Much research shows that the ratings that critics, judges, and consumers assign to wines are heteroscedastic. A rating observed is one draw from a latent distribution that is wine- and judge-specific. Estimating the shape of a rating’s distribution by minimizing a sum of cross entropies has been proposed and tested. This article proposes a method of improving the accuracy of that estimate by using information about the context of a wine competition or cross-section ratings data. Tests using the distributions implied by 90 blind triplicate ratings show that the sum of squared errors for the solution using context or cross-section information is 50% more accurate than not using such information and over 99% more accurate than ignoring the uncertainty about a rating.

A well-known method of studying iconic words is through the collection of subjective ratings. We collected such ratings regarding familiarity, iconicity, imagery/imageability, concreteness, sensory experience rating (SER), valence and arousal for Mandarin ABB words. This is a type of phrasal compound consisting of a prosaic syllable A and a reduplicated BB part, resulting in a vivid phrasal compound, for example, wù-mángmáng 雾茫茫 ‘completely foggy’. The correlations between the newly collected ABB ratings are contrasted with two other sets of prosaic word ratings, demonstrating that variables that characterize ABB words in an absolute sense may not play a distinctive role when contrasted with other types of words. Next, we provide another angle for looking at ABB words, by investigating to what degree rating data converges with corpus data. By far, the variable that characterizes ABB items consistently throughout these case studies is their high score for imageability, showing that they are indeed rightfully characterized as vivid. Methodologically, we show that it pays off to not take rating data at face value but to contrast it with other comparable datasets of a different phenomenon or data about the same phenomenon compiled in an ontologically different manner.

This is the story of the Princeton Wine Group, a group whose membership has been relatively constant for almost 40 years. This group has enjoyed 244 blind tastings involving 1,708 different wines. A statistical analysis was performed at each tasting examining whether participants ranked the quality of wines similarly and whether the preferences of the group were correlated with several variables including professional wine ratings and the prices of the wine. The article concludes with a discussion of lessons learned from a lifetime of wine tastings.

The impact of product ratings is significant in the experience goods market, whose intangible products are difficult to evaluate before consumption. Product ratings can reduce information asymmetries because they represent a credible signal of quality and thus positively affect product sales. In this study, we shed light on how professional critics behave by focusing on the influence of product characteristics and former reviews on rating behavior and market prices as additional quality signals in wine markets. Using 13,911 observations from professional wine tastings, we analyze 8,444 worldwide-produced wines and their ratings over 20 years. We find clear evidence to suggest that prices and product ratings are significantly related. Finally, the results suggest that review consistency evolves and determines current ratings.

Abstract domains of knowledge may have social origins. However, whether abstract concepts (ACs) may also differentially affect communicative interaction and conversation has not been explored. Here, we studied ACs’ communicative functions by collecting in an Italian and an English sample, ratings for concrete concept (CC) and ACs related to three main dimensions: communicative/pragmatic [i.e., Openness to Negotiation (ON), Easiness to Start a Conversation (ESC)], semantic/metacognitive [i.e., Social Metacognition (SM) – perceived need of others, Word Confidence (WC), Contextual Availability (CA)], and emotional–experiential (i.e., Pleasantness, Valence, Familiarity). Overall, Italian participants judged it was easier to start a conversation, the more pleasant, familiar, and positively valenced were rated the concepts. Crucially, at lower values of the emotional–experiential component (i.e., Familiarity in the Italian sample, also Pleasantness and Valence in an English sample), there was an advantage of ACs over CCs in the ESC. Moreover, in the Italian sample, participants rated ACs higher on SM, ON, and lower on WC and CA. Notably, in both the Italian and English sample, ACs with higher ratings on the ESC dimension belonged to the Self-Sociality subcluster. The results offer new insights into the pragmatic aspects linked to ACs’ use.

Analyses and aggregations of the ratings that wine critics and judges assign to wines are made difficult by stochastic error and biases that remain even when wines are assessed blind to price, label, capsule, and closure. Stochastic error is due to the partially random nature of ratings. Cognitive and omitted-variable biases are due to anchoring, expectation, serial position, commercial, and other factors. Differences in decanting, filtering, aeration, and temperature can also affect ratings.

Much research shows that the ratings that judges assign to the same wine are uncertain. And while the ratings may be independent, research also shows that they are not identically distributed. Thus, an acute difficulty in ratings-related research and in calculating consensus among judges is that each rating is one observation drawn from a latent distribution that is wine- and judge-specific. What can be deduced about the shape of a latent distribution from one observation? A simple maximum entropy estimator is proposed to describe the distribution of a rating observed. The estimator can express the implications of zero, one, a few blind replicates, and many observations. Several tests of the estimator show that results are consistent with the results of experiments with blind replicates and that results are more accurate than results based on observed ratings alone.

The outcome of the famous 1976 Judgment of Paris, a blind wine tasting of ten wines by nine French judges, brought American wines to the forefront of the wine business. A Californian wine, the 1973 Stag's Leap Wine Cellars S.L.V. Cabernet Sauvignon, was declared the winner, surpassing four highly prized French wines (Château Mouton-Rothschild 1970, Château Montrose 1970, Château Haut-Brion 1970, and Château Léoville Las Cases 1971). We collect ratings from experts for (almost) all vintages of the same ten wines over the years 1968–2021 and find that the Stag's Leap Cabernet Sauvignon is far from being first. We conclude that either the 1973 vintage was overrated by the experts who tasted it in 1976, or 1973 was merely an outlier in this winery.

The modern era of wine journalism has provided abundant information about wines and widespread use of numerical rating systems. A tiny difference, especially at the top of the distribution of ratings, may have striking consequences on wine sales and investment returns. This article provides a general framework to obtain a consensus among tasters’ opinions (reflected as numerical wine ratings) via three subsequent stages: normalization, approval, and aggregation. It is inspired by contributions in political science, social choice, game theory, and operations research. We apply it to the Judgment of Paris as well as to rank 2018 en-primeur Bordeaux wines, rated by five international experts. (JEL Classifications: C18, L15, L66)

Much literature shows that the ratings assigned by wine judges are uncertain, some authors have proposed that judges be tested, and a few wine competitions do test judges. However, no literature or competition has yet proposed a test or rating for judges based on realistic competition conditions. This article uses coefficients of multiple correlation to rate each of 54 judges who assigned ratings to 2,811 wines entered in a commercial competition. Results show that there is a strong and positive correlation between the ratings assigned by most judges to most wines. However, those correlations also show that the ratings assigned by approximately 10% of judges are indistinguishable from random assignments. Using correlations to rate the raters, a program is underway to monitor those judges and variations in competition protocol that may affect their ratings. (JEL Classifications: A10, C00, C10, C12, D12)