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

Yuan Lo-Hua, Liu Anthony, Yeh Alec, Aaron Kaufman, Reece Andrew, Bull Peter, Franks Alex, Wang Sherrie, Illushin Dmitri and Bornn Luke ()
Additional contact information
Yuan Lo-Hua: Harvard University – Statistics, Cambridge, Massachusetts, USA
Liu Anthony: Harvard University – Statistics, Cambridge, Massachusetts, USA
Yeh Alec: Harvard University – Statistics, Cambridge, Massachusetts, USA
Reece Andrew: Harvard University – Psychology, Cambridge, Massachusetts, USA
Bull Peter: Harvard University – Institute for Applied Computational Science, Cambridge, Massachusetts, USA
Franks Alex: Harvard University – Statistics, Cambridge, Massachusetts, USA
Wang Sherrie: Harvard University – Statistics, Cambridge, Massachusetts, USA
Illushin Dmitri: Harvard University – Statistics, Cambridge, Massachusetts, USA
Bornn Luke: Harvard University – Statistics, Cambridge, Massachusetts, USA

Journal of Quantitative Analysis in Sports, 2015, vol. 11, issue 1, 13-27

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.

Keywords: basketball; data decontamination; forecasting; model ensembles (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://doi.org/10.1515/jqas-2014-0056 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:11:y:2015:i:1:p:13-27:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html

DOI: 10.1515/jqas-2014-0056

Access Statistics for this article

Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman

More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().