We propose a way of extracting and aggregating per-move evaluations from sets
of Go game records. The evaluations capture different aspects of the games such
as played patterns or statistic of sente/gote sequences...
Using machine learning
algorithms, the evaluations can be utilized to predict different relevant
target variables. We apply this methodology to predict the strength and playing
style of the player (e.g. territoriality or aggressivity) with good accuracy. We propose a number of possible applications including aiding in Go study,
seeding real-work ranks of internet players or tuning of Go-playing programs.