Modeling the Evolution of Distributions: An Application to Major League Baseball
Gary Koop
Edinburgh School of Economics Discussion Paper Series from Edinburgh School of Economics, University of Edinburgh
Abstract:
In this paper, we develop Bayesian techniques for modeling the evolution of entire distributions over time and apply them to the distribution of team performance in Major League baseball for the period 1901-2000. Such models offer insight into many key issues (e.g. competitive balance) in a way that regression-based models cannot. The models involve discretizing the distribution and then modeling the evolution of the bins over time through transition probability matrices. We allow for these matrices to vary over time and across teams. We find that, with one exception, the transition probability matrices (and, hence, competitive balance) have been remarkably constant across time and over teams. The one exception is the Yankees, who have outperformed all other teams.
Keywords: Bayesian; Gibbs samples; ordered probit; Damn Yankees (search for similar items in EconPapers)
Pages: 38
Date: 2001-05
New Economics Papers: this item is included in nep-dev and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.econ.ed.ac.uk/papers/id71_esedps.pdf
Related works:
Journal Article: Modelling the evolution of distributions: an application to Major League baseball (2004) 
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:edn:esedps:71
Access Statistics for this paper
More papers in Edinburgh School of Economics Discussion Paper Series from Edinburgh School of Economics, University of Edinburgh 31 Buccleuch Place, EH8 9JT, Edinburgh. Contact information at EDIRC.
Bibliographic data for series maintained by Research Office ().