Improving Major League Baseball Park Factor Estimates

Rohit A Acharya 1 , Alexander J Ahmed 2 , Alexander N D'Amour 3 , Haibo Lu 4 , Carl N Morris 5 , Bradley D Oglevee 6 , Andrew W Peterson 7  and Robert N Swift 8
  • 1 Harvard University
  • 2 Harvard University
  • 3 Harvard University
  • 4 Harvard University
  • 5 Harvard University
  • 6 Harvard University
  • 7 Harvard University
  • 8 Harvard University

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.

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JQAS, an official journal of the American Statistical Association, publishes research on the quantitative aspects of professional and collegiate sports. Articles deal with subjects as measurements of player performance, tournament structure, and the frequency and occurrence of records. Additionally, the journal serves as an outlet for professionals in the sports world to raise issues and ask questions that relate to quantitative sports analysis.

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