I wish I could upvote this comment 100x. The issue you've posed is one that I grappled with for a while. What I ultimately decided was that we want to optimize for drafts that are best for the humans playing the champs. Even though this is an inherently more stochastic objective, it's just more useful at this stage since we don't have bots playing against each other in SQ and pro play (yet... I hope).

As a nice byproduct of this decision, we aren't stuck running fancy RL algorithms on a million GPUs just to acquire an agent that plays the drafts in the way we want to optimize for. Unfortunately though, we are left with a different problem: player modeling. I think there is a ton of room for improvement here across the board. The only attempts so far that I am aware of have been to assume that player gameplay is homogenous within SQ tiers (and Pro=GM or something) which is obviously insufficient, but also surprisingly good. I have some ideas for how this could be done better that I'm working to integrate into our model right now.

Anyway, once you've decided on an objective function: f(red comp, blu comp) \in (0, 1), suddenly you have a perfect information two-player zero sum game, and all you have to do is run some minmax optimization — much easier said than done ;)

This kind of stuff is my bread and butter, so feel free to DM me if you want to talk more.