Andrew DeGraff painted maps that show the geography in movies and their characters’ paths. Above is the map for Back to the Future, with 1985 Hill Valley on the top and 1955 Hill Valley on the bottom.
Category Archives: movies
Here’s a fun spin on the name analysis genre by Mary Zam. She compared the distribution of names used in movies against names used in real life:
Thousands of babies are called Sophia or Abigail, Mason or Dylan every year. But writers do not rush to call the main characters with such names. According to the statistic, almost all of the top names are much less common in the film industry rather than in real life. Seems that they just don’t want to use too ordinary names in their scenarios.
However, there are always some exceptions from this rule such as Jack (up to x3), Maria (x3), Peter (x2) or Sarah (x4.5). But such names makes only about 5-10% of top each decade. On the other hand, there is a bunch of “cinematic” names such as Simon (20 times more often in movies & tv than in real life) and Kate (20 times more often) which you won’t find in the real life top lists.
There's big money in wizarding worlds, galaxies far away, and various time-shifted universes. Let's take a stroll through the billions of dollars earned by franchises over the years. Read More
Evie Liu and William Davis, reporting MarketWatch, looked at release strategies of Oscar nominees over the past few years. Some go for the wide release with the movie playing in over 1,500 theaters, whereas others choose a platform release with the movie playing in fewer than 50 theaters. The last seven of eight Best Picture winners went with the latter route.
Many stories don’t follow a linear format. There are flashbacks, or multiple timelines run simultaneously. Story Curves is a research project that tries to visualize the back and forth.
Story curves visualize the nonlinear narrative of a movie by showing the order in which events are told in the movie and comparing them to their actual chronological order, resulting in possibly meandering visual patterns in the curve.
The main part is that top timeline, which shows story order on the y-axis and movie running time on the x-axis. So if you were to visualize a movie that was linear, you’d see a straight line running from the top left corner to the bottom right. For nonlinear movies, like The Usual Suspects, you get a line that fluctuates.
In case the format looks familiar, you might recognize it from The New York Times. They used it to show the nonlinearity of movie trailers, and that piece motivated the Story Curves work. [via @eagereyes]
It’s been quite the year of randomness and things we never would have imagined at any other time before they occurred. So in the spirit of this year, here’s A Christmas Story for you.
I put it in the form of charts, because that’s the only way I know how to communicate. (It’s a problem, I know. I’m working on it. I mean come on, cut me some slack. It’s almost Christmas.)
In any case, as I was saying. Here are some Christmas charts for you. They may or may not be based on movies.
I think we can all agree ’tis the season to be with family and friends rather than Home Alone.
Or maybe you’re not quite there yet. I say just give it time. Maybe take a ride on the Polar Express towards the Miracle on 34th Street. You might see a Grinch or an Elf. Who knows? Keep an eye out for any lengthy Clauses though.
I think all in all, It’s a Wonderful Life.
And if you look, you’ll see that Love Actually is all around.
In a collaborative effort, the Geena Davis Institute on Gender in Media computed screen time for men and women algorithmically, in contrast to the more crude measurement of script lines. Key findings:
Male characters received two times the amount of screen time as female characters in 2015 (28.5% compared to 16.0%).
When a film has a male lead, this gender gap is even wider, with male characters appearing on screen nearly three times more often than female characters (34.5% compared to 12.9%).
In films with a female lead, male characters appear about the same amount of time as female characters (24.0% compared to 22.6%). This means that even when women are featured in a leading role, male characters appear on screen just as often.
Interesting work here. I just wish they included movie names in their charts. It would’ve provided a better connection to the data.
Hollywood has been talking gender equality in the movies more than usual lately, so Hanah Andersen and Matt Daniels for Polygraph looked into the matter from a data perspective.
We didn’t set out trying to prove anything, but rather compile real data. We framed it as a census rather than a study. So we Googled our way to 8,000 screenplays and matched each character’s lines to an actor. From there, we compiled the number of words spoken by male and female characters across roughly 2,000 films, arguably the largest undertaking of script analysis, ever.
I think the general assumption is that getting an Oscar nomination for Best Picture has a direct effect on profits. Krisztina Szucs put together a straightforward interactive that shows this isn’t typically the case.
Each bar represents a percentage of profit for a film. Roll over a bar, and you see three highlighted ones. The first represents the percentage of profit before a nomination, the second represents percentage of profit after a nomination but before the winner announcement, and the third bar represents percentage of profit after the ceremony.
So what you’re looking for is height before the red bar and after it. For impact level, you expect the third bar to be higher or at least the same as the first, but as you can see, most movies make the bulk of their profit in the beginning regardless.
Forget about Shakespeare. Let's look at a real classic: Love Actually. Somehow I made it through the entire holiday season without watching the movie, as someone in my household who is not me really likes it. I'm more of a It's a Wonderful Life guy.
Anyways, David Robinson, a data scientist at Stack Overflow, did a quick analysis of character appearances in Love Actually. The chart above shows how characters appear together in each scene. The vertical axis represents characters and the horizontal axis is scene number. Each vertical line essentially represents a scene and dots signal character appearances.
Check out that last scene where everyone comes together and we learn that love actually is all around. Tear.