Swing shift: a mathematical approach to defensive positioning in baseball

Bouzarth Elizabeth, Grannan Benjamin, Harris John, Hartley Andrew, Hutson Kevin () and Morton Ella
Additional contact information
Bouzarth Elizabeth: Mathematics, Furman University, Greenville, SC, USA
Grannan Benjamin: Business and Accounting, Furman University, Greenville, SC, USA
Harris John: Mathematics, Furman University, Greenville, SC, USA
Hartley Andrew: Mathematics, Furman University, Greenville, SC, USA
Hutson Kevin: Mathematics, Furman University, Greenville, SC, USA
Morton Ella: Mathematics, Furman University, Greenville, SC, USA

Journal of Quantitative Analysis in Sports, 2021, vol. 17, issue 1, 47-55

Abstract: Defensive repositioning strategies (shifts) have become more prevalent in Major League Baseball in recent years. In 2018, batters faced some form of the shift in 34% of their plate appearances (Sawchik, Travis. 2019. “Don’t Worry, MLB–Hitters Are Killing The Shift On Their Own.” FiveThirtyEight, January 17, 2019. Also available at fivethirtyeight.com/features/dont-worry-mlb-hitters-are-killing-the-shift-on-their-own/). Most teams use a shift that overloads one side of the infield and adjusts the positioning of the outfield. In this work we describe a mathematical approach to the positioning of players over the entire field of play without the limitations of traditional positions or current methods of shifting. The model uses historical data for individual batters, and it leaves open the possibility of fewer than four infielders. The model also incorporates risk penalties for positioning players too far from areas of the field in which extra-base hits are more likely. This work is meant to serve as a decision-making tool for coaches and managers to best use their defensive assets. Our simulations show that an optimal positioning with three infielders lowered predicted batting average on balls in play (BABIP) by 5.9% for right-handers and by 10.3% for left-handers on average when compared to a standard four-infielder placement of players.

Keywords: integer linear programming; multiple criteria decision making; shift strategies in baseball (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jqas-2020-0027 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:17:y:2021:i:1:p:47-55:n:5

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html

DOI: 10.1515/jqas-2020-0027

Access Statistics for this article

Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman

More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().