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Licensed Unlicensed Requires Authentication Published by De Gruyter February 24, 2015

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 and Luke Bornn EMAIL logo

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.


Corresponding author: Luke Bornn, Harvard University – Statistics, Cambridge, Massachusetts, USA, e-mail:

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Published Online: 2015-2-24
Published in Print: 2015-3-1

©2015 by De Gruyter

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