A mixture-of-modelers approach to forecasting NCAA tournament outcomes

Lo Hua Yuan, Anthony Liu, Alec Yeh, Aaron Kaufman, Andrew Reece, Peter Bull, Alex Franks, Sherrie Wang, Dmitri Illushin, Luke Bornn

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)13-27
Number of pages15
JournalJournal of Quantitative Analysis in Sports
Issue number1
StatePublished - Mar 1 2015


  • basketball
  • data decontamination
  • forecasting
  • model ensembles

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Decision Sciences (miscellaneous)


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