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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Steve Rigdon, PhD

CiteScore 2017: 0.67

SCImago Journal Rank (SJR) 2017: 0.290
Source Normalized Impact per Paper (SNIP) 2017: 0.853

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Volume 13, Issue 1


Volume 1 (2005)

On the importance of the probabilistic model in identifying the most decisive games in a tournament

Francisco Corona / Juan de Dios Tena Horrillo
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  • University of Liverpool, Management School, Chatham Street, Liverpool L19 7ZH, UK, Tel.: + 44(0)1514752486
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/ Michael Peter Wiper
Published Online: 2017-03-03 | DOI: https://doi.org/10.1515/jqas-2016-0013


Identifying the decisive matches in international football tournaments is of great relevance for a variety of decision makers such as organizers, team coaches and/or media managers. This paper addresses this issue by analyzing the role of the statistical approach used to estimate the outcome of the game on the identification of decisive matches on international tournaments for national football teams. We extend the measure of decisiveness proposed by Geenens (2014) in order to allow us to predict or evaluate the decisive matches before, during and after a particular game on the tournament. Using information from the 2014 FIFA World Cup, our results suggest that Poisson and kernel regressions significantly outperform the forecasts of ordered probit models. Moreover, we find that although the identification of the most decisive matches is independent of the model considered, the identification of other key matches is model dependent. We also apply this methodology to identify the favorite teams and to predict the most decisive matches in 2015 Copa America before the start of the competition. Furthermore, we compare our forecast approach with respect to the original measure during the knockout stage.

Keywords: entropy; game importance; Kernel regression; ordered probit model; Poisson model


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About the article

Published Online: 2017-03-03

Published in Print: 2017-03-01

Citation Information: Journal of Quantitative Analysis in Sports, Volume 13, Issue 1, Pages 11–23, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2016-0013.

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Francisco Corona, David Forrest, J.D. Tena, and Michael Wiper
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