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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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    Abstract

    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
    Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 24 Sep 2008
    Last modified: 05 Sep 2014 13:37
    URI: http://strathprints.strath.ac.uk/id/eprint/6911

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