The Prediction of Batting Averages in Major League Baseball

Sarah R. Bailey, Jason Loeppky and Tim B. Swartz
Additional contact information
Sarah R. Bailey: Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, Canada
Jason Loeppky: Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia Okanagan, 3187 University Way, Kelowna, BC VIV1V7, Canada
Tim B. Swartz: Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, Canada

Stats, 2020, vol. 3, issue 2, 1-10

Abstract: The prediction of yearly batting averages in Major League Baseball is a notoriously difficult problem where standard errors using the well-known PECOTA (Player Empirical Comparison and Optimization Test Algorithm) system are roughly 20 points. This paper considers the use of ball-by-ball data provided by the Statcast system in an attempt to predict batting averages. The publicly available Statcast data and resultant predictions supplement proprietary PECOTA forecasts. With detailed Statcast data, we attempt to account for a luck component involving batting averages. It is anticipated that the luck component will not be repeated in future seasons. The two predictions (Statcast and PECOTA) are combined via simple linear regression to provide improved forecasts of batting average.

Keywords: big data; forecasting; logistic regression; PECOTA; Statcast (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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