Iain's Oscar Predictions

Applying discrete choice models to predict Academy Award winners

by 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.

Over the period 1938-2015, the model correctly predicted 222 out of 312 awards (71%). (There was only sufficient data for the model to be able to predict from 1938 onwards.) Broken down by category, correct predictions were 72% for Best Picture, 83% for Director, 72% for Lead Actor, and 58% for Lead Actress. Predicting has become easier over time. For example, over the last 41 years (1975-2015), the model correctly predicted 134 out of 164 awards (82%). Broken down by category, correct predictions for this period were 73% for Best Picture, 93% for Director, 83% for Lead Actor, and 78% for Lead Actress.

Iain Pardoe's research homepage (data and code for making the predictions are available here).

Dean K. Simonton's research homepage.


Last updated: February 7, 2017


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© 2017, Iain Pardoe