Every year since 1929, 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 research 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.
Over the period 1939-2020, the model correctly predicted 232 out of 328 awards (71%). (There was only sufficient data for the model to be able to predict from 1939 onwards.) Broken down by category, correct predictions were 70% for Best Picture, 83% for Director, 72% for Lead Actor, and 59% for Lead Actress. Until very recently, predicting has become easier over time. For example, over the last 45 years (1976-2020), the model correctly predicted 144 out of 180 awards (80%). Broken down by category, correct predictions for this period were 69% for Best Picture, 91% for Director, 82% for Lead Actor, and 78% for Lead Actress. Over the last 15 years (2006-2020), the model correctly predicted 47 out of 60 awards (78%).
- Latest predictions: 2021 (Awards ceremony Apr 25, 2021)
- Previous published predictions (78% accuracy):
- Iain Pardoe's research page (data and code for making the predictions are available here).
- Dean K. Simonton's home page.
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