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Applying discrete choice models to predict Academy Award winners

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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. The paper frames the question of predicting the four major awards—picture, director, actor in a leading role and 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 favoured to win.

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

Document Type: Research Article


Affiliations: 1: University of Oregon, Eugene, USA 2: University of California at Davis, USA

Publication date: April 1, 2008

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