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Various applications to a more realistic baseball simulator

Author

Listed:
  • Beaudoin David

    (Département Opérations et Systèmes de Décision, Faculté des Sciences de l’Administration, Pavillon Palasis-Prince, Bureau 2636, Université Laval, Québec (Québec), G1V0A6 Canada)

Abstract

This paper develops a simulator for matches in Major League Baseball (MLB). Aspects of the approach that are studied include the introduction of base-running probabilities which were obtained through a large data set, and the simulation of nine possible outcomes for each at-bat. Various applications to the simulator are investigated, such as the definition of a measure of the ability of a batter/pitcher, in-play strategy and the determination of the optimal batting order for a given team.

Suggested Citation

  • Beaudoin David, 2013. "Various applications to a more realistic baseball simulator," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(3), pages 271-283, September.
    • Handle: RePEc:bpj:jqsprt:v:9:y:2013:i:3:p:271-283:n:2
    DOI: 10.1515/jqas-2012-0034
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    References listed on IDEAS

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    1. Sueyoshi, Toshiyuki & Ohnishi, Kenji & Kinase, Youichi, 1999. "A benchmark approach for baseball evaluation," European Journal of Operational Research, Elsevier, vol. 115(3), pages 429-448, June.
    2. Baumer Ben S, 2009. "Using Simulation to Estimate the Impact of Baserunning Ability in Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-18, May.
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    Cited by:

    1. Pettigrew Stephen, 2014. "How the West will be won: using Monte Carlo simulations to estimate the effects of NHL realignment," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(3), pages 345-355, September.

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