Data Viz Revision: Maeve’s Westworld Attribute Matrix

This post contains spoilers about “Westworld


In episode 6 of HBO’s wildly popular drama “Westworld,” viewers got a brief look at the “Attribute Matrix” of Maeve, one of the host androids featured in the show (h/t reddit):


The attribute matrix is a graph of the values assigned to each trait (on a scale from 0 (1?) to 20). The visualization itself is just a radar chart. I’ve reproduced a rough version below for better visibility:


Since I first got a glimpse of this back in episode 6, I’ve been thinking about better ways to visualize the Westworld hosts’ attributes. The biggest problem with using a radar chart is that there doesn’t seem to be any meaningful order or organization of the host attributes; the polygon carved out by the radar chart values is an arbitrary shape that could change drastically with a different attribute order.

Radar charts are sometimes used when comparing multiple attributes among different series of values.  In this example, the values of six different attributes are compared across several countries and the resulting polygons are laid on top of one another:


Kap Lab (via Scott Logic)

In this next example, the concept of small multiples is used to compare the 12 NBA players who made the 2013 All Star Game for the Eastern Conference based on how they rank in 11 statistical categories:


Rami Moghadam

In these 2 examples, the polygon shapes formed by connecting each series value make sense to compare in the context of the visualizations. They compare multiple bundles of values on a common scale. In the two examples above, those bundles are countries and NBA players, respectively.

But in the image from Westworld, only one host’s values–Maeve’s–are shown.  This removes the main advantage a radar chart has, namely, comparing multiple values across many series.

Given that, I decided on 4 potential revisions.


Option 1: Bar Chart


I decided to do a pretty standard bar chart for the first revision.  Gone is the unwieldy polygon of the radar chart; in its place is a series of bars, ranked from highest to lowest.  This allows the audience to more easily grasp the relationship among the attribute values.

I decided against the random order of the original attribute matrix or alphabetical ordering because they don’t really help when looking at a single host’s data.


Option 2: Bullet Chart


This is like the previous bar chart, only with an added series showing the maximum value of 20.  The benefit of this one is that for each attribute, you can see how far the value is from the maximum, so it gives the effect of a bar filling up.  I like this one.


Option 3: Lollipop Chart


This one is similar to the bar chart, only with thinner bars and a filled circle at the end.  The lollipop looks a bit cleaner to me, probably because the bars take up less space.

(h/t Stephanie Evergreen for the Excel tutorial).


Option 4: Table with Conditional Formatting


This final revision is just a table with the values ranked from highest value to lowest. I added shading created by conditional formatting based on the same ranking.  For that reason, the shading is redundant, but I like the look.



I think any one of these is preferable to the original radar chart.  Which one would you choose?  Is there another, more effective visualization that I’ve overlooked?


  1. In answer to your question, I would go back to your critique of the radar chart and order the traits so that they are NOT random. Then you would get a smoother shape that “leans” in a direction and very well gives the sense of the mixture of those traits.


  2. Although a complete different ‘ballgame’, you may well want to have a look into the build up and display of unique attributes of players in the soccer management game Football Manager. There you’ll find players also charted in 1-20 values in either graphics or bundled attributes. Checking out the editor you’d also find ‘hidden’ character traits that interconnect with the visible ones, create unique individuals on the pitch as well as interactively.

    The matrix graph reminds me of those psychology spiderweb diagrams, here certain traits are logically linked or seemingly related. That might give better overview


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