B1027
Title: Predicting competitions by combining conditional logistic regression and subjective Bayes: An Academy Awards case study
Authors: Christopher Wilson - TIME (United States)
Christopher Franck - Virginia Tech (United States) [presenting]
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. 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 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 we will also discuss the opportunities and challenges faced by statisticians and data journalists who embark on these sorts of collaborations.
