Applying discrete choice models to predict Academy Award winners

Iain Pardoe and Dean K. Simonton


Every year since 1928, the Academy of Motion Picture Arts and Sciences has recognized outstanding achievement in film with their prestigious Academy Award, or Oscar. Before the winners in various categories are announced, there is intense media and public interest in predicting who will come away from the awards ceremony with an Oscar statuette. There are no end of theories about which nominees are most likely to win, yet despite this, there continue to be major surprises when the winners are announced. This article frames the question of predicting the four major awards - picture, director, actor in a leading role, actress in a leading role - as a discrete choice problem. It is then possible to predict the winners in these four categories with a reasonable degree of success. The analysis also reveals which past results might be considered truly surprising - nominees with low estimated probability of winning who have overcome nominees who were strongly favored to win.

Keywords: Bayesian; Conditional logit; Films; Forecasting; Mixed logit; Motion pictures; Movies; Multinomial logit

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Last updated: August 6, 2007

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© 2005, Iain Pardoe, Lundquist College of Business, University of Oregon

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