Monday, August 13, 2018

Drawing lines through a graph of points — what does this mean?

The short answer is: it depends on who is drawing the line, and why they are drawing it.

Some time ago I published a post about getting the question right when analyzing data. I pointed out that the question usually leads to the choice of an appropriate mathematical model, which we then use to answer that question. We fit the model to the data (or the other way around), and reach some conclusions from what the model tells us. So, asking the right question will usually tell us something useful.


However, we need to think about the purpose of the model. Are we actually trying to create some model that helps us understand our data, or are we just trying to draw some line through a graph of the data? This is the difference between explaining our data and summarizing it, respectively (see An introduction to data modeling, and why we do it). Here, I will draw some different lines through a couple of wine-related data sets, representing different models, to show you what I mean.

Bordeaux wine production

Consider the following graph, which is taken from the American Journal of Enology and Viticulture 51: 249-261, 2000. It shows the total production of the Bordeaux wine region (vertically) over 60 years (horizontally). Each point represents one vintage.

Polynomial model fit

The authors have added a polynomial line to their empirical data, to illustrate the trend in wine production. The line fits the data quite well, with 89% of the variation in the data being fitted by the model.

This model may well be adequate for the authors' purpose (in writing the paper). However, it cannot be a realistic model for the data. For example, the model suggests that production decreased during the first third of the 20th century — indeed, it implies that wine production in 1905 was the same as in 1995, which is not what happened (see the actual Bordeaux history as discussed by the Wine Cellar Insider).

So, this is simply a "line of best fit" to the data, used as a convenience. It cannot be used to discuss wine production outside the range of years shown on the graph. That is, the line does not model wine production, but merely summarizes the available data.

If we wanted to model the actual wine production, then we would need a model (and line) that shows a small increase in wine production from the mid-1700s until the mid-1900s (since that is what actually happened).

As an example, consider the following graph of the same data, to which I have added two straight lines. One line fits the data until the mid-1960s and the other fits the data from then onwards. This is called a piecewise model (ie. it consists of a series of straight lines).

Piecewise model fit

The two lines of this piecewise model happen to intersect in 1968, which turns out to be the last year in which Bordeaux had a poor vintage. This intersection may thus not be coincidence. Indeed, Pablo Almaraz (2015. Bordeaux wine quality and climate fluctuations during the last century: changing temperatures and changing industry. Climate Research 64: 187-199) suggests that production management in Bordeaux changed during the 1960s, under which circumstances this new model would have some realistic basis.

However, this piecewise also cannot be correct, because it suggests that there would be a continuing increase in production during the 2000s, and we know that this did not subsequently happen. A sigmoid model would be needed, instead.

To illustrate what I mean by this type of model, let's look at the wine production of a single château.

Single château wine yield

This next graph plots some data for an unnamed wine producer, although only Château Latour fits the description given in the American Economic Review, Papers and Proceedings 101: 142-146, 2011. This time, wine production is shown for the 1840s until the early 2000s.


We can see that wine production increased during the 160 years of data, and we could, if we so inclined, fit a straight line as a "best fit". However, this line would fit only 50% of the variation in the data.

A more realistic model would be one that suggests little change in production until the 1950s, and little change in production from the 1990s onwards. Such a model is shown as the thick line in this final graph. Such models are known as sigmoid (the lines are shaped like the letter S) — technically, this one is a logistic model.

Logistic model fit

The model indicates that the long-term average production from 1850-1950 was c. 17 hL/ha. Production then rapidly increased to 45 hL/ha by 1990 (ie. a 3-fold increase). The mid-point of the increase was between the 1967 and 1968 vintages. This model thus fits the conclusions from the piecewise model quite nicely.

However, this model is probably not entirely correct, because it implies that Bordeaux wine production was unchanged in prior centuries, when it probably increased somewhat, from the 1700s.

Discussion

There is a difference between fitting a line to the data (curve fitting) and trying to model the biology represented by the data. Both types of analysis fit an equation to a set of data, which is then visualized as fitting a line to a set of points on a graph. However, curve fitting focuses on finding the best-fitting equation, while modeling focuses on finding a model with a realistic biological interpretation.

Fitting a line is a mathematical procedure of convenience — it summarizes the data. However, the resulting equation may not have much direct biological relevance — the parameters of the model equation need to have a reasonable biological interpretation. In particular, a model should have relevance outside the range of the observed data — if the equation predicts values that are known to be incorrect, then it cannot be a good model for the biology, and nor can it be good if it predicts outrageous unknown values. A fitted curve is relevant only within the range of the data.

It is thus important to understand the purpose the author(s) had fitting the line, because this determines how we interpret the meaning.

Monday, August 6, 2018

Why not expand the 100-point scale?

Value judgments are usually presented on some sort of quantitative scale, with an upper limit of maybe 5 stars or 10 points, or even 100 points. In most cases, the maximum value represents the best quality that the evaluators expect to see.* This leads to a potential problem when someone or something achieves that quality. What happens next, now that we know the maximum can be achieved? What do we do when someone does even better?


For example, at the 1984 Winter Olympics the figure-skating pair of Jayne Torvill and Christopher Dean received maximum artistic-impression scores of 6.0 from each of the 12 judges, which had never happened before (for a single performance). Does this mean that no-one can ever do better? Not unexpectedly, the International Skating Union's International Judging System eventually replaced the previous 6.0 system (in 2004), so that scores no longer get near the maximum possible.

In a similar vein, it has been pointed out innumerable times that the top end of the 100-point wine-quality scale has become unnaturally crowded. This graph of the frequency distribution of some of Robert Parker's wines scores illustrates the issue (taken from my post Biases in wine quality scores). Here, the height of each vertical bar in the graph represents the proportion of wines receiving each score, as shown horizontally.


There is a distinct bump in the graph at a score of 100, indicating that more wines are being awarded this score than would be expected. This is precisely what happens when we reach the ceiling of any quality scale — there are lots of very good wines, and we cannot distinguish among them because we have to give them all the same score: 100.

We probably need to address this issue. Given the large subjective component in such ratings, there are only two general ways to go about this. We either:
  1. re-scale the 100-point scale, thus reducing the quality implication of the scores, so that "100-point wines" no longer get 100 points but instead get a wider range of lower points; or 
  2. go past the 100 limit, and start doling out scores that exceed 100 points.
This raises the question of whether the latter option has ever been chosen. Indeed, it has happened at least once that I know of (and there may be others).

In September 1998, Jancis Robinson posted on her web site a set of quality scores from a vertical tasting of the wines of Château d'Yquem (Notes from attending an Yquem vertical tasting).** The data are shown in the next graph, with the quality scores vertically and the wine vintages horizontally. The first two vintages were the "Thomas Jefferson wines" supplied by Hardy Rodenstock, and so their provenance is considered doubtful.

Jancis Robinson's wine-quallity scores for Château d'Yquem

The quality of the remaining wines is nominally scored on Robinson's usual 20-point scale. Note that three of the wines received a score of 20, while four of them were awarded scores that notably exceed 20 points (marked by the red line). Robinson made no comment about her unexpected scores, but she did use a series of superlatives in her tasting notes, the like of which we do not usually see from her pen (eg. "absolutely extraordinary").

Obviously, Robinson has her own personal quality scale, and what we are presumably being told here is that these wines exceed her usual expectations for a "20-point wine". It therefore seems to me that this is a prime example of option (2) presented above.

As such, the question does now rise as to whether this approach was actually necessary in this particular case. We might find a possible answer by looking at what other people have done when confronted with these same wines.

As one example, Per-Henrik Mansson published a set of quality scores for many of the same wines in the May 1999 issue of the Wine Spectator magazine (Three centuries of Château d'Yquem). He used a 100-point scale for his scores, so I have converted them to a 20-point scale for the comparison shown in the next graph (Mansson's relevant scores are in maroon).

Comparison of scores from Jancis Robinson and Per-Henrik Mansson

The correlation between the two sets of scores is 48%, which is slightly higher than we have come to expect from wine professionals (10-40%). However, Mansson never exceeded the nominal limit of his scale — of the 121 scores in his article, there are four 100-point scores, but none scored higher. Indeed, a comparison of the scores on the 20-point scale shows that Robinson's scores are generally 25% higher than Mansson's, across the board.

I think that we might therefore argue that Mansson has provided an example of option (1) presented above (ie. re-structuring the scale so that we don't bump our head against the score ceiling). Actually, Mansson provided nine scores that are <70 and 30 scores that are <80, so that he used a large part of the score range from 50-100 points (his lowest score is 55). This wide range of scores would be considered very unusual during the 20 years since he published his scores!

As a final note, there are only two vintages for which Robinson and Mansson strongly disagree — Robinson scored the 1931 vintage much higher than did Mansson, and he returned the favor with the 1971 vintage.



* This was not actually true at the undergraduate university I attended. The final (research) year of my science degree was assessed on a scale of 1-20. In this case, 20 points represented perfection, which could not be obtained in practice by anyone, let alone a student. Nor could a student get 18 or 19 points, although these might be obtained by a professional scientist. The best that might be expected for a student was 16 points, in which case the student was awarded the University Medal, which happened only occasionally. The top mark that might regularly be expected (ie. every year) was 14 points. At the other end, 0 points was a fail at the Honours year, which meant that the student would get a Pass award, instead.

** Thanks to Bob Henry for providing a copy of the blog post.

Monday, July 30, 2018

What are Australia's most collected wines?

It is sometimes a topic of discussion as to which wines are most likely to be represented in cellar collections. I am not referring necessarily to expensive wines or to cult wines, but to wines that are actually in the cellars of real consumers and collectors (not necessarily investors). Which wines do people actually put aside for drinking (or selling) later?

One could look at the data of a collaborative site like CellarTracker, which lists the cellar collections (number of bottles) of many of their community members. However, this would be a slow business, as the data are not readily available in bulk.


One alternative is to look at the data from commercial storage facilities, given that many people use them when they have a serious size of wine collection. Once again, this would be tricky in a country the size of the USA, where there are many such facilities, large and small scattered across the country.

However, in Australia there is one main company, Wine Ark, which makes such an analysis possible. Indeed, Wine Ark actually does the work for us, by releasing every few years a list of Australia’s 50 Most Collected Wines. Wine Ark was established in 1999, and stores more than two million bottles of wine in 16 cellars across Australia (for clients from more than 30 countries). So, their lists should provide a good overview of what is happening with regard to collections of Australian wine.

I have compiled the data from the four lists published to date (2006, 2009, 2013, 2016), in which the wines are simply ranked in order of the number of bottles in storage. From this, I have constructed a network of the 53 wines that appear in most of the lists.

Australia's most collected wines

The wines at the top of the Ark lists are at the top of the network, progressing down to lower ranked wines at the bottom. The network gets messy towards the bottom because the lower ranked wines can vary a lot in position from list to list — indeed, the lists have changed quite a lot across the decade over which they were compiled.

Nevertheless, there is a group of 14 wines that are at the top of every list, and I guess that we should call these the "most collected" wines. These are all widely available, as their production is relatively large. Notably, 6 of them come from Penfolds, which specifically targets this segment of the wine market. The Penfolds and Wynns companies are both owned by Treasury Wine Estates, giving this conglomerate 8 of the top 14 wines. This may justify their claim to be Australia's premier wine company.

Interestingly, while all of the listed wines are relatively expensive, Australia's most expensive wines (eg. Grange and Hill of Grace) are not necessarily at the top of the list. Indeed, Penfolds Bin 389 Cabernet Shiraz actually topped two of the lists — this is often recognized as the most collected wine in Australia, as it has the same cellaring potential as Pengfolds Grange but costs only one-tenth of the price.


The listed wine with the biggest production is Wynns Coonawarra Estate Cabernet Sauvignon (often called Black Label), which I have written about before (Why lionize winemakers but not viticulturists?). At the other extreme, Australia's cult wines are produced by more low-profile winemakers, such as Chris Ringland, Drew Noon, and Phillip Jones, whose productions are too small to appear in the Ark lists.

It is also worth noting that 10 of the 53 wines (20%) in the network are white, rather than red, including 2 of the top 14. It is debatable whether this is a surprisingly large or disappointingly small percentage of the collectable wines; but it includes 5 chardonnays, 3 rieslings, and 2 semillons (one of them a botrytized version).

Finally, we can compare the network to Langton's Classification of Australian Wine. Langton's Fine Wine Auctions holds more than 250 auctions every year, and their Classification is based on the sale prices of the auctioned wines. The current Classification is from 2014, with a new one due in September this year (the 30th anniversary).


The differences between the two lists are quite revealing. All of the top 14 Ark wines are in the Langton's Classification, although only 8 are listed as Exceptional, with 2 Outstanding and 4 Excellent. Conversely, there are 3 Classification wines that are not in the Ark cellaring list: Bass Phillip Reserve Pinot Noir, Chris Ringland Dry Grown Barossa Ranges Shiraz, and Jim Barry The Armagh Shiraz — these are pretty much cult wines (ie. low production).

Of the remaining 39 Ark wines, 8 are classified as Exceptional, 11 as Outstanding, and 15 are Excellent. That leaves 5 wines in the Ark list that are not in the Classification at all: Jacobs Creek St Hugo Cabernet Sauvignon, Tyrrell's Vat 9 Shiraz, Seppelt Chalambar Shiraz, Howard Park Cabernet Merlot, and Rockford Rifle Range Cabernet Sauvignon.

There are also 29 Outstanding Classification wines that do not appear in the Ark lists. Perhaps the most unexpected of these is the Penfolds Bin 144 Yattarna Chardonnay, which was specifically created by Penfolds as the white equivalent of their red Grange (ie. a cellaring wine — "the result of one of the most comprehensive and highly publicized wine development programs ever conducted in Australia"). Apparently, Penfolds have convinced the auction market (Langtons) but not yet the cellaring public (Wine Ark).

These large differences between the two lists presumably reflect the different attitudes of people who are cellaring wines and those who are selling them at auction. Do you want mature wine to enjoy in your lifetime, or are you treating them as an investment? This may result in you choosing different wines for your storage.

Monday, July 23, 2018

Calculating value for money wines

The prices of wines rarely seem to go down. Indeed, at the top end of the market, they seem to go up rather alarmingly. It has often been noted that the Baby Boomer generation has been willing to pay much more for good wines than the Generation X and Millenial generations are currently doing. This means that the latter groups are looking for wines that are seriously good value for money.

I have previously provided a summary of various quantitative methods for assessing value for money (ie. in addition to personal judgment): Quantifying value-for-money wines - part 1, part 2, part 3 and part 4. The fact that there are seven different methods discussed in that blog series tells you just how seriously people have taken this issue. In all cases, the crucial relationship is between the quality score and the price (QPR) — for any given wine quality we need to estimate the price that is considered to be good value for money.


I thought that it might be interesting to present a real example of one way in which this value can be obtained. Indeed, it is the one that I use myself.

How I do it

Needless to say, in practice I use the method that works best for me. It is described in the post Quantifying value-for-money wines, part 2, although the most detailed discussion is in The relationship of wine quality to price. For it, I need a data set consisting of as many wines as possible, for each of which I have both a quality score and the price — the more wines the better.

I have previously noted that, due to the economics of how the government-owned liquor chain Systembolaget operates, Sweden has mid- and high-quality wines at a cheap price, but has no discount wines (Why is wine often cheaper in Sweden than elsewhere?). This means that I need to optimize the QPR — I can't just go down to a store and see what wines are on special, because there are no such wines. I have also noted the way in which new wines become available (Wine monopolies, and the availability of wine), and that the most interesting wines appear in Systembolaget's "Small quantities" assortment (små partier), which is where I focus my attention.

The wine-quality scores that I use come from Jack Jakobsson, who regularly does the Systembolaget wine tastings for BKWine Magazine. He produces a monthly report of all of the new-release wines that he has been able to taste (some wines are in too small a quantity to be made available to the media). He uses a 20-point scale, including half-points. His scores during 2017 ranged from 11 to 18.5 (see the graph at the bottom of the post). Wines that would score higher may exist, but they are not available for media tastings.

I standardize the prices to those for 750ml bottles of table wine (red, rosé, white, sparkling) — that is, excluding fortified wines, which tend to exist on a different price scale, and also other-sized bottles (halves, magnums, etc).

This first graph shows the scores for the 1,691 "Small quantities" wines that Jakobsson tasted during 2017, with the single-bottle price shown vertically and the quality score shown horizontally (each point represents one wine). You can convert the Swedish crown (krona) into US$ by dividing by c. 9 (eg. 175 kronor ≈ $20).

Wine prices in Sweden during 2017

What I need to do now is derive the QPR relationship for these wines. That is, I need to calculate the "expected" or "average" price-quality relationship. I do this by fitting some mathematical model to the data (as explained in An introduction to data modeling, and why we do it). I have to do this only once, so it it no big deal.

As I have discussed before (eg. The relationship of wine quality to price), an exponential model is usually the best fit to economic data, and this is shown in the next graph. Note that the vertical axis is logarithmic, which means that the model can be represented by a straight line. The fit of the data to the model is quite good (60%), especially compared to some other price-related data sets that I have looked at.

Fitting the exponential model to the wine data

The line on the graph may look like it is a bit too low, but that is only because there is a mass of points in the lower part of the graph — half of the points really are above and half below the line.

Since the fit of the model and data is quite good, we can now proceed to identify the value-for-money wines. [If the fit is poor, then the exercise would become pointless!] The next graph shows three dashed lines, representing three different QPR criteria.

Identifying the value for money wines

The wines below the pink line are the best 5% in terms of value for money, while those below the blue line are the best 10% — these are the wines we should think about purchasing if we want to get the most for our money. The wines above the black dashed line are the worst 5% in terms of value for money — these are the rip-off wines, because I can get the same wine quality for a lot less money.

You will note that the best bargains are usually in the 15-16.5 points range (which is approx. 88-91 points on the 100-point scale). This is a very nice quality range if you happen to like good wines — there is no need for me to pay more than the equivalent of US$25 for a "90-point wine".

The practical result of this analysis is that I now have a separate price noted for each quality score, which I can use to assess the value for money of any new wines that are released — new wines that are selling for less than that price are good value for money. For example, I always take a close look at any new wines that are below the prices represented by the pink line on the graph.

Conclusion

The procedure outlined here possibly looks cumbersome, but it is quite straightforward for anyone used to a bit of quantitative analysis. It works well, in practice; and it could be applied to a compiled set of scores for any set of wines.

Jakobsson's scores

Finally, in accepting to use the scores provided by Jack Jakobsson, it is of interest to look at whether there are any biases in his scores, such as I have discussed in previous blog posts (eg. Biases in wine quality scores). The next graph compares the frequency distribution of Jakobsson's scores (in blue) with that expected from unbiased scores (in maroon).

There are few biases in the quality data

Interestingly, his scores are remarkably unbiased, compared to the situation discussed in my previous posts for other collections of wine scores. There is a slight under-representation of 15.5 compared to a score of 15 or 16, along with a small over-use of 14 and 14.5 compared to lower scores, but that is about all. Perhaps this is a result of using a 20-point scale, where there is no temptation to over-use scores like 90 on the 100-point scale.

Jakobsson also helpfully provides an indication of the likely best period during which to enjoy each of the wines. This is a topic that I will return to in a future post. On the flip-side of the coin, the most obvious downside to his reviews is his apparent disdain for rosé wines — they rarely get good scores, even when they taste pretty good to me.

Monday, July 16, 2018

Differing opinions of amateurs at the same wine tasting

I have previously written about a direct comparison of two professional wine writers tasting the same wines at the same time (Laube versus Suckling — their scores differ, but what does that mean for us?). This begs the question of what happens at wine tastings attended by several people, especially if they are interested amateurs rather than wine professionals. This is a trickier question to address, because of the paucity of published data. However, I will look at one possible dataset here.


On 20 January 2014, at the Ripple Restaurant, in Washington DC, there was a vertical tasting of wines from Château Calon Ségur (located in Bordeaux), for 16 vintages from 1982-2010. This was attended by a number of people, three of whom have put their quality score for each wine online:
  Panos Kakaviatos (Calon Segur 1982-2010: first ever promotional tasting in the US)
  Aaron Nix-Gomez (The Calon Segur vertical 2010-1982)
  Kevin Shin (dcwino on CellarTracker)
We can try to compare these scores.

Furthermore, we can also try to compare these scores to those from a professional wine writer. On 6 November 2013, there was another tasting of many of the same wines, at the Carré des Feuillants restaurant, in Paris. From this tasting, Jane Anson has also put her quality score for each wine online (Chateau Calon Ségur: retrospective 1995-2011). These can be added to our comparison, given that the tastings were only a few weeks apart.

As I have done in previous blog posts (eg. How large is between-critic variation in quality scores?), we can quantify the relationships among the scores using correlation analysis between pairs of tasters. This measures the percentage of the data variation that is shared in common between those tasters — the larger the percentage then the better the agreement there is among the quality scores of the wines tasted. This is shown in the table for all six possible pairs.

Correlations among the wine tasters

There is quite a reasonable degree of agreement among the three wine-interested amateurs, especially Kevin Shin and Aaron Nix-Gomez. Indeed, these percentages are higher than the level I observed among the professional critics in the post cited above (often only 10-40%). Perhaps amateurs are less determinedly different from each other than are professionals?

Notably, the comparisons between these amateurs and the professional (Jane Anson) show much lower agreement. This may reflect the bottles opened at the two different tastings; but the values are certainly in accord with those found among other professionals.

It might be also be useful to look at a picture of these data, rather than a table of numbers. To do this, we can employ a network, as I used in the post on professionals cited above. This is shown in the graph.

Network of the shared wine-quality scores

The important point for interpreting the graph is that the length of the lines represents how similar are the wine-quality scores. The lines in the center represent the shared similarity among the scores, while the lines leading to the names represent the variation in the scores that is unique to that person.

What this network says is that very little of the variation in quality scores is agreed upon by the four people, and that they each have their own personal opinion, which differs notably. In this case, there is little consensus on the quality of the different vintages.

So, amateurs may be somewhat different from professionals, but they still go their own way. Wine quality is apparently not a shared experience.

Monday, July 9, 2018

The ups and downs of wine-blog posting

A couple of weeks ago I wrote a post about How long can wine bloggers keep it up?. At the time, I mentioned that I recorded the number of posts per month for all of the Australian wine-related blogs that I could locate. This allows me to look at changes in the rate of blog posting throughout the life of each blog. In this new post, I will show you some of the more obvious patterns. I will use individual blogs as my examples, but I will group them into sets with similar patterns of posts — what types of wine blogs are there?


The individual qualities of wine blogs have long interested people. For example, back in 2013, Lettie Teague searched for Five wine blogs I really click with. She searched "not just a handful of blogs here and there but hundreds and hundreds of wine blogs from all over the world." However, the fate of blogs is almost always the same — only one of her chosen blogs has posted since the middle of 2017. Unusually, one of the bloggers did actually put up a "good-bye" post (Brooklynguy's Wine and Food Blog).

In my previous post on the subject, I illustrated the coming and going of the Australian wine blogs from the beginning of 2006 until May 2018 (150 months). In all of the graphs shown here, "Time 0" is the time of the first blog post for each blog, so that the graphs illustrate what happened to the blogs through their lifetime. I have excluded the three most prolific blogs, which all started long before 2006 (these would fit into the last two graphs below).

The first graph simply shows the number of blogs (in pink), illustrating that the number of blogs decreases through time (ie. many blogs last a short time and only a few make it for a long lifetime). For the cognoscenti, this is called a Type I survivorship curve (note the logarithmic vertical axis).

Number of Australian wine blogs and their posts

The blue line shows the average number of monthly posts for those blogs still surviving at any one time. The average remains steady at 4-5 posts per month for c. 5 years, by which time the number of blogs has halved. Thereafter, the average becomes much more variable, depending on which blogs are still going. The longest-lived blogs keep up a high average monthly number of posts (eg. >10 years = >10 monthly posts) — if the blogger is still going after 6 years, then they really have something to say!

We can now look at the individual blogs, looking not at how long they last but at what happened along the way. The blogs are arranged in groups, although there is nothing definitive about the following groupings. They are merely examples of patterns that appear in the data. Not all of the blogs are actually shown here.

The next graph shows a few blogs that burst out the blocks with a flurry of activity but then slowed down over the first year, followed by a slower stream of activity.

Australian wine blog postings

The next group of blogs did the opposite, starting relatively slowly but followed by a burst of activity later on. This burst could take up to 2 years to kick in. In all cases the burst was not sustained by the blogger.

Australian wine blog postings

For the next group, each blog shows a series of episodes of bigger activity, rather than a single burst. These bursts usually represent different topics of interest to the writer; for example, reporting on travels to wine regions. It is easy to see these blogs as extensions of those in the previous graph — some bloggers get a second or third wind, but some do not.

Australian wine blog postings

We now move on to a group of blogs that all have regularly had a relatively high number of posts (eg. >3 per week). Some of these bloggers decreased their activity after an initial burst, but they still maintained their prolific rate of posting. For example, at one point Full Pour simply halved the number of posts from one month to the next, but then continued at the new rate.


Australian wine blog postings


The Intrepid Wino was the most erratically posting blogger I encountered — on some occasions wine-tasting notes were uploaded in bulk, with a maximum of 171 posts in one month (off the top of the graph) — the nearest competitor was Wine Will Eat Itself, with a maximum of 98 (see below).

We now move on to those blogs that have consisted mostly of wine-tasting notes. Obviously, these notes are relatively short, and so there can be a lot of posts in any given month — here, we are talking of up to 1 per day, or even more. However, you will note that the bloggers illustrated in this next graph all decreased their activity after an initial burst.

Australian wine blog postings

The final graph shows those blogs consisting mostly of wine-tasting notes but where the number of posts increased dramatically at a particular time. You can all guess what that time was — the blogger started receiving large numbers of wine samples, for free, rather than basing their comments on their own drinking habits or on group tastings. The most blatant example is Wine Will Eat Itself — sadly, here the prolific activity was stopped by the death of the blogger.

Australian wine blog postings

This sort of activity by wine writers has long been questioned. For example, David Shaw wrote a pair of articles for the Los Angeles Times way back in August 1987 (Wine writers: squeezing the grape for news, and Wine critics: influence of writers can be heady), revealing what was then presumably unknown to much of the reading public — many if not most newspaper and magazine wine writers were paid very little money, and relied on wine producers and marketers in a way that could easily be seen as a conflict of interest.

The main issue, of course, is that the writers usually prefer to write favorable reviews, and therefore simply ignore all wines that they view unfavorably. This means that some of the Australian wine blogs simply catalog (mostly) Australia's wines, one bottle at a time, but actually ignoring most of them. This may not be of much help to the reader, who is not being warned about what to avoid.

This also produces an uncritical view of the world. We all know what a 5-star review says before we read it (as we also do for a 1-star review), so why read it? These reviews provide an unrelenting tone, which ultimately becomes tedious. The real interest lies in the 2- and 3-star reviews, because something went wrong, and we need to assess whether it would also be a deal-breaker for us. Wine bloggers, please take note.

Monday, July 2, 2018

An introduction to data modeling, and why we do it

Among all of the current hype about quantitative data analysis (eg. The arms race for quants comes to the world’s biggest asset managers), especially with regard to what are called Big Data, I have noted a few negative comments about the idea of modeling data. Not unexpectedly, non-experts are often wary of things beyond their own expertise. (I know I am!) So, I thought that I might write a post outlining just what people are trying to do when they do the sorts of things that I normally do in this blog.

Background

If the world is a non-random place, then it is likely that we can find at least a few patterns in it that we can describe and explain in a simple way. We refer to this process of description and explanation as modeling. In this process, we try to find simple models that can be used for both describing and explaining the world around us; and, if we get it right, then these models can be used for forecasting (prediction), as well.

This is not data modeling

The main issue is that life cannot be entirely predictable — there are predictable components and unpredictable components. For example, we all know that we have a limited life-span, although we do not know where or when we will depart. Nevertheless, there is a measured average life-span (which is the predictable component), along with variation around that average (the unpredictable component). We are thus all thinking that we might live for 80-85 years, and we can plan a future for ourselves on that basis.

Think of it this way: the predictable component gives us optimism, because we can make plans, while the unpredictable component makes our plans go astray.* Models are our formal, mathematical way of trying to identify the two components. The idea is to find out whether the predictable part dominates, or not. If it does, then forecasting is a viable activity for us all.

Another way of thinking about this is the classic question as to whether the glass of water is half full or half empty. The full part is the predictable component, and the empty part is the unpredictable component. Of course, the glass is both half full and half empty; and we should actually be interested in both components — why is it half full, and why is it half empty? Each will tell us something that might be of interest.

Modeling

So, models try to formalize this basic idea mathematically. If we have some quantitative data, then we can try to find an equation that tells us about the predictable component of the data, and about how much the real data deviate from the model in some (possibly unpredictable) way. For example, we anticipate that each person's life-span is not random, and we can thus model it by assuming that it deviates in some unpredictable way from the (predictable) average lifespan. Similarly, tomorrow's weather is not random, but instead it deviates from today's weather in more or less unpredictable ways.

To get a picture of what is happening, we often draw a graph. For example, our data might be shown as a series of points, and we can then fit a line to these data. This line represents the model, and the closeness of the points to the line tells us how well our model fits the data. The line is the predictable component, and the deviation of the points from the line represents the unpredictable component.

A couple of examples

Here are two wine-related examples, of a type of modeling that I have used in previous blog posts. In both cases, I will be fitting an Exponential Model to a set of data, as this seems to be the simplest model that fits these data sets well (see the discussion at the end of the post).

The first set of data come from the EuroStat database. It lists the average size of a vineyard holding for the 18 European Union countries with the most vineyard area. Each point in the first graph represents a single country, with the countries ranked in decreasing order horizontally, and the average vineyard size shown vertically.

Average vineyard holdings in the European Union

The line represents our model (the Exponential). Note that the vertical axis is on a logarithmic scale, which means that our model will form a straight line on the graph. Also, the model fits 97% of the data, which means that our model fits very well (see the discussion later in the post).

Using our glass metaphor, the graph shows us that the glass is almost full for all of the countries — the predictable component of the data is by far the largest (ie. the points are close to the line). However, for France our glass is not at all full, and there is a large unpredictable component (the point is not particularly near the line). Both of these conclusions should interest us, when studying the data. We should be happy that such a simple model allows us to describe, explain and forecast data about vineyard sizes across Europe; and we should wonder about the explanation for the obviously different situation in France.

The second example is very similar to the first one. This time, the data set comes from the AAWE. It lists the average 2015 dollar value of wine exports for 23 countries. As above, each point in the graph represents a single country, with the countries ranked in decreasing order horizontally, and the export value shown vertically.

Wine export values per country

Everything said for the first example applies here, as well, except that this time the country with the greatest deviation from the model is the lowest-ranked one. We might ask ourselves: Is it important that Romanian wine exports do not fit? We do not know; but the model makes it clear that we might find something interesting if we look into it. This is the point of modeling — it tells us which bits of the data fit and which bits don't; and either of these things could turn out to be interesting.

Models

There is an old adage that models should be relatively simple, because otherwise we lose generality. Indeed, this idea goes back to Aristotle, although William of Ockham is usually given the most credit (ie. Occam's razor). So, simpler is better. The alternative is called "over-fitting" the data, which is bad.

We could try to model things exactly — for example, we could think in detail about the things that cause weather to vary from today's or people's lives to deviate from the average. However, it should be obvious that this would be unproductive, because there are simply too many possibilities. So, we try to use models that are as simple and general as possible.

The main practical issue is that there are lots of mathematical models, which differ from each other in oodles of way, and many of them might fit our data equally well. This sometimes leads to unnecessary arguments among the experts.

However, we do have various ways of helping us measure how well our data fit any given model. In my case, I usually provide the percentage fit, as shown in the above two examples. This is sometimes described as the amount of the data that is "explained" by the model, although it is better to think of it as the amount of the data that is "described" by the model. Either way, it represents the percentage of the data that the model claims is predictable, with the remainder being unpredictable by the model.

We would, of course, do well to pick a model that provides as high a fit as possible. However, the best-fitting model might actually be over-fitting the data. To guard against this, we should also have some reasonable explanation for why we think that our chosen model is suitable for the data at hand.


Simply trying an arbitrary range of models, and then choosing the best-fitting one, is almost guaranteed to over-fit the data. At the other extreme, simply fitting a straight line to your graph can also be a very poor procedure — and I will discuss this in a later post.



* Robert Balzer: "Life is what happens to you ... when you are planning other things."
[Quote provided by Bob Henry.]

Monday, June 25, 2018

How long can wine bloggers keep it up?

It was back in 2012 that Andrew Jefford presented a keynote address to a bloggers conference entitled "The Death of the Wine Writer". There has been a lot of discussion of this topic since then, but very little actual data demonstrating any such thing, at least as far as wine blogs are concerned.

I once presented a post about The rise and fall of wine blogs, and other things, using data on the frequency of Google searches. However, this was not targeted specifically at individual blogs, because there were not enough data for most of them. This time, I intend being more specific. I have been told that "it’s a wine blog wasteland on the internet, with countless blogs that haven’t been touched for years" (Wine Turtle), but to what extent is this really true?


Back in 2013, it was reported that "there are about 1,450 wine blogs today, of which about 1,000 are nonprofessional endeavors", and that "only 18% of bloggers have been blogging for more than six years." This is a lot of blogs to check, so I need to subsample them.

There are only three places I could write about with any confidence. Of these, the USA has far too many wine blogs to study (probably up to 1,000, with c. 350 people attending the annual Wine Bloggers Conference), while Sweden has far too few (less than a dozen active). That leaves Australia (with more than a dozen extant), whose wine blogs I will discuss here. There seems to be no reason to assume that the situation for wine blogging in Australia is different to anywhere else, other than in the actual number of blogs.

There have been a few commentaries discussing the fate of Australian wine blogs, as there have been elsewhere. For example, in January 2015 Anthony Madigan posted in The Week That Was (a newsletter from Australia's Wine Business Magazine) the question: "Whatever happened to all the wine bloggers out there?" Andrew Graham, from the Australian Wine Review blog, responded that it wasn't that bad (Where have all the Australian wine bloggers gone?); and he updated his comments in his 10th anniversary review earlier this year.

Some data

To check the current situation, I have tried to compile a list of those wine blogs based in Australia that have been active at some time during the past 10 years, excluding blogs consisting mostly of industry news and announcements, or wine sales. My list is likely to be comprehensive but not exhaustive — I have checked every blog whose existence I heard of during my search, but I cannot exclude the possibility of missed blogs. In each case, I recorded the number of posts for each month of the blog's active period, up to and including May this year. [See the footnote.]

My final list has 63 blogs on it, with the total number of posts varying from 18 (from a 1-person blog) to more than 39,000 (from a 3-person blog). Of these blogs, 23 posted in May 2018, which is more than one-third of them.

Longevity of Australian wine blogs

The longevity of the blogs is shown in the first graph, with the horizontal axis counting the number of months from the first post to the last (inclusive), and the vertical axis counting the number of blogs. Three of the blogs are listed as ">149": The Real Review (167 months), The Wine Front (201 months), and Chris Shanahan (314 months). I have also plotted separately that subset of the blogs that have not yet posted in 2018 ("Finished blogs").

It is interesting to note the dip in the number of blogs in the "50-59 months" category, indicating that the blogs have tended to last either <4 years or >5 years (technically: a bimodal distribution). Of the no-longer-posting blogs, most of the blogs made it for 3-4 years' worth of posts, although some then died out at that time (30%). If the blogs did make it past that time, then they tended to die out after 5-6 years (30%). However, 40% of the blogs have lasted longer than this, which is double the estimate (18%) reported at the top of this post.

This seems to be the answer to the question posed in the post's title.

Starting and stopping points for Australian wine blogs

The second graph shows which years the blogs started (remember: these are only the wine-related blogs where something was posted during 2008 or later). Half of the blogs started during 2009-2011, with only 3 starting after 2013. Obviously, there was a "boom time" for wine blogging in Australia, which is now long gone.

The same graph shows the year in which the last blog post occurred, with 25 of the 63 blogs (40%) having posted in 2018. This is a lot more extant Australian wine blogs than I think people realize. They are listed at the bottom of this post.

However, it is rather difficult to know whether a wine blog is merely moribund or is actually dead. Only one of the bloggers explicitly noted that his blog was ending (and, sadly, he died shortly afterwards). Most of the other bloggers just stopped posting regularly, although in several cases the blog was actually taken offline (and I accessed it through the Internet Archive's Wayback Machine).

Monthly posts for two Australian wine blogs

It has been noted that often "bloggers allow weeks, months, even years to go by without posting a thought". As an example, the third graph shows two of the Australian blogs where posts kept appearing sporadically for several years after the regular blog posts stopped, indicating that they may not yet be dead, even now. So, baring the known decease of the blogger, or the blog itself being taken offline, we can't definitively say that any of the blogs are actually "finished".

The final graph shows the activity of the blogs, with the horizontal axis showing the average number of posts per month during the their lifetime (excluding the most prolific blog, with an average of 195 posts per month!). The most common activity numbers are once per fortnight, once per week, twice per week and three times per week, with half of the bloggers sticking to 4 posts or less per month. This rate of posting indicates that the posts are mainly about wine-related topics, rather than being short reports of wines tasted.

Average number of posts per month for Australian wine blogs

However, the seven most prolific blogs in the graph (having averaged >20 posts per month) do consist mostly of wine reviews. In order, they are: Wine Will Eat Itself, Qwine, Tyson Stelzer, Australian Wine Journal, Australian Wine Review, Wino Sapien, and Grape Observer; and to this list we can add The Wine Front, with its massive average of 6.5 posts per day (from three people). I might write about these two distinct types of wine blogs in a future post.

Conclusion

It seems that 25 of the 63 wine-related blogs that have been active in Australia during the past decade have posted sometime during 2018, which is not an insignificant number (see the list at the bottom of the post). Nevertheless, Anthony Madigan's January 2015 comment was spot on. During the previous 5 years, 19 blogs had stopped posting (32% of those in existence at the time), and no new ones had been started during the previous year. He was quite right to ask his question at that particular moment. [Mind you, Madigan's own blog (Country Wine) has the smallest average number of posts per month (0.4), and he has not posted since May last year.]


So, irrespective of any perceived image problem, it seems that announcements of the death of wine blogging are greatly exaggerated, at least in Australia.



Compiling the data for this post was relatively straightforward for the blogs hosted by Blogger, because the default setup is to have a post archive actually listing the number of posts for each month. However, Wordpress blogs have no such default. In fact, only one Wordpress blog provided the information in any easy-to-access manner. In every other case, I had to count the posts manually. So, you Wordpress bloggers — I hate you all!

Only three blogs defeated my attempts to count their posts manually, all produced by wine critics: The Wine Front (39,000 posts since 16 September 2001), The Real Review (2,700 posts since 1 July 2004), and Tyson Stelzer (2,600 posts since 1 April 2010).



Wine-related blogs from Australia that have posted at least once so far during 2018:

Australian Wine Review
Australian Wine Reviews - and Beyond
Best Wines Under $20
Chris Shanahan
Drinkster
Grape Observer
Happy Wine Woman
The Inquisitive Palate
The Intrepid Wino
More Red Sir!
People of Wine
Que Syrah
Qwine
The Real Review
The Tasting Glass
Travelling Corkscrew
Tyson Stelzer
Vino Notebook
The Vinsomniac
The Wine Front
The Wine Wankers
Winemusing (formerly The Wine Muse)
Wino Sapien
Winsor Dobbin Wine of the Week
Winsor's Choice

Note that this list excludes blogs that consist mostly of industry news and announcements, or wine sales, as well as personal web pages without a distinct blog component.

Monday, June 18, 2018

Not all retailers cherry-pick the best critic scores for their wines

I noted in an earlier post that wine retailers often cherry-pick the critics' quality scores that they quote when advertising their wines (Laube versus Suckling — their scores differ, but what does that mean for us?). They can do this because the quality scores from different critics often do not agree, and there is a pecuniary advantage to the retailer for any given wine to appear to be as good as it can be.


However, not all wine advertising is like this. For example, Wine Rack Media, a company offering services to online wine retailers, makes available the scores (for any given wine) from a range of critics, and these scores are sometimes quoted in full by retailers. For example, both Fisher's Discount Liquor Barn and Incredible Wine Store have web pages "powered by Wine Rack Media", and they both use that company's wine database.

Let's take a specific example of a well-known wine, to see what information we get: Caymus 'Special Selection' Cabernet Sauvignon. Checking online, we are given wine descriptions (including quality scores) for most of the vintages from 1978 to 2005, many of them from multiple sources: Wine Spectator, Wine Enthusiast, Wine and Spirits, Stephen Tanzer, Connoisseurs' Guide to California Wine, and Vintage Tastings.

What is more interesting, though, is that we are often given multiple scores from each of these sources, rather than a single score. Most reputable sources of wine-quality scores are likely to have tasted the wines on different occasions, often by different tasters, so that we do have multiple scores available. Most wine retailers report only the highest of these scores, for each wine (ie. a cherry-picked score); but not in this example.

The first graph shows all of the quality scores we are given from the Wine Spectator magazine, with the vintages listed horizontally and the scores vertically. Where there are multiple scores for a particular vintage, I have connected them with a line.

Wine Spectator quality scores for Caymus 'Special Selection' Cabernet Sauvignon

As you can see, the repeated scores tend to differ from each other by at least a couple of points, although one pair differs by 16 points (82 versus 98) and some others differ by 10 points and 9 points. Clearly, cherry-picking a score would be very effective here. Indeed, all of the scores below 90 points have a paired score that is much higher — we could, if we were so inclined, easily claim that the Caymus Cabernet is consistently better than a 90-point wine!

For comparison, the second graph shows some of the other quality scores, as well.

Several critics' quality scores for Caymus 'Special Selection' Cabernet Sauvignon

Note that the Tanzer scores are almost all lower than the other scores for the same vintage, while the Connoisseurs' Guide scores are usually lower. Indeed, the only vintages for which there is good agreement are 2000 and 2004 (where all of the scores are within 1 point).

With this sort of presentation of the quality scores we can thus see at a glance where we stand with regard to the variability among critics' scores. If only all wine retailing was this honest about the multitude of data available.

Monday, June 11, 2018

Actual wine consumption versus the recommended maximum

Many, if not most, countries have an "official" recommended maximum level of wine consumption per week for adults. However, the recommended value differs rather a lot between the countries. Moreover, the observed weekly consumption of wine also differs between countries. I have therefore wondered how these two values compare — the actual consumption versus the recommended maximum one.


For the recommended wine-intake values for each country, I have used the data from the International Alliance for Responsible Drinking (IARD), which were last updated in January 2018. The values given are in grams of alcohol per week (for each adult), with separate values for males and females, if they differ. We know that one 750 ml bottle of 12.5% wine equals 74 g alcohol, so we can use this to convert the values to bottles of wine per week.

For the actual wine-intake values for each country, I have used the Annual Per Capita Wine Consumption 2014-16 list from the AAWE facebook page. The values given are liters of wine per person per year. To convert these to "per adult", I have used the World Bank data for the percent of each population aged 15-64 years (this is apparently a standard age group for "adults"). I then converted these new values to bottles of wine per week.

This means that I now have two figures for bottles of wine per week per adult, one estimating actual consumption and one describing the recommended maximum, for a range of countries. Clearly, the value for actual consumption does not take into account what proportion of the population actually drinks wine.

The only complication is that the observed consumption values combine both males and females, whereas the recommendations for the sexes sometimes differ — where they do differ, the value for females is typically one-half or two-thirds of the value for males. Interestingly, there has been a recent trend for countries to lower the recommended limit for wine consumption by men to the same value used for women. Apparently, we are moving away from the James Bond / Humphrey Bogart hard-drinking lifestyle, at least as a medical recommendation for men (see my blog post James Bond, alcoholic).

Anyway, the data in the table show the actual consumption (bottles of wine per week per adult) as a percentage of the recommended limit for men. Most countries have a recommended limit for men of 1.9-2.3 bottles per week.

Croatia
France
Netherlands
Switzerland
Portugal
UK
Belgium-Lux.
Italy
Denmark
Austria
Australia
Sweden
New Zealand
Uruguay
Germany
Georgia
Chile
Greece
Bulgaria
Ireland
Argentina
Romania
Finland
Canada
USA
Spain
90%
90%
88%
83%
75%
74%
63%
60%
53%
49%
47%
45%
45%
44%
39%
39%
36%
36%
31%
31%
30%
27%
26%
20%
16%
15%

Note that most of the countries have wine intake that is less than 50% of the recommended maximum wine consumption. In theory, this should make the medical people happy. However, there are two ways to be at the top of this list: (i) have a high consumption, and (ii) have a low recommended maximum consumption.

The countries with lower limits than 1.9 bottles per week include Bulgaria and the United Kingdom (1.5 bottles), Chile (1.3), and the Netherlands (1.0). This explains why the UK and the Netherlands are near the top of the list, even though their consumption is not particularly high.

The other top countries are there because of their high wine consumption. Indeed, Croatia, Portugal, France and Switzerland each consume 25% more wine per adult than does their nearest rival (Italy).

The countries with the highest recommended limits include Argentina, Canada and the USA (2.7 bottles per week), Greece (2.8), Romania (3.0), and Spain (3.8). This explains why these countries occupy the bottom places on the list — they set high limits, and so their people's consumption gets nowhere near that limit.

Note that Chile and Bulgaria have low recommended limits but even lower wine consumption.

Finally, it is worth noting that those countries with wine-consumption values exceeding 50% are likely to have average consumptions that exceed the recommended value for females, since these are often half that of the values for males. This is true for Croatia, Switzerland and Portugal. Also, since the value for actual consumption does not take into account what proportion of the population actually drinks wine, there will be many people of both sexes who exceed the recommended limit, possibly by a great margin.

Monday, June 4, 2018

What is a "most admired" wine brand?

The short answer is: It depends on what year it is!

Each year, the April edition of Drinks International magazine contains a supplement with a survey called The World’s Most Admired Wine Brands. A group of people are asked to vote for the wine brands they "most admire" based on the criteria that each brand should:
  • be of consistent and / or improving quality
  • reflect its region or country
  • be well marketed and packaged
  • respond to the needs and tastes of the target audience
  • have broad appeal among wine consumers.
So, while "admiration" is admitted to be an intangible thing, there are clearly a few pointers here.


The people polled are drawn from "a broad spectrum of the global wine community", which apparently includes: masters of wine, sommeliers, commercial wine buyers, wine importers and retailers, wine journalists, wine consultants and analysts, wine educators, and other wine professionals. There were only 60 people involved back in 2012, but there are now more than 200.

The people could originally vote for up to six wine brands, but apparently they are now asked for only three choices. Furthermore, they are provided with a list of previous winners, including "a list of more than 80 well-known brands and producers, but as usual we also encourage the option of free choices". David Williams has presented his own take on what it means to take part in this poll.

The polls

I have compiled the poll results for the years 2011-2018 inclusive. Each of the published lists contains only the results for the top 50 ranked wine brands in that year — all we know about the other brands is that were ranked lower than 50th place in that year.

Across the 8 years, 98 brands have appeared at least once in the lists. However, only 15 of these brands appeared in all 8 lists, with a further 10 brands appearing in 7 of the 8 lists. There were 20 brands that appeared only once each. There is thus a great deal of variability in "admiration" from year to year.

The first graph shows the yearly data for 9 of the wine brands that appeared in all 8 lists, with the vertical axis showing their ranking from 1st on down to 50th. As you can see, even for these brands their results varied dramatically from year to year. Indeed, only the top three brands have shown any consistency at all — these are Torres (from Spain), Concha y Toro (from Chile), and Penfolds (from Australia). Indeed, Torres was ranked either 1st or 2nd every year, which must make it the most admired brand of all.

The "most admired" wine brands from 2011-2018

To this list of consistent brands we can also add E. Guigal (from France) and Ridge (from the USA), both of which missed the 2011 list but had relatively consistent ranks thereafter (mostly in the top 10).

The second graph focuses on the wine brands from Bordeaux, including those that did not make it onto all 8 lists — this is far and away the best-represented wine region in the lists, with 10% of the brands.

The "most admired" Bordeaux wine brands from 2011-2018

These are all top wine chateaus, of course, with the most expensive wines. The most successful of them seems to be Château Margaux, but even it varies in rank from 7 to 29 across the years. Château Latour and Château d'Yquem are the only ones to get into the top 5 in at least one year, but these two chateaus then missed the list entirely in other years. Clearly, Bordeaux does not engender unmitigated admiration in the wine world.

As far as countries are concerned, France hosts 20% of the admired wine brands:
Bordeaux
Rhône
Burgundy
Languedoc
Beaujolais
Provence
General
10
3
2
2
1
1
1
You will note the very poor showing from Burgundy — there are not many large wine brands (only Louis Latour makes most of the lists), but instead a host of smaller brands marketing very expensive wine (Domaine de la Romanée-Conti makes it onto two lists).

The remaining countries include:
Australia
Spain
USA
New Zealand
Chile
Italy
South Africa
Portugal
   [Porto
Germany
Argentina
Canada
China
Hungary
Lebanon
13
 13
11
8
7
6
6
5
4]
3
2
1
1
1
1
Even though the Porto region hosts 4 of Portugal's admired wine brands (Dow's, Graham's, Sandeman, Taylor's), these have only ever made it onto two of the lists (2016 and 2017).

The brands

I have provided a summary of the data relating to the individual brands in the following network. It displays all of those brands that appeared in at least 50% of the lists (ie. 4 out of 8). This is a form of multivariate data summary, as described in my post Summarizing multi-dimensional wine data as graphs, Part 2: networks. [Technical details: this is a neighbor-net based on the gower distance.]

Each brand is represented by a dot in the network. Brands that are closely connected in the network are similar to each other based on their ranks across the 8 polls, and those that are further apart are progressively more different from each other. So, for example, the three top-ranked brands (Torres, Concha y Toro, Penfolds) are together at the top of the diagram, followed by the next pair (E. Guigal and Ridge). From there, the network progresses down to the less-admired wine brands at the bottom.


Note that Guigal and Ridge are at the head of a bunch of 13 wine brands, all of which are relatively highly admired. Then there is a group of 7 intermediate brands (the network area from Oyster Bay down to Pétrus), plus Marqués de Riscal on its own — the latter is isolated in the network only because it inexplicably missed the 2017 list.

You will also note the proximity in the network of Yellowtail to Château Mouton-Rothschild and Cheval Blanc! This should make you wonder about the criteria for "brand admiration".

The basic issue with these lists

There are potentially at least three things wrong with the "best of" type of list: (i) there is rarely any clear idea of what "best" is supposed to mean; (ii) the list is of arbitrary length (eg. Top-10 or Top-50 only); and (iii) the ranking does not reflect the differences in the original scores. In this instance, we have some idea of what "most admired" is supposed to mean; but the other two issues definitely apply here.

More importantly, there is an issue with interpretation that is rarely mentioned. To say, as many of the media have, that "In the 2018 poll the industry voted Torres the most-admired wine brand" is wrong, because there is no evidence that the people polled did any such thing. Indeed, we do not know how many people actually did put Torres (or any other brand) on their own personal list of three brands. All we know is that Torres is listed as the no. 1 brand because more people put it on their list than did so for any other brand — it may have been a lot of people or it may not.


To provide a concrete example of what I mean, I will refer to my previous discussion of a similar situation for the "Greatest Films Poll" produced every decade by Sight & Sound magazine, which lists the top films as voted by selected film critics. The critics are asked to each list 10 films; and in the most recent poll Alfred Hitchcock's film Vertigo topped the overall poll. However, the vast majority of the critics (77%) said that this film doesn't even belong in the top 10 (ie. it was not on their personal list), let alone first. However, it is listed as the no. 1 film, because more critics (23%) put it on their list than did so for any other film. Similarly, 91% of the critics said The Searchers should not be in the top 10, and yet it is ranked no. 7. So, the rank order of the films is simply that — a rank order; it does not tell you how many critics think highly of each film.

This is the same point that I am making for the wine brands, although in this case I do not have the detailed information to say exactly how many people listed each brand.

In a similar vein, anyone who knows anything about banking will known that "the most trusted bank" is nothing more than the "the least mistrusted bank". The distinction is not trivial for the consumer.

This knowledge does not stop the misuse of these sorts of lists, of course. For example, Voxy recently noted: "Villa Maria [was] named the world’s most admired New Zealand wine brand by Drinks International". This is, strictly speaking, true, but Villa Maria did go down from 4th rank overall last year to 8th this year!

In a future post I might looks at some of the other industry awards, such as The World Ranking of Wines and Spirits and The Drinks Business Awards.