I've got a dataset of about 1700 attacks in this game. A subset of the data is provided here. It consists of 9 columns:
The modifier is in a range of 1 to 3 depending on the ratio between the attackers-stats (
Adef) and the defenders stats.
This sounds like a simple regression, but there is a layer of obscurenment where the player's stats are reduced depending on how balanced they are.
So we have a "score" which is less than the sum if the stats are unbalanced - like having wayy more strength than defense.
I've tried countless different ways to make this adjustment in Excel, but I'm unable to find a good match. Secondly, the values are bound to be rounded, sometimes up and sometimes down. We don't know. The rounding can be on the sum, the score, the resulting ratio and so on.
This is the best result from messing around in Excel:
Score = SUM - STDEV. This provides a decent fit, but it kinda breaks down in the most extreme cases (1 stat that is multiple orders of magnitude greater or smaller than the rest.
I'm looking for any ideas or pointers in which direction I can look to solve this? In my head, this is something to throw through a neural network, but I've got no experience with that. I prefer to work with Python if this is a direction I should look.