  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.
