The Prediction of Batting Averages in Major League Baseball

• Main Authors: Sarah R. Bailey, Jason Loeppky, Tim B. Swartz
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Stats
• Subjects: big data; forecasting; logistic regression; PECOTA; Statcast
• Online Access: https://www.mdpi.com/2571-905X/3/2/8

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.