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Quantifying Randomness Versus Consensus in Wine Quality Ratings*

Published online by Cambridge University Press:  29 April 2014

Jing Cao
Jing Cao*
Affiliation:
Associate Professor, Department of Statistical Science, Southern Methodist University, 6425 Boaz Street, Dallas, TX 75275; e-mail: [email protected].
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Abstract

There has been ongoing interest in studying wine judges' performance in evaluating wines. Most of the studies have reached a similar conclusion: a significant lack of consensus exists in wine quality ratings. However, a few studies, to the author's knowledge, have provided direct quantification of how much consensus (as opposed to randomness) exists in wine ratings. In this paper, a permutation-based mixed model is proposed to quantify randomness versus consensus in wine ratings. Specifically, wine ratings under the condition of randomness are generated with a permutation method, and wine ratings under the condition of consensus can be produced by sorting the ratings for each judge. Then the observed wine ratings are modeled as a mixture of ratings under randomness and ratings under consensus. This study shows that the model can provide excellent model fit, which indicates that wine ratings, indeed, consist of a mixture of randomness and consensus. A direct measure is easily computed to quantify randomness versus consensus in wine ratings. The method is demonstrated with data analysis from a major wine competition and a simulation study. (JEL Classifications: C10, C13, C15)

Keywords

Mixed modelpermutationquantifying randomnesswine judge consensus
Type
Articles
Information
Journal of Wine Economics , Volume 9 , Issue 2 , August 2014 , pp. 202 - 213
Copyright
Copyright © American Association of Wine Economists 2014 

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Footnotes

*

The author acknowledges support from the California State Fair Commercial Wine Competition for making the data available. Special thanks go to Robert T. Hodgson, G.M. “Pooch” Pucilowski, and Aaron E. Kidder. The author also thanks the reviewer for helpful suggestions that improved the paper.

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