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

Lo-Hua Yuan 1 , Anthony Liu 1 , Alec Yeh 1 , Aaron Kaufman 2 , Andrew Reece 3 , Peter Bull 4 , Alex Franks 1 , Sherrie Wang 1 , Dmitri Illushin 1  and Luke Bornn 1
  • 1 Harvard University – Statistics, Cambridge, Massachusetts, USA
  • 2 Harvard University – Government, Cambridge, Massachusetts, USA
  • 3 Harvard University – Psychology, Cambridge, Massachusetts, USA
  • 4 Harvard University – Institute for Applied Computational Science, Cambridge, Massachusetts, USA
Lo-Hua Yuan, Anthony Liu, Alec Yeh, Aaron Kaufman, Andrew Reece, Peter Bull, Alex Franks, Sherrie Wang, Dmitri Illushin and Luke Bornn

Abstract

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.

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JQAS, an official journal of the American Statistical Association, publishes research on the quantitative aspects of professional and collegiate sports. Articles deal with subjects as measurements of player performance, tournament structure, and the frequency and occurrence of records. Additionally, the journal serves as an outlet for professionals in the sports world to raise issues and ask questions that relate to quantitative sports analysis.

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