The purpose of this paper is to provide a tutorial of data analysis methods for answering questions that arise in analyzing data from wine-tasting events: (i) measuring agreement of two judges and its extension to m judges; (ii) making comparisons of judges across years; (iii) comparing two wines; (iv) designing tasting procedures to reduce burden of multiple tastings; (v) ranking of judges; and (vi) assessing causes of disagreement. In each case we describe one or more analyses and make recommendations on the conditions of use for each. (JEL Classifications: C10, C12, C13, C59, C90)

Evaluating observed wine-tasting results as a mixture distribution, using linear regression on a transformation of observed results, has been described in the wine-tasting literature. This article advances the use of mixture models by considering that existing work, examining five analyses of ranking and mixture model applications to non-wine food tastings and then deriving a mixture model with specific application to observed wine-tasting results. The mixture model is specified with Plackett-Luce probability mass functions, solved with the expectation maximization algorithm that is standard in the literature, tested on a hypothetical set of wine ranks, tested with a random-ranking Monte Carlo simulation, and then employed to evaluate the results of a blind tasting of Pinot Gris by experienced tasters. The test on a hypothetical set of wine ranks shows that a mixture model is an accurate predictor of observed rank densities. The Monte Carlo simulation yields confirmatory results and an estimate of potential Type I errors (the probability that tasters appear to agree although ranks are actually random). Application of the mixture model to the tasting of Pinot Gris, with over a 95% level of confidence based on the likelihood ratio and t statistics, shows that agreement among tasters exceeds the random expectation of illusory agreement. (JEL Classifications: A10, C10, C00, C12, D12)

We examine the degree of consensus in quality ratings of prominent U.S. wine publications. For the purposes of wine consumption and research, are ratings on the ubiquitous 100-point scale reliable measures of quality? The value of expert judgment has been called into question by a number of studies, especially in the context of wine competitions and tasting events. Using data on 853 wines, we find a moderately high level of consensus, measured by the correlation coefficient, between most pairs of publications, similar to the level found by Ashton (2013). Rank and intraclass correlations are similar. Consensus is not found to be related to the blinding policies (or lack thereof) of the critical publications. (JEL Classifications: C93, D46)

We use the expectation-disconfirmation and categorization theories to study the effects of sensory versus nonsensory cues relative to a transformed attribute on categorization and typicality judgments relative to a new food product. In an experiment involving 51 participants and low-alcohol wines (new products), we show that categorization and typicality judgments differ according to sensory versus nonsensory cues. The new transformed product is categorized more often in its original category—wine—and perceived as more typical in the nonsensory compared to the sensory condition. (JEL Classifications: L66, M31)

This paper examines the ratings of 12 influential wine critics on the Bordeaux en primeur market over the vintages 2003–2012. We hypothesize that wine experts differ significantly in their rating approach and influence on prices. We find that European critics are less transparent and in general more severe in their scoring than their American counterparts. Experts also appear to reach a relatively strong consensus on overall wine quality but have more diverse opinions on wines that achieve a surprising level of quality given the vintage, the ranking, or the appellation from which they originate. Our evidence also suggests that Robert Parker and Jean-Marc Quarin are the most influential critics, as a 10% surprise in their scores leads to a price increase of around 7%. We further find that their impact is higher for appellations and estates that are not covered by the official 1855 classification and for the best vintages. (JEL Classifications: C60, G11, Q11)