TLDR: random is roughly 477 elo. (See details and caveats below)
See link, where someone matches weird chess algos against various dilutions of stockfish (stockfish at lvl n with x% of random move mixed in). So this elo is of course, relative to various stockfish dilutions at the time of recording. https://www.youtube.com/watch?v=DpXy041BIlA
This is precisely what I was looking for! Thanks. (I was actually imaging various amounts of noise being added to the weights of the evaluation neural net, but this is probably close enough.)
Consider as a near-limiting case, imagine an engine that before the game began flipped a coin. On heads, it plays the game as Stockfish. On tails, it plays the game as Worstfish. What is this engine’s ELO?
I’m short of time to go into details but this should help illustrate why one should be careful treating ELO as a well defined space rather than a local approximation that’s empirically useful for computationally-limited players.
TLDR: random is roughly 477 elo. (See details and caveats below)
See link, where someone matches weird chess algos against various dilutions of stockfish (stockfish at lvl n with x% of random move mixed in). So this elo is of course, relative to various stockfish dilutions at the time of recording.
https://www.youtube.com/watch?v=DpXy041BIlA
This is precisely what I was looking for! Thanks. (I was actually imaging various amounts of noise being added to the weights of the evaluation neural net, but this is probably close enough.)
Consider as a near-limiting case, imagine an engine that before the game began flipped a coin. On heads, it plays the game as Stockfish. On tails, it plays the game as Worstfish. What is this engine’s ELO?
I’m short of time to go into details but this should help illustrate why one should be careful treating ELO as a well defined space rather than a local approximation that’s empirically useful for computationally-limited players.