Predicting Academy Award winners using discrete choice modeling

Iain Pardoe

Abstract

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 a golden Oscar statuette. There are no end of pet theories about which nominees are most likely to win, based on observations such as the fact that only three movies have won the Best Picture Oscar without also receiving a Best Director nomination. 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. Using Bayesian modeling techniques, it is possible to predict the winners in these four categories with a reasonable degree of success. The analysis also reveals which past nominees have really upset the odds (winners with low estimated probability of winning), and which appear to have been truly robbed (losers with high estimated probability of winning).

Key Words: Bayesian; Forecasting; Movies; Multinomial logit.

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Last updated: September 14, 2005


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

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