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The Prediction of Batting Averages in Major League Baseball Bailey, Sarah R.; Loeppky, Jason; Swartz, Tim B.

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.

Item Metadata

Title
The Prediction of Batting Averages in Major League Baseball
Creator
Bailey, Sarah R.; Loeppky, Jason; Swartz, Tim B.
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2020-04-03
Description
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.
Subject
big data; forecasting; logistic regression; PECOTA; Statcast
Genre
Article
Type
Text
Language
eng
Date Available
2020-06-30
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0392020
URI
http://hdl.handle.net/2429/74814
Affiliation
Arts and Sciences, Irving K. Barber School of (Okanagan); Non UBC; Computer Science, Mathematics, Physics and Statistics, Department of (Okanagan)
Citation
Stats 3 (2): 84-93 (2020)
Publisher DOI
10.3390/stats3020008
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
DSpace

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CC BY 4.0