> high rated players are inherently more difficult to model

yes and no. there's a bit of a bathtub curve here where below ~1000 elo lower ratings are harder to predict because their moves are closer to random.

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kelipso 7 days ago | parent [-]

I don't even think "high rated players are inherently more difficult to model" is correct, and the opposite is more likely to be correct. Top player, more than 2700 players, tend to play the engine moves the majority of the time, while lower rated players tend to stray from the engine lines.

	▲	janalsncm 6 days ago \| parent \| next [-]
		https://arxiv.org/pdf/2006.01855 If you check figure 4, SF at depth 15 matches about 47% of the time which is lower than their NN. So quantitatively, SF isn’t amazing at move matching, and lower than the best NN at its relevant rating range (1900 -> 54%). Qualitatively, SF is pretty unsatisfying as a model of a pro human because a lot of its moves are counterintuitive.
	▲	adgjlsfhk1 7 days ago \| parent \| prev [-]
		top players are hard to model just because there aren't many of them. 1200-2200 has tons of players and they all make various degrees of sensible moves most of the time, so you can plausibly train a NN to sample from those distributions. for 2500+ you have far fewer players so the likely moves depend a lot more on the styles and blindspots of specific players.