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Regression models for estimation of park effects in Major League Baseball

Abstract Number:

3657 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Jason Osborne (1), Richard Levine (2)

Institutions:

(1) North Carolina State University, N/A, (2) San Diego State University, N/A

Co-Author:

Richard Levine  
San Diego State University

First Author:

Jason Osborne  
North Carolina State University

Presenting Author:

Jason Osborne  
North Carolina State University

Abstract Text:

It is well-known that some ballparks in Major League Baseball are are more conducive to scoring than others. Estimation of "park factors" that quantify these differences has received considerable attention in industry and in the literature, but has not been without criticism. We make two contributions towards the improvement of estimating these effects. We compute, for each ballpark, runs and home runs achieved by all players (home and visiting) with plate appearances at the park, when visiting all other parks. This "elsewhere" measure of performance can be used to quantify offensive strength-of-schedule observed at each park. Secondly, we fit generalized linear models to test data to estimate probabilities of a variety of outcomes (e.g. home runs, doubles, foulouts) that are specific to batter-pitcher handedness combinations. These regression models use handedness-specific relative frequencies of events computed using training data as explanatory variables. The models are fit using test data and used to compute handedness-specific event probabilities adjusted to league averages of event probabilities which we define as park factors.

Keywords:

Generalized linear models. |Regression. Analysis of covariance. Covariate-adjustment.|Baseball. Park Factors.| | |

Sponsors:

Section on Statistics in Sports

Tracks:

Miscellaneous

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