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- Our most challenging breakdown yet ⭐️
Happy Friday to all who celebrate!
Today we’re double-clicking on one of our most challenging data visualizations to date - The Taylor Swift effect on the NFL which we published in conjunction with Sportsish. It was tough for multiple reasons:
😡 Pissing off Swifties is not a good business decision so the facts needed to be ultra-precise.
👯 The type of data we used was totally homogenous so adding variability to the breakdown wasn’t easy.
Here’s how it went ⬇️
Vision 👁️
It’s no secret Taylor’s new involvement in the NFL has expanded the fanbase - but we’ve never known the true magnitude. So when this fantasy football impact survey dropped from Yahoo, we had to bring it to life and showcase a sliver of the Taylor Swift effect with data.
But from a visualization perspective, survey data can be difficult. 😭
It’s all the same type of data in the form of “this percent of people said this” so spicing it up isn’t easy. Our goal was to add a layer of variability to the data story and keep viewers engaged.
CHARTS 🌁
Overall Participation 🟪
Chart Type - Waffle Chart 🧇
Yahoo’s survey stated that 22% of all fantasy football participants this year were playing for the first time and half of them were women. This stat might not jump off the page but fantasy football’s participant pool is normally 80% men, meaning the 50/50 split of new participants is highly over-indexed toward women. ⚖️
This single statistic set the tone 🔊 for the breakdown.
How we rate it ✭✭✭✭✩
I love waffle charts. Compared to pie and bar charts this gives a better sense of “part of a whole” data. You can even imagine every square is one survey respondent and the story becomes ultra clear.
The data also catered to a “hierarchy format” where we could embed the women's participation callout within overall participation. This wouldn’t work in a pie or bar chart ❌ but the waffle obliged.
Fantasy Football Strategies 𑗏
Chart Type - Triple Donut 🍩🍩🍩
Close your rings! Inspired by Apple Watch’s fitness visuals, this chart creates a tiered “percent of total” graphic - a perfect candidate for survey data. The metrics were:
❤️ 10% of fantasy football participants are playing because of the Taylor/Travis relationship.
⭐️ 37% think drafting players in celebrity relationships is a strong strategy.
8️⃣7️⃣ 59% think drafting Travis Kelce at all costs is a good strategy.
How we rate it ✭✭✩✩✩
There are three things wrong here…
First, any data professional would tell you that “ring charts” aren’t statistically accurate. Since the radius is different for each ring, outer rings are over-represented in a side-by-side comparison if you straighten out the lines. Apple has even caught a bit of flack from the small but mighty 💪 data visualization community for this… They seem to be doing just fine though.
Second, the first category is not like the other two. Talking about general participation and then shifting to draft strategies was a bad decision on my part and could have lost the readers easily. Shame! 😞
Third, it’s hard to draw and looks a bit ugly.
Work/Life Balance 💼
Chart Type - Stacked Proportion Bars 📚
All roads lead home.
Most surveys will heavily use proportion bars like this in their results but they can be overdone. The data here stated:
💼 47% of respondents said doing well in fantasy football at work would help them get a raise and 42% said it would help with a promotion.
💕 On the relationship side, 10% said they were playing fantasy football to impress a romantic partner and 16% claimed they were managing a team with their significant other.
How we rate it ✭✭✭✩✩
This chart was… fine. While it can be a bit boring, splitting the charts into logical pairs (work and relationships) made the comparisons easier to digest.
Plus nothing will ever beat a good old-fashioned bar chart for side-by-side comparisons.
OTHER STUFF WE MADE 🎥
A few fun breakdowns and partnerships this week, check them out below on Instagram!
Thanks for reading and please feel free to reach back out with any feedback! Love it or hate it we’re all ears 👍
— Claire and Riley
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