This is the second post of a short series describing my work estimating the score of the Wingspan Automa, and interesting things I learned along the way. As usual, the code for this project is on Github.

## Charts and Graphs!

I’m not going to bore you with the details of learning matplotlib, the Python library for rendering sophisticated charts and graphs, but it turned out to be as cumbersome to use, but also easy enough to get most of what I wanted out of it. Mostly, but good enough to render results for all of you.

### Interpreting Each Graph

For each graph below, the x axis represents the possible score in one of the simulations, and the y axis represents the number of times that score appeared. This is why each graph is a bell curve; the scores are predictable along a mean. The lines-and-triangle pair shows the mean and standard deviation of each graph.

Each minor axis line along the x axis represents a difference of two points for the Automa.

Most of the news won’t be that interesting in this article except that it verifies a few things:

- No surprise: the more points the Automa scores for each Draw action, the higher the score. About eight points per level, it seems – you see the difference between each curve’s mean by looking at the x-coordinate of each triangle. Although I don’t understand why higher-points per Draw action results in a curve of a different height. Each plot line represents 1/3 of all 1,800,000 simulated games. Is it really possible that 600,000 simulations wasn’t enough?It’s interesting that Eagle-eyed Eagle has the largest standard distribution of values, which is why its graph is shorter, more variance. I assume that’s because it depends on the frequency that
*Draw a Bird*and*Play a Bird*actions come up.

plot mean stdev [Eaglet] 80.39430833 7.717951275 [Eagle] 88.548995 7.791237642 [Eagle-eyed Eagle] 96.69753167 8.288301338 - There’s about a two point difference in the Automa’s score whether you use the base game or European Expansion, or both.

plot mean stdev deck[ee] 89.587765 10.54851503 deck[both] 88.28183833 10.24598541 - There’s not much difference whether the Automa uses the
*Rasp Life Fellow*or*Autwitcher*game-end card, though there’s an interesting deviation between the two.

plot mean stdev goal[RaspbLifeFellow] 88.59058889 10.37572037 goal[Autwitcher] 88.50330111 10.34003917 - Of course the Automa is going to store more points with the Autumbon Society card as opposed to without it, about seven on average, it seems. Autombon Society also increases the standard deviation of scores, which isn’t a surprise, I guess — there’s more cards to draw from each round.

plot mean stdev autumbon[False] 84.98969444 9.2331507 autumbon[True] 92.10419556 10.19885435

## Coming Next

Why? What if? (When I dig in, and then write it.. :D)