A mixture-of-modelers approach to forecasting NCAA tournament outcomes - Yuan, L.-H.; Liu, A.; Yeh, A.; Kaufman, A.; Reece, A.; Bull, P.; Franks, A.; Wang, S.; Illushin, D.; Bornn, L. (Journal of Quantitative Analysis in Sports, 2015)
Predicting the outcome of a single sporting event is difficult; predicting all of the outcomes for an entire tournament is a monumental challenge. Despite the difficulties, millions of people compete each year to forecast the outcome of the NCAA men`s basketball tournament, which spans 63 games over 3 weeks. Statistical prediction of game outcomes involves a multitude of possible covariates and information sources, large performance variations from game to game, and a scarcity of detailed historical data. In this paper, we present the results of a team of modelers working together to forecast the 2014 NCAA men`s basketball tournament. We present not only the methods and data used, but also several novel ideas for post-processing statistical forecasts and decontaminating data sources. In particular, we highlight the difficulties in using publicly available data and suggest techniques for improving their relevance.