Go Game Stone Selection Data for Empirical vs. Theoretical Probability Analysis

21,212 professional Go matches among 311 masters and 586 amateur games from 293 participants were analyzed to investigate discrepancies in binary choice probability. The dataset, authored by Yong-Hwan Kim and last updated in April 2026, shows higher-ranked players had a significantly higher chance of selecting black stones. This research provides insights into the divergence between theoretical and empirical odds in sequential strategic interactions.

Use Cases

• Modeling binary choice behavior based on player ranking data mentioned in the description
• Analyzing strategic interactions in sequential games based on the stone selection process
• Testing hypotheses about empirical probability deviations in human decision-making based on large-scale match data

Strengths

• Includes data from 21,212 professional Go matches, providing a substantial sample for analysis
• Contains results from an in-situ experiment with 293 amateur participants playing 2 games each
• Explicitly compares player rankings (higher vs. lower) with stone selection outcomes

Limitations

• Column-level documentation is absent; field semantics must be inferred after download
• Row count is unknown, which may limit suitability assessment
• The dataset is small (18.0 KB), indicating limited scope or aggregated summary statistics

Provenance

Source

figshare, author Yong-Hwan Kim

Collection Method

Large-scale data analysis of professional Go matches and in-situ experiments with amateur players

Freshness

Last updated 2026-04-30 05:24:39; freshness should be verified