New maths model to predict box office success

A new mathematical model which can accurately predict films that become blockbusters or flops at the box office - up to a month before the movie is released - has been devised.
The model with a prediction accuracy of up to 99 per cent is based on an analysis of the activity on Wikipedia pages about American films released in 2009 and 2010.
Researchers examined 312 movies, taking into account the number of page views for the movie's article, the number of human editors contributing to the article, the number of edits made and the diversity of on-line users.
The model was applied retrospectively so the researchers systematically charted the on-line buzz on Wikipedia around particular films and compared this with the box takings from the first weekend after release.

The results of the comparison between the predicted opening weekend revenue, using their mathematical model, and the actual figures showed a high degree of correlation.
The mathematical algorithm allowed researchers to predict box office revenues with an overall accuracy of around 77 per cent.

Researchers say this level of accuracy is higher than the best existing predictive models applied by marketing firms, which they estimate to be at around 57 per cent.
They could predict the box office takings of six out of 312 films with 99 per cent accuracy where the predicted value was within one per cent of the real value. Some 23 movies were predicted with 90 per cent accuracy and 70 movies with an accuracy of 70 per cent and above.

The more successful the show, the more accurately the researchers were able to predict box office takings. Researchers said this is possibly due to the increased amount of on-line data generated by films that turn out to be successes.
The model correctly forecast the commercial success of Iron Man 2, Alice in Wonderland, Toy Story 3 and Inception, but failed to accurately forecast the financial return on less successful movies Never Let Me Go, and Animal Kingdom.
"These results can be of great value to marketing firms but more importantly for us; we were able to demonstrate how we can use socially generated on-line data to predict a lot about future human behaviour," Dr Taha Yasseri, from the Oxford Internet Institute at the University of Oxford, said.

"The predicting power of the Wikipedia-based model, despite its simplicity compared with Twitter, is that many of the editors of the Wikipedia pages about the movies are committed movie-goers who gather and edit relevant material well before the release date," said Yasseri.
"We have demonstrated for the first time that Wikipedia edit statistics provide us with another tool to predict social events," co-author Janos Kertesz, from the Central European University of Budapest, Hungary, said.
The study was published in the journal PLoS ONE.