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6 Kommentare
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](https://cprfilm.substack.com/p/need-a-quick-2-million-get-nominated)
Wow, it’s almost like movies that have been out for a while don’t tend to earn as much as movies that just came out last week
Also one measly year of data is not going to be enough to show this trend meaningfully.
This isn’t beautiful data. It’s obvious data.
More people see moves right after they come out than…later than that? Crazy.
Maybe compare to non-Oscar nominated films and organize the data by weeks since theatrical release. Might be meaningful data then
I would think people tend to see movies in theaters when they first come out, and then on streaming after nomination.
adding more screens is only going to exaserbate the per screen drop.
A better analysis would be to look at the weekend before versus the weekend after announcement. It’s a very well known trend to see that opening weekend is a movie’s best weekend. A few buck that trend, but it’s rare. But if a movie’s been out for 2 months and gets a nomination, how much is that worth?
yah, obviously more theatres want to show the movie after it’s nominated, so the denominator goes up. but if you can earn $400 from 2 theatres or $1000 from 10 theatres, which would you pick? that’s a decrease of 50% per theatre— terrible stat to choose to tell the story,