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Modelling the evolution of distributions: an application to major league baseball

Koop, G.M. (2004) Modelling the evolution of distributions: an application to major league baseball. Journal of the Royal Statistical Society: Series A, 167. pp. 639-656. ISSN 0964-1998

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We develop Bayesian techniques for modelling 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 modelling 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.

Item type: Article
ID code: 6911
Notes: Working paper version
Keywords: bayesian, Gibbs sampler, ordered probit, sports statistics, Economic Theory, Statistics, Economics and Econometrics, Social Sciences (miscellaneous), Statistics and Probability, Statistics, Probability and Uncertainty
Subjects: Social Sciences > Economic Theory
Social Sciences > Statistics
Department: Strathclyde Business School > Economics
Depositing user: Strathprints Administrator
Date Deposited: 24 Sep 2008
Last modified: 20 Oct 2015 20:37

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