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Comparison of Different Ranking Methods in Wine Tasting*

Published online by Cambridge University Press:  07 November 2017

Jing Cao  and
Lynne Stokes
Jing Cao*
Affiliation:
Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, P.O. Box 750332, Dallas, TX 75275-0332
Lynne Stokes
Affiliation:
Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, P.O. Box 750332, Dallas, TX 75275-0332; e-mail: [email protected].
*
e-mail: [email protected] (corresponding author).
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Abstract

In this paper, we compare three ranking methods in wine tasting in terms of their respective accuracy levels. The first two are the original-score average and rank average, which are conventional methods in practice. The third is a relatively new ranking method called Shapley ranking. It is a game-theory-based ranking method, whereby judges are required not to rank order or score all the wines but only to choose a subset that they find meritorious. A simulation study is designed, wherein the data-generating scheme mimics how the real wine-tasting data are produced. We also consider two criteria in the comparison: the squared-error loss, which is a suitable measure when accurate ranking of all wines is of interest; and the percentile loss, which only considers whether the wines are correctly put in a certain subset. The main conclusion from our study is that the ranking based on score average is generally more accurate than that based on rank average. Shapley ranking, with the consideration that it puts less burden on judges in wine tasting, may outperform the other methods in certain conditions. (JEL Classifications: C11, C15, D72, D81)

Keywords

model selectionranking methodsShapley rankingsimulationwine tasting
Type
Articles
Information
Journal of Wine Economics , Volume 12 , Issue 2 , May 2017 , pp. 203 - 210
Copyright
Copyright © American Association of Wine Economists 2017 

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Footnotes

*

The authors would like to thank the editor and reviewers for their helpful and constructive comments.

References

Ashenfelter, O., and Quandt, R. (1999). Analyzing a wine tasting statistically. Chance, 12, 16–20.Google Scholar
Cao, J., and Stokes, L. (2010). Evaluation of wine judge performance through three characteristics: Bias, discrimination, and variation. Journal of Wine Economics, 5(1), 132–142.Google Scholar
Cao, J., Stokes, L., and Zhang, S. (2010). A Bayesian approach to ranking and rater evaluation: An application to grant reviews. Journal of Educational and Behavioral Statistics, 35(2), 194–214.CrossRefGoogle Scholar
Ginsburgh, V., and Zang, I. (2012). Shapley ranking of wines. Journal of Wine Economics, 7(2), 169–180.Google Scholar
Shapley, L. (1953). A value for n-person games. In Kuhn, A. W., and Tucker, A. W. (eds.), Contribution to the Theory of Games, Vol. II, 307–317. Princeton, NJ: Princeton University Press.Google Scholar
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, Series B, Methodological, 64, 583–616.Google Scholar