Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Steve Rigdon, PhD


CiteScore 2018: 1.67

SCImago Journal Rank (SJR) 2018: 0.587
Source Normalized Impact per Paper (SNIP) 2018: 1.970

Print + Online
See all formats and pricing
More options …
Volume 9, Issue 3

Issues

Volume 1 (2005)

Various applications to a more realistic baseball simulator

David Beaudoin
  • Corresponding author
  • Département Opérations et Systèmes de Décision, Faculté des Sciences de l’Administration, Pavillon Palasis-Prince, Bureau 2636, Université Laval, Québec (Québec), G1V0A6 Canada
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-08-13 | DOI: https://doi.org/10.1515/jqas-2012-0034

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.

This article offers supplementary material which is provided at the end of the article.

Keywords: analysis of variance; in-play strategy; in-play probabilities; logistic regression; major league baseball; measure of performance; optimal batting order; simulation

References

  • Ano, K. 2001. “Modified Offensive Earned-Run Average with Steal Effect for Baseball.” Applied Mathematics and Computation 120(1–3): 279–288.Google Scholar

  • Baumer, B. S. 2009. “Using Simulation to Estimate the Impact of Baserunning Ability in Baseball.” Journal of Quantitative Analysis in Sports 5(2): 1–16.Google Scholar

  • Beaudoin, D. and T. B. Swartz. 2010. “Strategies for Pulling the Goalie in Hockey.” The American Statistician 64(3): 197–204.CrossrefWeb of ScienceGoogle Scholar

  • Bennett, J. M. and J. A. Flueck. 1983. “An Evaluation of Major League Offensive Performance Models.” The American Statistician 37: 76–82.Google Scholar

  • Bukiet, E. R., E. Harold, and J. L. Palacios. 1997. “A Markov Chain Approach to Baseball.” Operations Research 45: 14–23.CrossrefGoogle Scholar

  • Cover, T. M. and C. W. Keilers. 1977. “An Offensive Earned-Run Average for Baseball.” Operations Research 25: 729–740.CrossrefGoogle Scholar

  • D’Esopo, D. A. and B. Lefkowitz. 1977. “The Distribution of Runs in the Game of Baseball.” pp. 55–62 in Optimal strategies in sports, edited by S.P. Ladany and R. E. Machal. New York: North Holland.Google Scholar

  • Hirotsu, N. and M. Wright. 2005. “Modelling a Baseball Game to Optimise Pitcher Substitution Strategies Incorporating Handedness of Players.” IMA Journal of Management Mathematics 16: 179–194.Google Scholar

  • Hirotsu, N. and M. Wright. 2004. “Modelling a Baseball Game to Optimize Pitcher Substitution Strategies Using Dynamic Programming.” pp. 131–161 in Economics, Management, and Optimization in Sports, edited by S. Butenko et al. Berlin: Springer.Google Scholar

  • James, B. 1981. The Bill James Baseball Abstract. New York: Ballantine Books.Google Scholar

  • James, B. 1987. The Bill James Baseball Abstract. New York: Villard Books.Google Scholar

  • Kinoshita, A. 1987. “Evaluation of Baseball Batters and Pitchers (in Japanese).” Communications of the Operations Research Society of Japan 32: 689–697.Google Scholar

  • Lackritz, J. 1990. “Salary Evaluation for Professional Baseball Players.” The American Statistician 44: 4–8.Google Scholar

  • Lewis, M. 2003. Moneyball: the art of winning an unfair game. New York: W.W. Norton and Company.Google Scholar

  • Lindsey, G. R. 1977. “A Scientific Approach to Strategy in Baseball.” Optimal strategies in sports. New York: Elsevier-North Holland.Google Scholar

  • McCracken, V. 2001. “Pitching and Defense: How Much Control Do Hurlers Have?l.” http://www.baseballprospectus.com/article.php?articleid=878

  • Mills, E. and H. Mills. 1970. Player win averages. New Jersey: A.S. Barnes and Co., Cranbury.Google Scholar

  • Pankin, M. D. 1978. “Evaluating Offensive Performance in Baseball.” Operations Research 26: 610–619.CrossrefGoogle Scholar

  • Sueyoshi, T., K. Ohnishi, and Y. Kinase. 1999. “A Benchmark Approach for Baseball Evaluation.” European Journal of Operational Research 115: 429–448.CrossrefGoogle Scholar

  • Sugano, A. 2008. “A Player Based Approach to Baseball Simulation”, University of California, Los Angeles (dissertation).Google Scholar

  • Tango, T. M., M. G. Lichtman, and A. E. Dolphin. 2006. The book: playing the percentages in baseball. Dulles, Virginia, USA: Potomac Books Inc.Google Scholar

About the article

Corresponding author: David Beaudoin, Associate Professor, Département Opérations et Systèmes de Décision, Faculté des Sciences de l’Administration, Pavillon Palasis-Prince, Bureau 2636, Université Laval, Québec (Québec), G1V0A6 Canada


Published Online: 2013-08-13

Published in Print: 2013-09-01


Citation Information: Journal of Quantitative Analysis in Sports, Volume 9, Issue 3, Pages 271–283, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2012-0034.

Export Citation

©2013 by Walter de Gruyter Berlin Boston.Get Permission

Supplementary Article Materials

Comments (0)

Please log in or register to comment.
Log in