OUR FAVORITE BREAKDOWN 💡

The Caitlin Clark Effect 🏀

Watch here → Instagram TikTok

CHARTS 🌁

Banner Year 📅

Chart Type - Dual Axis Line and Bullet Chart 🚄

The message is total fan attendance in the WNBA jumped almost 50% this year - notated by the orange line. But what drove that jump?

Access to raw game-by-game attendance numbers allowed us to plot the unbelievable increase of games with 16K or more fans on a second axis. This year there were 35 of them in the WNBA with 30 including Caitlin Clark’s Fever. 🤯

The league has never been close to that mark before and the last 10 years combined had just 25 of these “super” games.

How we rate it ✭✭✭✭✭

This graph would not survive alone in the wild. 🌲 The dual axis is an immediate turn-off for readers let alone a bullet chart ingrained within it. If I saw this in a newspaper or article I’d turn the page.

But this is NOT the wild. This is not a newspaper or article (kind of). 😬

Combining voice-over and drawing in the video immediately connected the orange line to the blue bars, providing an answer to the question in everyone’s mind - why did attendance jump so much?

And that’s why I love these hybrid data stories, the ability to spoon-feed 🥣 a viewer through an analysis no matter the complexity.

Everybody Eats 📈

Chart Type - Slope Chart ⛷️

Everybody eats.

All 12 teams in the WNBA had an attendance bump this year - averaging 60% across the league. And it could have been even higher. 😳

That’s because several teams were capacity-constrained in their arenas this year. The Chicago Sky are a good example of an exciting team able to fill bigger stadiums but were limited to Wintrust Arena with only 10K seats. Compare that to the red line of the Indiana Fever who were playing in Gainbridge Fieldhouse which can hold 18,000 fans.

The Fever averaged over 17K fans per game this this year, the highest mark ever in the league and an almost 3X increase from last season.

How we rate it ✭✭✭✭✩

Slope charts are great for two-point comparisons. It’s essentially a short-form line chart where you can display a bunch of series’ at once.

But this chart can be dangerous… ⚠️

If the categories (teams in our case) don’t all move in a similar direction it quickly becomes confusing and looks more like a spider web than a graph.

That’s why you need to pick your spots carefully and highlight the important lines in a chart like this. Since we had a dataset that was uni-directional 🆙 the slope chart really hit home.

Honey, we have visitors! 🏡

Chart Type - Bullet Chart (again) 🚄

It can be difficult to isolate the impact of a single person on an entire league. All season we’ve heard of broken records, never-before-seen moments, and hype around Caitlin’s game.

But could we find one non-esoteric angle that really proved out her stardom? ⭐️

Yes.

With the attendance data set, we could distinguish WNBA fans when Caitlin was in the house vs. when she wasn’t. One caveat is some teams moved arenas to local NBA venues when the Fever came to town, jumping those numbers way up - but that’s the point, right?

On average, hosting the Fever in your city brought in 110% more fans than a non-Caitlin Clark game across the league. Talk about impact.

How we rate it ✭✭✭✭✩

I didn’t expect to use one, let alone two bullet charts for this breakdown but I’m glad I did. They’re great for showcasing a subset of a category within a bar chart which this type of data catered for very well.

OTHER STUFF WE MADE 🎥

A few fun breakdowns and partnerships this week, check them out below on Instagram!

Odds of becoming a professional baseball player ⚾️

Shohei Ohtani 50/50 Club chart ⚾️

Why Sidney Crosby is the best guy in Hockey 🏒💰

The future of NBA contracts 📈🏀