Overview of Guest Beers – Data from 155 beer tastings

Now that we’re underway, I thought it might be interesting to look at some of the guest beer data we’ve collected over the last year or two.  Pretty much every taste panel I administer (about 3 per week, at the moment) ends with a brief descriptive profiling session focusing on a beer which I have purchased from a local grocer.  Occasionally, I’ll include one of our own rarer beers, but all samples are tasted blind to eliminate bias.  The data presented here is fairly basic, just to provide a general view.  In the future as we accumulate more data we may dig a little deeper and try to uncover some interesting trends.

As a quick overview, here is a chart which shows the relative amounts of beers we’ve tasted from the various breweries;  to date, 155 in all, representing breweries of all types from all over the world. Note the distribution: half of the breweries on the list are only represented by one beer each.  The other half of the breweries represent 118 out of the 155 of samples (76%).   At the moment, Deschutes and Rogue take the top spots with 7 beers tasted each.

Relative numbers of guest beers from each brewery which have been tasted on our panel. Click to embiggen.

But what about the most common terms we use to describe these beers?  Since the data indicates that we have used over 1400 unique descriptors and putting them all on one chart would be prohibitive, I’ve shown only those descriptors which have been used 5 times or more.  As I’ve mentioned previously, and as you can see below, the most common descriptor we’ve used to describe guest beers is “oxidized”.   There are times when I prefer to use a more specific term than this since it can mean a number of things, (including papery, wet cardboard, stone fruit, port) but for an informal exercise like rating guest beers I accept its use as long as we can    accounts for 5% of the total number of terms, at 69 instances it is used 35% more than the next-most common descriptor, “sweet” (n=51), and 73% more than third place, “alcoholic” (n=40).

Most Common Descriptors Used

Next, a glance at the distribution of beer styles that we’ve tasted.  IPA reigns supreme on our panel, with 26 of them tasted.  However, there is a tremendous amount of gray area, wiggle room, and overlap between beer styles, so categorizing them like this is bound to pose difficulties and require awkward compromises.  Most of the time I just use the style stated on the label.  Despite this lack of clear style boundaries, we can still get some information about how they break down into groups.

Frequency of styles represented as guest beers on taste panel.

Now we’ll look at common terms for each of the top 5 styles:  IPA, pale ale, lager, ale, and pilsner.  IPA is first, and only the descriptors used more than twice are included (119 total IPA descriptors).  Once again, oxidized is at the top of the list but the rest should be fairly familiar to those who enjoy the hoppier beers.  Fruity, floral, and vegetative terms dominate the list, as expected.

IPA descriptors (n>=2)

Next, we look at pale ales (76 total descriptors).  Interestingly, oxidized does not take the top spot, but rather “thin” (as in a watery, diluted mouthfeel).  This was a bit of a surprise.  Not so surprising is the lack of fruity and floral terms compared to the IPA’s.

Pale ale descriptors (n>=2)

Our friends, the sulfury lagers are up next (55 unique descriptors among them).  We’d expect a decrease in the frequency of the fruity ester characteristics, and an increase in the sulfur notes common in lagers.

Lager descriptors (n>=2)

The category “ales” is pretty generic, and obviously includes many styles presented here, but it’s used here because most styles were categorized by how it was described on the label.  Ales had 65 unique descriptors, oxidized is the most common again.

Ale descriptors (n>=2)

Finally, it might be nice to compare lagers to pilsners, our fifth most frequently tasted beer style (46 descriptors).  Somewhat surprisingly, oxidized is the 4th most common term but the surprise is tempered when you notice it’s outnumbered by the banner flavors associated with pilsners:  sulfur, grainy, and skunky (this one depending on packaging mostly).

Pilsner descriptors (n>=2)

This just scratches the surface of the type of information we can get out of this data.  I find Excel’s pivot tables to be highly useful in filtering this kind of data in whatever fashion you need.  As I said, we’ll explore this more thoroughly in the future.

 

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