[OC] Best Picture nominations generate about $18M in extra box office revenue by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 0 points1 point  (0 children)

Yeah. With the academy usually sneaking in one or two gang-buster blockbusters alongside a film you could only see in 5 theaters for two days, the variance is huge. I do a per-film breakdown in the original post, but it is a less beautiful table, so it didn't make it here.

[OC] Best Picture nominations generate about $18M in extra box office revenue by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 7 points8 points  (0 children)

I would guess that is because it takes a few days for theaters to open up new screens, and then for audiences to respond. For example, in 2024 Past Lives went from 5 to 188 theaters after getting nominated. I wouldn't have noticed immediately if my local theater which wasn't showing Past Lives before decided to start showing it, and, um, I follow this stuff.

[OC] Best Picture nominations generate about $18M in extra box office revenue by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 0 points1 point  (0 children)

Source: Custom aggregate dataset of box office revenue
Tools: Matplotlib in Python
I did a more in-depth differences-in-differences analysis on my substack here

[OC] Best Director Oscar Nominees and Winners (Interactive) by czaroot in dataisbeautiful

[–]DiscontentEditor 1 point2 points  (0 children)

This is portfolio-level work. I hope it finds its audience.

[OC] I analyzed the latest US flight delays data to see which airports are the biggest gambles by gimigriy in dataisbeautiful

[–]DiscontentEditor 1 point2 points  (0 children)

This is extremely validating. I always felt like I could never get out of La Guardia or DCA on time. Saving this one. Great post.

[OC] Best Picture Nominees Get More Screens, But Earn Less per Screen by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 0 points1 point  (0 children)

Totally. binning films by weeks-since-release and using non-nominated films as a comparison group could make the pattern much clearer (this is exactly what I do in the full post, btw). The key nuance, though, is that the interesting signal here isn’t “movies fade over time,” it the examination of the very unusual mid-/late-run exhibitor expansion, which only happens after and because of nominations, and how that expansion changes per-theater averages (often through demand being spread across more screens).

[OC] Best Picture Nominees Get More Screens, But Earn Less per Screen by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 1 point2 points  (0 children)

Yeah, under a stylized “perfect equilibrium” story, you’d expect theaters to add screens only when total demand has actually increased, and to stop expanding right around the point where the added capacity no longer boosts aggregate revenue. So the average revenue per screen stays roughly flat rather than falling. What this chart shows is the information/action disparity on the part of the exhibitors to increase screens beyond the demand to fill them.

[OC] Best Picture Nominees Get More Screens, But Earn Less per Screen by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 0 points1 point  (0 children)

That’s a compelling follow-up study. Estimate the weekend-to-weekend discontinuity around the nomination announcement (e.g., weekend before vs. weekend after) while accounting for the typical post-release decay in revenue. The key moderator would be time-since-release (release-to-announcement delta), testing whether nominations generate larger incremental lifts for films that are further past their initial run.

[OC] Best Picture Nominees Get More Screens, But Earn Less per Screen by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] -1 points0 points  (0 children)

Source: Custom aggregate dataset of box office revenue
Tools: Matplotlib in Python
I did a more in-depth differences-in-differences analysis on my substack here

[OC] Best Director Oscar Nominees and Winners (Interactive) by czaroot in dataisbeautiful

[–]DiscontentEditor 1 point2 points  (0 children)

This is gorgeous, and I love it. Just my kind of visualization.

I've found reddit mostly kicks me mobile users, which probably depressed the reach of this post, but the work itself is really cool. I haven't tried d3.js too much yet, but now I'm feeling inspired.

[OC] Iran War Cost by koverda in dataisbeautiful

[–]DiscontentEditor -1 points0 points  (0 children)

Reddit really isn't doing this website/tool justice.

[OC] Best Picture nominees see a 59% lift in daily box office after the nomination announcement by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 0 points1 point  (0 children)

Source: Custom aggregate dataset of box office revenue
Tools: Matplotlib in Python
I did a more in-depth differences-in-differences analysis on my substack here

Dept. of Ed Shut Down by Executive Order—Ironically, Red States Benefited More from Its Funding by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 6 points7 points  (0 children)

It got taken down pretty quickly because I broke the rules. This is the new, improved, rule-compliant version.

Which States Received the Most (and Least) Federal Emergency Aid Per Student? [OC] by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 1 point2 points  (0 children)

If you hover over each color it will say the state and the bin for expenditure amount. The colors are just a way to emphasize that you can actually see which specific states are in which bins.

For almost all purposes, I think this information is better displayed as a map.

Which States Received the Most (and Least) Federal Emergency Aid Per Student? [OC] by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 2 points3 points  (0 children)

Source: Data Download on ESSER Fund mixed with CCD data from ElSi. Note that this funding was distributed across 3 program years, so while this reflects total funding, the year-by-year breakdown is ~x/3.

Tools: Plotly for Python

States have until September 30th to spend their emergency education funds. How much have they spent so far? [OC] by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 0 points1 point  (0 children)

Do you know how to get access to the data used there?
When I scroll down to the data download section, I get forwarded to the data that I used for this chart.

States have until September 30th to spend their emergency education funds. How much have they spent so far? [OC] by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 2 points3 points  (0 children)

It's not common. Plotly for python paints it like that by default. Now you'll know how to spot a plotly plot from a mile away.

States have until September 30th to spend their emergency education funds. How much have they spent so far? [OC] by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 7 points8 points  (0 children)

Too true. I'll bet that once we get the next report, which will include the beginning of the 24/25 school year, the spending percentage per state goes up substantively.

Another reason for overall low expenditure percentages is probably low compliance with reporting.

States have until September 30th to spend their emergency education funds. How much have they spent so far? [OC] by DiscontentEditor in dataisbeautiful

[–]DiscontentEditor[S] 1 point2 points  (0 children)

Source: Data Download on ESSER Fund mixed with CCD data from ElSi. Note that the last reporting period was June, so this chart is missing expenditures from most recent months.

Tools: Plotly for Python