Predicting competitions by combining conditional logistic regression and subjective Bayes: An Academy Awards case study

TL;DR

This paper introduces a combined conditional logistic regression and subjective Bayes approach to probabilistically predict competition outcomes, exemplified by predicting the 2019 Best Picture Oscar, addressing challenges of modeling and expert opinion integration.

Contribution

The paper presents a novel combined statistical framework that integrates logistic regression with subjective Bayesian methods for competition outcome prediction.

Findings

Successfully re-analyzed Oscar prediction data

Demonstrated the method's ability to incorporate expert opinions

Provided strategies for deploying the method via online tools

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 re-analyze 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. Towards engaging and educating a broad readership, we discuss strategies to deploy the proposed method via an online application.