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Improving Major League Baseball Park Factor Estimates

Author

Listed:
  • Acharya Rohit A

    (Harvard University)

  • Ahmed Alexander J

    (Harvard University)

  • D'Amour Alexander N

    (Harvard University)

  • Lu Haibo

    (Harvard University)

  • Morris Carl N

    (Harvard University)

  • Oglevee Bradley D

    (Harvard University)

  • Peterson Andrew W

    (Harvard University)

  • Swift Robert N

    (Harvard University)

Abstract

The study of Park Factors (PF) is essential to the correct evaluation of player performance in Major League Baseball. We have identified two important problems with the commonly used formula which has been popularized by ESPN: it produces variable results due to unbalanced scheduling, and it has an inherent inflationary bias. To address these problems, we develop a new estimator for Park Factors using an ANOVA weighted fixed-effects model for run generation. Using simulated data, in addition to run data from 2000 through 2006, we show that this new estimator does not have the biases of the old estimator. From a strategic viewpoint, accurate PF values are needed to properly evaluate free agents and trade proposals, as well as to compare players for postseason awards. We develop a method to adjust statistics using Park Factors called a Neutral Park Adjustment (NPA), which takes into account the Park Factors of the entire schedule of a player, not simply their home park.

Suggested Citation

  • Acharya Rohit A & Ahmed Alexander J & D'Amour Alexander N & Lu Haibo & Morris Carl N & Oglevee Bradley D & Peterson Andrew W & Swift Robert N, 2008. "Improving Major League Baseball Park Factor Estimates," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(2), pages 1-18, April.
    • Handle: RePEc:bpj:jqsprt:v:4:y:2008:i:2:n:4
    DOI: 10.2202/1559-0410.1108
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    Cited by:

    1. Vock David Michael & Vock Laura Frances Boehm, 2018. "Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 37-56, June.

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