  The widespread availability of superhuman AI engines is changing how we play
the ancient game of Go. The open-source software packages developed after the
AlphaGo series shifted focus from producing strong playing entities to
providing tools for analyzing games. Here we describe two ways of how the
innovations of the second generation engines (e.g.~score estimates, variable
komi) can be used for defining new metrics that help deepen our understanding
of the game. First, we study how much information the search component
contributes in addition to the raw neural network policy output. This gives an
intrinsic strength measurement for the neural network. Second, we define the
effect of a move by the difference in score estimates. This gives a
fine-grained, move-by-move performance evaluation of a player. We use this in
combating the new challenge of detecting online cheating.
