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MLB Umpires – The Study of Imperfection

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Goals of the Project

    • Analyze the variability of precision and accuracy during decision making
        • Utilize MLB Umpires as a case study
    • Look for trends and factors that may cause variability
    • View the magnitude of the effects.
    • Evaluate possible solutions

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Methodology

    • Collect and cumulate game, umpire, and team data from Umpire Scorecards
    • Conduct an analysis of umpire performance using experience as the dependant variable
    • Create a Overturn Simulation to see the possible seasonwide affects

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Important Terms

    • Accuracy – How accurate an umpire

is within the MLB Standard Strikezone

    • Consistency – How accurate an

umpire is within his own strikezone

    • Favor – The cumulative difference in run expectancy values for inaccurate pitches.

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Umpire Analysis

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Conclusions

    • Significant Correlation between Years Experience & Umpire Impact
    • Strong case to say experience/age can be used to explain differences in accuracy between umpires.

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Umpire Overturns

    • What is it: When the favor of a team is greater than its winning or losing run differential.
    • 284 occurrences ~2% of games since 2015
    • 171 vs 113 for the Home and Away teams
    • Significant decrease in overturns per year.

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Umpire Impact

    • Positive Correlation between experience and Overturns (R^2 = .16)
    • Fits similar theme

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Season Wide Impact

    • Separated Overturns into wins gained and lost per team
    • Calculated percentage of overturned outcome using pythagorean expected win percentage taking into account favor

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Case Study 1

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Case Study 2

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Case Study 3

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Concluding Thoughts

    • Umpire variability is legitimate, and correlates strongly with years of experience.
    • No bias was found towards or against any team
    • Variability can have a real impact on a season.
    • What will the effect of AAA Robot Umpires be?

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Thank you!

Questions?

Contact info: jasoos@syr.edu | linkedin.com/in/jonahsoos24