Software Predicts Movie Success

scheming daemons writes "TechNewsWorld has an article about software that predicts whether a movie will be successful or not by factoring in its rating by censors (e.g. G, PG, R), strength of the cast, genre, competition from other films at the time of release, special effects, whether it is a sequel, and the number of theaters in which it will show."

Comments

Re:Hollywood has used this formula for years:

[grub]

More explosions, chases and boobs done with a *smaller budget* == more profit. :)

Re:Program??

[erikus]

actually, they did use excel. google cache

citation + main (unimpressive) results

[dankelley]

The citation for the research paper is Ramesh Sharda and Dursun Delen, Predicting box-office success of motion pictures with neural networks, Expert Systems with Applications, Volume 30, Issue 2, February 2006, Pages 243-254. (http://www.sciencedirect.com/science/article/B6V0 3-4GV2PCH-1/2/35524bc2ff6fd852c98d8c9f3c3dc8c9). This is not a free journal, but if you're at a university it is quite likely that you have a library subscription. The paper is an interesting read, whether you're keen on film or on neural nets.

The main result is that the method (neural net) works a little better than other methods on the same data (Table 4 of paper). It scores 75% in a test; conventional regression scores 71%. As they say in the statistical literature, "big woop"; the fancy new thing is marginally better than the simple old thing.

As for the practical side of things, the main predictive variable is the number of screens on which the film was initially shown. The next-highest predictive variables are a variable representing the use of technical effects and a variable represengint the actors' reputation. Well, none of these indicates that this tool (or others discussed in the paper) is of any real use to the industry. The suggested use of the tool is to predict movie success. But the main predictive variables all represent things the industry already knew, when the film was being made and promoted. It's like asking a patient if they have a cold, and then charging them to tell them they have a cold.