earticle

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Analysing Go games with AIpowered engines is now common practice among amateur and professional Go players. These engines rely on deep artificial neural networks (ANN) and MonteCarlo tree search (MCTS) to evaluate board positions. For a given ANN, the engine’s accuracy increases with the number of positions evaluated during the tree search, or number of visits (often called the number of playouts). Although more playouts lead to more accurate analyses, the relationship between playouts and engine accuracy has not been fully explored. In this study, we analysed thousands of games from amateur and professional players, using different numbers of playouts per move, ranging from 5 to 15,000. A statistical analysis of the results shows that a constant number of playouts per move leads to varying accuracy during the game (high accuracy during the opening and endgame, low accuracy during the middle game). Quantitative guidelines to choose the number of playouts depending on one’s rank are derived. Looking at AI prediction of human win rate, the playouts needed to reach 99% accuracy are 140 for 1d, 235 for 5d, and 1,500 for 9d Fox players and 5,500 for a 9p professional player.