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Applying discrete choice models to predict Academy Award winners

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  • Iain Pardoe
  • Dean K. Simonton

Abstract

Summary. 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.

Suggested Citation

  • Iain Pardoe & Dean K. Simonton, 2008. "Applying discrete choice models to predict Academy Award winners," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 375-394, April.
    • Handle: RePEc:bla:jorssa:v:171:y:2008:i:2:p:375-394
    DOI: 10.1111/j.1467-985X.2007.00518.x
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    References listed on IDEAS

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    Cited by:

    1. Henry Aray, 2021. "Oscar awards and foreign language film production: evidence for a panel of countries," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(3), pages 435-457, September.
    2. Féidhlim P. McGowan, 2024. "The rule of tome? Longer novels are more likely to win literary awards," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 48(2), pages 311-329, June.
    3. Andreas Spitz & Emőke-Ágnes Horvát, 2014. "Measuring Long-Term Impact Based on Network Centrality: Unraveling Cinematic Citations," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-12, October.

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