Predicting competitions by combining conditional logistic regression and subjective Bayes: An Academy Awards case study
Christopher T. Franck, Christopher E. Wilson
The Annals of Applied Statistics · 2021 · Volume 15 · Issue 4
Abstract
Predicting the outcome of elections, sporting events, entertainment awards and other competitions has long captured the human imagination. Such prediction is growing in sophistication in these areas, especially in the rapidly growing field of data-driven journalism intended for a general audience as the availability of historical information rapidly balloons. Providing statistical methodology to probabilistically predict competition outcomes faces two main challenges. First, a suitably general modeling approach is necessary to assign probabilities to competitors. Second, the modeling framework must be able to accommodate expert opinion which is usually available but difficult to fully encapsulate in typical data sets. We overcome these challenges with a combined conditional logistic regression/subjective Bayes approach. To illustrate the method, we reanalyze data from a recent Time.com piece in which the authors attempted to predict the 2019 Best Picture Academy Award winner using standard logistic regression. Toward engaging and educating a broad readership, we discuss strategies to deploy the proposed method via an online application.
Keywords
Academy Awards, conditional logistic regression, prior elicitation, Subjective Bayes
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