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The Prediction of Batting Averages in Major League Baseball

Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, Canada
Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia Okanagan, 3187 University Way, Kelowna, BC VIV1V7, Canada
Author to whom correspondence should be addressed.
Stats 2020, 3(2), 84-93;
Received: 10 March 2020 / Revised: 28 March 2020 / Accepted: 31 March 2020 / Published: 3 April 2020
(This article belongs to the Section Data Science)
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. View Full-Text
Keywords: big data; forecasting; logistic regression; PECOTA; Statcast big data; forecasting; logistic regression; PECOTA; Statcast
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Bailey, S.R.; Loeppky, J.; Swartz, T.B. The Prediction of Batting Averages in Major League Baseball. Stats 2020, 3, 84-93.

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