Parsing the Relationship between Baserunning and Batting Abilities within Lineups

Baumer Ben S., Piette James and Null Brad
Additional contact information
Baumer Ben S.: CUNY Graduate School and University Center
Piette James: University of Pennsylvania
Null Brad: Stanford University

Journal of Quantitative Analysis in Sports, 2012, vol. 8, issue 2, 19

Abstract: A baseball team's offensive prowess is a function of two types of abilities: batting and baserunning. While each has been studied extensively in isolation, the effects of their interaction is not well understood. We model offensive output as a scalar function f of an individual player's batting and baserunning profile z. Each of these profiles is in turn estimated from Retrosheet data using heirarchical Bayesian models. We then use the SimulOutCome simulation engine as a method to generate values of f(z) over a fine grid of points. Finally, for each of several methods of taking the extra base, we graphically depict the surface f(z) over changes in the probability of advancing via that method. This framework allows us to draw conclusions both about optimal baserunning strategies in general, and about how particular offensive profiles affect a player's optimal baserunning strategy. We present many informative visualizations and analyze specific aspects of several well-known Major League players.

Keywords: baseball; baserunning; Bayesian modeling; simulation; data visualization (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/1559-0410.1429 (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:8:y:2012:i:2:n:7

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

DOI: 10.1515/1559-0410.1429

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 ().