A Simple Statistical Model for Differentiating Go Games

by Bruce Carson, Sangit Chatterjee, and Frederick Wiseman.

Abstract: Two simple statistics, a vector length and a vector angle, calculated from the Go board were investigated in order to determine whether they could be used to predict the type of game being played - a game between two amateurs or a game between two professionals. Results indicated the former variable was of predictive value, while the latter was not. Two classification schemes were used for prediction purposes -- linear discriminant analysis and Classification and Regression Trees (CART). The fact that the vector length was of predictive power should encourage others to calculate similar statistics from the Go board so as to bring about an increase in predictive power. This increase in predictive power may eventually lead to ideas in programming the game of Go.

Key Words: Classification, Linear discrimination, CART, Go programming

Bruce Carson, bcarson@asgoth.com
Sangit Chatterjee, s.chatterjee@neu.edu
Frederick Wiseman, f.wiseman@neu.edu

Editor: Debasis Kundu,Dkundu@utsa.edu

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