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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Rigdon, Steve

Editorial Board Member: Glickman, PhD, Mark

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CiteScore 2016: 0.44

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1559-0410
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An Oracle method to predict NFL games

Eduardo Cabral Balreira / Brian K. Miceli / Thomas Tegtmeyer
Published Online: 2014-03-27 | DOI: https://doi.org/10.1515/jqas-2013-0063

Abstract

Multiple models are discussed for ranking teams in a league and introduce a new model called the Oracle method. This is a Markovovian method that can be customized to incorporate multiple team traits into its ranking. Using a foresight prediction of NFL game outcomes for the 2002–2013 seasons, it is shown that the Oracle method correctly picked 64.1% of the games under consideration, which is higher than any of the methods compared, including ESPN Power Rankings, Massey, Colley, and PageRank.

Keywords: foresight predictions; NFL; oracle; PageRank; rankings

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About the article

Corresponding author: Eduardo Cabral Balreira, Mathematics, Trinity University, One Trinity Place, San Antonio, TX 78212, USA, Tel.: +2109998243, e-mail:


Published Online: 2014-03-27

Published in Print: 2014-06-01


The method of Massey used for the BCS Rankings in college football is proprietary, and thus not publicly available. The method we discuss is the original idea of Massey (1997), which he developed for an honors thesis as an undergraduate at Bluefield College.

For this data, we compute the rating vector based on the algorithm and Matlab routine given in Hunter (2004).

In Langville and Meyer (2003), it is given that Google originally used α=0.85.

In Callaghan et al. (2007), the authors named their method as Random Walker Ranking. As that description may also fit other Markov methods, we refer to it as Biased Random Walker.

All the variations of PageRank we have tested, always promote T6 to the 2nd highest ranking.

All computations were performed using Matlab R2013a.

One could certainly argue that this addition of the Oracle node opens the possibility for a distortion of the rankings in some way, especially by artificially forcing connectedness early in season. However, the data supports that, at least for standard choices of the statistics—score differential, wins, etc.—incorporated into the up and down vectors, this does not happen.

Teams with identical ranks for all methods other than WH meet, on average, less than once per year in the weeks considered. In the WH method, teams with the same record meet, on average, 24 times per season in the weeks considered.


Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2013-0063.

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