combine them in the right way and you can hook in more viewers by communicating the nailbiting play-by-play of The Race of Truth.
(And if you’re Chloe Dygert, Jen Valente, Kim Geist, and Kelly Catlin: y’all are amazing.)
