Wine Rating Data

An analysis of my wine ratings from HADM 4300: Introduction to Wines.

Before the semester went fully online due to COVID, I had a lot of fun tasting wines in HADM 4300: Introduction to Wines. As part of the class, we tasted about six wines each week and recorded notes on them, including a 1-10 rating. Before leaving the class, I tasted and rated 32 wines from the US and France (they covered other regions later). After finding my notes from that semester, I thought it would be fun to do a bit of data analysis.

The big questions: White or red? France or USA?

Forwarding
Figure 1: Ratings by color

Routing
Figure 2: Ratings by country

I tended to prefer reds to whites and (ever so slightly) French wines to US wines. However, with such a small dataset, the differences are not statistically significant (t-test p-values of 0.12 and 0.97 for red/white and France/USA).

A closer look: Regions and varieties

Forwarding
Figure 3: Ratings by region

Routing
Figure 4: Ratings by dominant variety

Unsurprisingly, I liked Bordeaux reds (Cabernet Sauvignon over Merlot) and Loire whites. Perhaps somewhat more surprising is New York topping the average rating list, thanks to a really great Long Island Cabernet Franc and a pretty good Finger Lakes Riesling. Je suis vraiment désolé, Alsace, but I really didn’t like the funkiness of the Gewürztraminer and Muscat we tried. My favorite wine was a Loire Chenin Blanc, the only 9.5 I gave.

Other factors: Price and vintage

Forwarding
Figure 5: Rating vs price

Routing
Figure 6: Ratings by year

I don’t seem to have any particular preference for year or be influenced much by price (I gave all ratings price-blind). The Pearson correlation between price and rating is 0.19 (p=0.29). However, the two most expensive wines we tasted were both fantastic: Château Langoa Barton and Château Pédesclaux.

Conclusions

  • Try more Chenin Blanc
  • When in doubt, go for a Bordeaux Cabernet Sauvignon
  • Need more data!

Raw data

In decending rating order:

Name Rating Year Region Dominant Variety Color Price
Marc Brédif Classic 9.5 2018 Loire Chenin Blanc white $22
Lieb Cellars Estate 9.0 2018 New York Cabernet Franc red $25
Château Langoa Barton 9.0 2015 Bordeaux Cabernet Sauvignon red $80
Château Pédesclaux 9.0 2015 Bordeaux Cabernet Sauvignon red $70
Vidal-Fleury 8.5 2015 Rhône Marsanne white $35
Château de Sancerre 8.5 2017 Loire Sauvignon Blanc white $27
Château Graville-Lacoste 8.5 2018 Bordeaux Sauvignon Blanc white $19
André Brunel Côtes du Rhône 8.5 2016 Rhône Grenache red $16
Picket Fence 8.5 2015 California Cabernet Sauvignon red $21
Barton & Guestier Saint-Émilion 8.0 2016 Bordeaux Merlot red $25
Cline Old Vine 8.0 2017 California Zinfandel red $10
Domaine Philippe & Vincent Jaboulet 8.0 2014 Rhône Syrah red $30
Gramercy Cellars Lower East 8.0 2016 Washington Syrah red $25
Frei Brothers Sonoma Reserve 8.0 2017 California Chardonnay white $17
Maison Champy Cuvée Edme 8.0 2016 Burgundy Chardonnay white $21
Hugel Classic 8.0 2016 Alsace Pinot Gris white $21
Patricia Green Cellars Reserve 8.0 2018 Oregon Pinot Noir red $29
Louis Jadot Couvent des Jacobins 7.5 2015 Burgundy Pinot Noir red $25
Pieuré St.-Flaurent Réserve 7.5 2018 Bordeaux Merlot red $11
Louis Jadot Chablis 7.5 2018 Burgundy Chardonnay white $25
Columbia Winery 7.5 2016 Washington Merlot red $14
Chateau LaFayette Reneau Dry 7.5 2018 New York Riesling white $13
Clos de la Sénaigerie Sur Lie 7.5 2018 Loire Muscadet white $12
Château de Myrat 7.0 2015 Bordeaux Sémillon white $45
Margerum M5 White 7.0 2017 California Grenache Blanc white $22
Charles Joguet 7.0 2018 Loire Cabernet Franc rose $22
Tarrica Wine Cellars 7.0 2017 California Pinot Noir red $14
Sterling Vintner’s Collection 6.0 2017 California Sauvignon Blanc white $12
Rainstorm 6.0 2017 Oregon Pinot Gris white $13
Domaine de Fa Roche Guillon 6.0 2015 Rhône Gamay red $35
Domaine Ostertag Fronholz 5.0 2016 Alsace Muscat white $28
Domaine Zind-Humbrecht 2.0 2018 Alsace Gewürztraminer white $27
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Kiran Tomlinson
PhD Student, Computer Science

I’m a Computer Science PhD student at Cornell University advised by Jon Kleinberg and interested in a blend of algorithms, data science, and machine learning.