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Predicting the outcomes of future events is a challenging problem for which avariety of solution methods have been explored and attempted. We present anempirical comparison of a variety of online and offline adaptive algorithms foraggregating experts' predictions of the outcomes of five years of US NationalFootball League games (1319 games) using expert probability elicitationsobtained from an Internet contest called ProbabilitySports. We find that it isdifficult to improve over simple averaging of the predictions in terms ofprediction accuracy, but that there is room for improvement in quadratic loss.Somewhat surprisingly, a Bayesian estimation algorithm which estimates thevariance of each expert's prediction exhibits the most consistent superiorperformance over simple averaging among our collection of algorithms.

An Empirical Comparison of Algorithms for Aggregating Expert Predictions