The value/policy model includes a few hundred thousand amateur games, and a few hundred million games of self-play. Once AlphaGo beat Fan Hui those would have been games of self-play versus the equivalent of a professional. So overfitting is probably not a problem. I think it's a basic incentive mismatch - MCTS algorithms tend to like close games, whereas humans will try crazy moves when losing to throw off their opponent.