Blockbuster or flop

Oklahoma State University professors have developed a neural network based computer program that can predict the box office success of movies before they hit theatres.

The software developed by by Drs. Ramesh Sharda and Dursun Delen uses seven parameters to determine the revenue range of a movie before its release.

Once the revenue range is determined the movie is classified in one of nine categories from ‘super flops’ that take in less than $1 million to ‘super blockbusters’ that gross more than $200 million.

“All the variables we use are factors you can usually consider as you are deciding whether to make a movie, so we expect this to be a powerful decision aide for potential investors,” said Sharda.

The OSU Regents professor of management science and information systems and his colleague Delen picked seven factors to help their so-called “neural network” decide on a revenue range for an upcoming movie.

The seven variables include the star value of the cast, the movie’s age rating, the time of release against that of competitive movies, the film’s genre, the degree of special effects used, whether it is a sequel or not, and the number of screens it is expected to appear on at its opening.

Sharda and Delen have been ’teaching’ their neural network by using data from actual movies. The pair has input data from 834 movies released between 1998 and 2002 to ensure the system’s reliability.

The system is expected to receive a welcome reception from the movie industry which is in a slump this year compared to last.