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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

4 Issues per year


CiteScore 2016: 0.44

SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358

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1559-0410
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Ranking rankings: an empirical comparison of the predictive power of sports ranking methods

Daniel Barrow / Ian Drayer / Peter Elliott / Garren Gaut / Braxton Osting
Published Online: 2013-05-27 | DOI: https://doi.org/10.1515/jqas-2013-0013

Abstract

In this paper, we empirically evaluate the predictive power of eight sports ranking methods. For each ranking method, we implement two versions, one using only win-loss data and one utilizing score-differential data. The methods are compared on 4 datasets: 32 National Basketball Association (NBA) seasons, 112 Major League Baseball (MLB) seasons, 22 NCAA Division 1-A Basketball (NCAAB) seasons, and 56 NCAA Division 1-A Football (NCAAF) seasons. For each season of each dataset, we apply 20-fold cross validation to determine the predictive accuracy of the ranking methods. The non-parametric Friedman hypothesis test is used to assess whether the predictive errors for the considered rankings over the seasons are statistically dissimilar. The post-hoc Nemenyi test is then employed to determine which ranking methods have significant differences in predictive power. For all datasets, the null hypothesis – that all ranking methods are equivalent – is rejected at the 99% confidence level. For NCAAF and NCAAB datasets, the Nemenyi test concludes that the implementations utilizing score-differential data are usually more predictive than those using only win-loss data. For the NCAAF dataset, the least squares and random walker methods have significantly better predictive accuracy at the 95% confidence level than the other methods considered.

Keywords: cross validation; Friedman test; Nemenyi test; hypothesis testing; sports rankings

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About the article

Corresponding author: Braxton Osting, UCLA, Department of Mathematics, 405 Hilgard Avenue, Los Angeles, CA 90095, USA, Tel.: +3108252601


Published Online: 2013-05-27

Published in Print: 2013-06-01


http://www.masseyratings.com/cf/compare.htm.

In equation (2), we take the fraction to be

if the game results in a 0–0 tie.

A digraph is weakly connected if replacing its arcs with undirected edges yields a connected graph.

Recall that for a matrix with non-negative entries, there exists a positive, real eigenvalue (called the Perron-Frobenius eigenvalue) such that any other eigenvalue is smaller in magnitude. The Perron-Frobenius eigenvalue is simple and the corresponding eigenvector (called the Perron-Frobenius eigenvector) has non-negative entries. See, for example, Horn and Johnson (1991).

The matrices W and S are irreducible if the corresponding directed graph is strongly connected. The matrix W is irreducible if there is no partition of the teams V=V1ߎV2 such that no team in V1 has beat a team in V2.


Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2013-0013.

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