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Activity Number: 392 - Bayesian Analysis of Complex, Structured Health and Social Data
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #320939
Title: Data Journalism, Statistical Methodology, and the Academy Awards: A Case Study
Author(s): Christopher Franck* and Christopher Wilson
Companies: Virginia Tech and
Keywords: Conditional logistic regression; Prior elicitation; Subjective Bayes; Academy Awards; Data journalism

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. 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. In this talk I describe a recent effort to furnish statistical methodology that (i) overcomes both challenges, and (ii) is also of interest to the broad audience served by data journalists. The analysis of 2019 and 2020 Academy Awards data provides a case study, and I will also discuss the opportunities and challenges faced by statisticians and data journalists who embark on these sorts of collaborations.

Authors who are presenting talks have a * after their name.

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