Human Binary Choice Probability in Go Games, 21,212 Professional Matches

A dataset analyzing discrepancies between theoretical and empirical probability in human binary choices, using stone selection in the game of Go as a model. The data includes results from 21,212 professional Go matches among 311 masters and 586 games among 293 amateur players. The research was authored by Yong-Hwan Kim and last updated in April 2026.

Use Cases

• Modeling strategic interactions in binary choice games based on the described sequential stone selection process.
• Investigating rank-based biases in probability outcomes based on the finding that higher-ranked players selected black stones more often.
• Testing theories of empirical versus theoretical probability in human decision-making contexts mentioned in the description.

Strengths

• Includes a large-scale analysis of 21,212 professional Go matches.
• Data covers a specific population of 311 Go masters and 293 amateur players.
• Published under a permissive CC-BY-4.0 license for reuse.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying analysis data is unknown, which may limit suitability assessment.
• The dataset is small (346.2 KB), indicating limited scope or aggregated summary statistics.

Provenance

Source

figshare, author Yong-Hwan Kim.

Collection Method

Analysis of professional Go match records and in situ games with amateur players.

Freshness

Last updated 2026-04-30 05:24:37; freshness should be verified.