You wouldn't really need a _ton_ of games to get plausible data, but unfortunately today each game costs real money - typically a dollar or more with my current harness, though I'm hoping to optimize it and of course I expect model costs to continue to decline over time. But even reasonably-expensive models today are making tons of blunders that a tournament grinder wouldn't.

I'm not trying to compute a chess-style "player X was at 0.4 before this move and at 0.2 afterwards, so it was a -0.2 blunder", but I do have "blunder analysis" where I just ask Opus to second-guess every decision after the game is over - there's a bit more information on the Methodology page. So then you can compare models by looking at how often they blunder, rather than the binary win/loss data. If you look at individual games you can jump to the "blunders" on the timeline - most of the time I agree with Opus's analysis.