Unlike much of the public I really do listen to what critics say before I go to see a movie. I especially like Kenneth Turan and Joe Morgenstern who lend their critiques to NPR every week. Some of their best reviewed films happen to be ones you’ve never heard of, but will be sorry if you miss. It comes as no surprise that many of these movies don’t make a lot at the box office. As the number of crappy films that do make it to the screen increase every year, resulting in fewer people going out to see movies, studio executives need some sort of crystal ball or…formula to know which films they should greenlight for maximum profitability. Enter Professor Ramesh Sharda of Oklahoma State University. MSNBC reports:

A scientist in the United States says he has come up with a computer program that helps predict whether a film will be a hit or a miss at the box office long before it is even made.

“Our goal is to try to find oil, in a way,” Professor Ramesh Sharda of the Oklahoma State University said Wednesday.

“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.

Yes. That’s exactly what Hollywood needs. More formulaic movies. It’s even more discouraging when you see the variables that Sharda finds will maximize studio profits. Some of them are things that cause me to shy away from a movie:

Sharda applied seven criteria to each movie: its rating by censors, competition from other films at the time of release, strength of the cast, genre, special effects, whether it is a sequel and the number of theaters 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.

I wonder what would happen if you started entering Bollywood films into the system. I predict the system would crash.

The system cannot take into account the intricacies of the plot, but Sharda says it can nonetheless get the revenue category spot-on 37 per cent of the time, and correct to within one category either side 75 per cent of the time. This is enough to make the system a “powerful decision aid”, Sharda says. [Link]