Meet Hal, the blockbuster-predicting computer
"We are trying to forecast the success of a movie based on things that are decided before a movie has been made," he told Reuters by telephone.
Sharda, an expert in information systems, has been working on the model for seven years and analysed more than 800 films before publishing a paper which appears in "Expert Systems With Applications" early next year.
Sharda applied seven criteria to each movie; its rating by censors, competition from other films at the time of release, star value, genre, special effects, whether it is a sequel and the number of theatres it opens in.
Using a neural network to process the results, the films are placed in one of nine categories, ranging from "flop," meaning less than $1 million at the box office, to "blockbuster," meaning more than $200 million.
The results of the study showed that 37 percent of the time the network accurately predicted which category the film fell into, and 75 percent of the time was within one category of the correct answer.
I'll be interested to hear more about this... seems like you could lose a lot of money on the 63 percent of movies where the computer can't accurately predict. And some of the criteria the system uses, like number of theaters a film opens in, and competition from other films, aren't things that studio chiefs know when they're considering whether to green-light a film and commit millions of dollars.