The Prediction of Batting Averages in Major League Baseball
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 provid...
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doaj-f938ab5d88784239879e06723b8218202020-11-25T02:21:57ZengMDPI AGStats2571-905X2020-04-0138849310.3390/stats3020008The Prediction of Batting Averages in Major League BaseballSarah R. Bailey0Jason Loeppky1Tim B. Swartz2Department of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, CanadaDepartment of Computer Science, Mathematics, Physics and Statistics, University of British Columbia Okanagan, 3187 University Way, Kelowna, BC VIV1V7, CanadaDepartment of Statistics and Actuarial Science, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A1S6, CanadaThe 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.https://www.mdpi.com/2571-905X/3/2/8big dataforecastinglogistic regressionPECOTAStatcast |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sarah R. Bailey Jason Loeppky Tim B. Swartz |
spellingShingle |
Sarah R. Bailey Jason Loeppky Tim B. Swartz The Prediction of Batting Averages in Major League Baseball Stats big data forecasting logistic regression PECOTA Statcast |
author_facet |
Sarah R. Bailey Jason Loeppky Tim B. Swartz |
author_sort |
Sarah R. Bailey |
title |
The Prediction of Batting Averages in Major League Baseball |
title_short |
The Prediction of Batting Averages in Major League Baseball |
title_full |
The Prediction of Batting Averages in Major League Baseball |
title_fullStr |
The Prediction of Batting Averages in Major League Baseball |
title_full_unstemmed |
The Prediction of Batting Averages in Major League Baseball |
title_sort |
prediction of batting averages in major league baseball |
publisher |
MDPI AG |
series |
Stats |
issn |
2571-905X |
publishDate |
2020-04-01 |
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. |
topic |
big data forecasting logistic regression PECOTA Statcast |
url |
https://www.mdpi.com/2571-905X/3/2/8 |
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