Impact Aversion: Over a Million MLB Umpire Decisions on Game Outcomes

Etan Green of Stanford University created a dataset analyzing over a million decisions made by Major League Baseball umpires. The data tests propositions about incentives solving principal-agent problems and reducing decision biases. It reveals a systematic aversion among umpires to choices that would more strongly change a game's expected outcome, termed 'impact aversion'.

Use Cases

• Modeling decision bias under pressure based on the described 'impact aversion' behavior.
• Analyzing principal-agent dynamics based on the described incentive structures in professional sports officiating.
• Studying systematic deviations from objective rules in high-stakes environments based on the described umpire behavior.

Strengths

• Over a million decisions provide a substantial observational basis for analysis.
• Data is derived from a high-stakes professional sports environment with clear rules and incentives.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Stanford University

Collection Method

Analysis of Major League Baseball umpire decisions.

Geography

Major League Baseball games, likely within the United States and Canada.