I couldn't resist digging a little bit deeper into the statistics. While playing around, a couple of little features crept in and a couple of bugs crept out. I consider the program done. Again. (Bugfixes will of course follow, if necessary.)
Now to some of the "scientific" results:
This is what happens when one chooses nothing but an average CBill
-criterion with a softness of 0.2
. Only assets within a certain range of the chosen CBill-value are allowed (80k
), others are greatly suppressed. The graph is the result of a lot more fine-tuning.
The same graph with a softness of 0.6
A softness of 1.0
, still using a center-value of 80k CBills
. To get a more even distribution, the value should be at roughly 100k. The very slight drop-off at the edges should give a nice suppression of Solitaires and Daishis, among others.
Here a more interesting image:
The standard criterion, 85k CBills
per asset - i.e. 680k for 8 players. To once and for all make out biases and inconsistencies in the statistics. The very lightest and the heaviest asset show some irregularities, while everything else is approximately flat. The outer assets occur roughly twice as often as others - which, regarding the total amount of assets in the graph, wouldn't be noticeable. The reason is just that the outmost assets can sometimes be chosen to "save" an otherwise useless list. And so they are, it seems.
The last image is very large (12MP), I advise against clicking on it.
This is what happens when a bunch of criteria interact: 640k CBills per 8-player team, CBills centered around 105k, "no" duplicates, 480t per 8-player team, 18 BV per 8-player team.
Even though the single criteria are statistically "clean", the result is not at all predictable. There are a lot of Partisan E (well, probably a manageable amount), while Daishis, Fafnirs and BloodAsps are decently rare. Overall still a nice result, I think.
PS: The Partisan-Mysterie is solved and could be explained using some mathematical and statistical tools...but I'll just leave a short comment: Decrease tonnage or increase CBills, both will result in less Partisans. There is a sweet spot to be found.Conclusion
I never thought this program would become so much more than the sum of its very simple parts. There are so many interdependencies between the different criteria/variables, many combinations will result in biases towards certain assets or at least give somewhat surprising results. I think I had my share of fun with this and made a statement about my utter nerdiness. Hopefully the work will be slightly useful.
To top things off, the list of criteria used for the last graph:
Criterion (Type): " Battle Value (Total)" (BTOT) - Asset-property: " battlevalue" - Softness: 0.05 - 2.07-2.25
Criterion (Type): " Mass (Total)" (BTOT) - Asset-property: " mass" - Softness: 0.00 - 57.00-60.00
Criterion (Type): " No Duplicate Variants" (DUPL) - Asset-property: " name" - Softness: 0.10
Criterion (Type): " No Duplicate Vehicles" (DUPL) - Asset-property: " parent_vehicle" - Softness: 0.20
Criterion (Type): " Price (Center Around)" (BSGL) - Asset-property: " price" - Softness: 1.00 - ~105000.00
Criterion (Type): " Price (Total)" (BTOT) - Asset-property: " price" - Softness: 0.01 - 75200.00-78400.00