In my refactoring of Blunder, I'm now at the point where I want to re-visit my mobility scheme. From looking at my logs, my original implementation netted ~30 Elo in self-play when I tested. It's a very basic setup, where the number of moves for each piece is counted, roughly averaged, and then multiplied by a fixed bonus per type. This sort of scheme wasn't always great though at accurately judging the mobility of a position, so I'm working on some different ideas to encode more mobility knowledge into the engine.
I'm curious what other engines are gaining in terms of mobility in their evaluation functions. I'd like to use these answers as a rough metric of how much effort to devote for now into improving evaluation.
Of course, for engines like OliThink, I know this number will be skewed to be quite a bit higher
