Stone Selection Probabilities from Professional and Amateur Go Matches

21,212 professional Go matches among 311 masters and 586 amateur games from 293 participants form a dataset on binary choice behavior. The data, authored by Yong-Hwan Kim and last updated in April 2026, investigates discrepancies between empirical and theoretical probability in human decisions. Analyses show higher-ranked players have a significantly higher chance of selecting black stones, challenging the assumption of equal odds in binary choices.

Use Cases

• Modeling strategic interaction in binary choice games based on the described stone selection process.
• Analyzing the correlation between player rank and choice probability based on the reported findings for professional and amateur players.
• Studying deviations from theoretical probability in human decision-making based on the large-scale analysis of Go matches.

Strengths

• Includes data from 21,212 professional Go matches involving 311 masters.
• Contains results from in situ games with 293 amateur participants, each playing 2 games.
• Explicitly investigates a specific research question on empirical versus theoretical probability.

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 in size (373.5 KB), indicating limited scope.

Provenance

Source

Yong-Hwan Kim via figshare

Collection Method

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

Freshness

Last updated 2026-04-30 05:24:36; freshness should be verified.