earticle

한국어

Artificial Intelligence (AI) is now routinely used by Go players to review their games. Analyzing individual mistakes helps players identify their weaknesses. However, deriving generalizable insights requires a broader analysis of mistake patterns. In this study, 100,682 AI-scored amateur and professional Go games are studied to investigate mistake patterns. Three different ranks are examined, ranging from low-level amateurs to top professionals. Various move features such as height, distance to previous move, and adjacent stones are analyzed to gain a deeper understanding of these mistake patterns. The key findings are as follows: (1) a noticeable improvement in opening performance among professional players since 2017; (2) a significant performance gap in the endgame between professionals (who exhibit near-optimal play) and amateurs; and (3) areas for improvement in tactical skills among amateurs, particularly in first-line and sacrificial moves.