If this actually hasn’t been explored, this is a really cool idea! So you want to learn a function (Player 1, Player 2, position) → (probability Player 1 wins, probability of a draw)? Sounds like there are a lot of naive architectures to try and you have a ton of data since professional chess players play a lot of games.
Some random ideas:
Before doing any sort of positional analysis: What does the (ELO_1,ELO_2,engine eval) → Probability of win/draw function look like? What happens when choosing an engine near those ELO ratings vs. the strongest engines?
Observing how rapidly the eval changes when given to a weak engine might give a somwhat automatable metric on the “sharpness” of a chess position (so you don’t have to label everything yourself)
If this actually hasn’t been explored, this is a really cool idea! So you want to learn a function (Player 1, Player 2, position) → (probability Player 1 wins, probability of a draw)? Sounds like there are a lot of naive architectures to try and you have a ton of data since professional chess players play a lot of games.
Some random ideas:
Before doing any sort of positional analysis: What does the (ELO_1,ELO_2,engine eval) → Probability of win/draw function look like? What happens when choosing an engine near those ELO ratings vs. the strongest engines?
Observing how rapidly the eval changes when given to a weak engine might give a somwhat automatable metric on the “sharpness” of a chess position (so you don’t have to label everything yourself)