Nearly 30% of Superbowl teams and 47% of all Playoff teams won’t even make the playoffs the next year.įor my design choice, I’m using color to identify a few of teams that had the largest positive or negative change in their record over one year. Lucky and unlucky teams often regress to the mean the next year. There are a number of reasons that a team’s record has such high year-to-year variability in the NFL.The small number of games in a season (16), strength of schedule, injuries, luck, and the draft are a few variables that drastically impact a team’s consistency. If there was a stronger correlation from one year to the next, the dots would be a much tighter fit around the trend line. The data points for each team and season are widely dispersed along the trend line when comparing records from Year N to Year N+1. Without further ado, scroll below to explore scores of scatterplots!įor the October #SWDchallenge I'm sharing a scatterplot that I recently created to illustrate the lack of consistency in the NFL from one season to the next. Do you have ideas on what you’d like it to be? Leave a comment with your thoughts! Until then, check out the #SWDchallenge page for the archives of previous months' challenges and submissions. The next monthly challenge will be announced on November 1 and will run through midnight PST on October 8th. png) to and we'll work to include any late entries this week (just a reminder that tweeting on its own isn't enough-we unfortunately don't have time to scrape Twitter for entries, so emailing is the sure way to get your creations included). If you tweeted or thought you submitted one but but don't see it here, email your submission (including your graph attached as. To everyone who submitted examples: THANK YOU for taking the time to create and share your work! The makeovers are posted below in alphabetical order by first name. I love hearing that people are using the challenge to practice new tools, or to try something in one they know that they haven’t done before. There was an impressive variety of tools used this month: Excel, Tableau, Qlik, PowerBI, R (ggplot2 and more), Plotly, Inkscape, and Illustrator. Luida’s whimsical flower-like pies, which plot relative happiness, simply made me happy to look at and explore. Simon used clearly labeled shaded bands to help us understand how the data points in the scatterplot relate to each other and to help with interpretation. included summary frequency distributions (“marginal histograms”) to summarize and lend additional understanding of the data. In terms of specific interesting design choices that stood out to me, I liked Jeremy & Sarah’s tails that put the focus on the 2020 expectation, but provided the context of wealth and age in 1995. Frans’ “Hidden Gems” plots nearly 18,000 data points, making accessible through thoughtful color and words, inviting us to explore the small multiple view. To highlight a couple nice overall designs that caught my eye: Alex packed in a ton of data in his bubblegraph view of birth and death rates by country. I appreciate the steps that many took to make their scatterplots accessible for exploration: clear titling, reference lines, and in some cases dividing the plot into halves or quadrants. While some computed correlations or added trendlines, others used the scatterplot to illustrate the lack of relationship between dimensions. That said, a couple who did employ smart focusing tactics via color and words to highlight specific takeaways include Dennis with his firefighter response times in the Netherlands and James’ impact of the change of a call center script. There were relatively fewer takeaway titles or prominent stories highlighted in the various visuals, perhaps because scatterplots simply seem to lend themselves to being explored. I was excited to see a handful of people layer on the additional dimension of time in a connected scatterplot: Antonio visualized 400 Italian names in an artful view that invites exploration, Bosley clearly depicted home runs by hits over the decades, RJ illustrated that people are going to the movies relatively less but watching comic films relatively more (check out his tilted axes to help orient us reading time left to right), and Tiago helps us understand misery in Portugal by political party (it’s definitely worth the scroll down to see it!).
Forty-seven people accepted, creating and sharing their work! Topics ranged from the light-hearted Star Wars, superheroes, and conversation topics between two friends, to more serious things like immunization, alcohol consumption, income, life satisfaction and diabetes. This month’s challenge was to make a scatterplot.
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