IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v5y2009i2n8.html

Using Simulation to Estimate the Impact of Baserunning Ability in Baseball

Author

Listed:
  • Baumer Ben S

    (CUNY Graduate School and University Center)

Abstract

In baseball, an offensive team's run scoring ability is dependent not only upon the batting skills of its players, but also their baserunning abilities. Using a Monte Carlo simulation based on actual statistics of real players, we estimate the magnitude of the effect of baserunning skills upon a team's run scoring ability. Our results largely confirm previous non-academic estimates that the impact of baserunning upon a team's run scoring ability is typically less than ±25 runs per season. However, we show using simple heuristic algorithms, that a team composed of the best (worst) nine baserunners could gain (lose) as many as 70 (55), runs per season due to baserunning.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:jqsprt:v:5:y:2009:i:2:n:8
    DOI: 10.2202/1559-0410.1174
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1559-0410.1174
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1559-0410.1174?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. R. Allan Freeze, 1974. "An Analysis of Baseball Batting Order by Monte Carlo Simulation," Operations Research, INFORMS, vol. 22(4), pages 728-735, August.
    2. Bruce Bukiet & Elliotte Rusty Harold & José Luis Palacios, 1997. "A Markov Chain Approach to Baseball," Operations Research, INFORMS, vol. 45(1), pages 14-23, February.
    3. Turocy, Theodore L., 2005. "Offensive performance, omitted variables, and the value of speed in baseball," Economics Letters, Elsevier, vol. 89(3), pages 283-286, December.
    4. Mark D. Pankin, 1978. "Evaluating Offensive Performance in Baseball," Operations Research, INFORMS, vol. 26(4), pages 610-619, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Baumer Ben S. & Piette James & Null Brad, 2012. "Parsing the Relationship between Baserunning and Batting Abilities within Lineups," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(2), pages 1-19, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
    2. J M Norman & S R Clarke, 2010. "Optimal batting orders in cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(6), pages 980-986, June.
    3. Holmes, Benjamin & McHale, Ian G. & Żychaluk, Kamila, 2023. "A Markov chain model for forecasting results of mixed martial arts contests," International Journal of Forecasting, Elsevier, vol. 39(2), pages 623-640.
    4. Daniel Cervone & Alex D’Amour & Luke Bornn & Kirk Goldsberry, 2016. "A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 585-599, April.
    5. Akifumi Kira & Keisuke Inakawa, 2014. "On Markov perfect equilibria in baseball," TMARG Discussion Papers 115, Graduate School of Economics and Management, Tohoku University.
    6. M Wright & N Hirotsu, 2003. "The professional foul in football: Tactics and deterrents," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(3), pages 213-221, March.
    7. Davis Donald M, 2011. "Markov Analysis of APBA, a Baseball Simulation Game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-14, July.
    8. Mancini Simona & Isabello Andrea, 2014. "Fair referee assignment for the Italian soccer serieA," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 153-160, June.
    9. M B Wright, 2009. "50 years of OR in sport," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 161-168, May.
    10. John Charles Bradbury & Douglas J. Drinen, 2007. "Crime And Punishment In Major League Baseball: The Case Of The Designated Hitter And Hit Batters," Economic Inquiry, Western Economic Association International, vol. 45(1), pages 131-144, January.
    11. Young William A & Holland William S & Weckman Gary R, 2008. "Determining Hall of Fame Status for Major League Baseball Using an Artificial Neural Network," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(4), pages 1-46, October.
    12. Kostuk, Kent J. & Willoughby, Keith A. & Saedt, Anton P. H., 2001. "Modelling curling as a Markov process," European Journal of Operational Research, Elsevier, vol. 133(3), pages 557-565, September.
    13. Hirotsu Nobuyoshi, 2011. "Reconsideration of the Best Batting Order in Baseball: Is the Order to Maximize the Expected Number of Runs Really the Best?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-12, May.
    14. Srinivas K. Reddy & Antonie Stam & Per J. Agrell, 2015. "Brand Equity, Efficiency and Valuation of Professional Sports Franchises: The Case of Major League Baseball," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 5(1), pages 63-89, January.
    15. Ryan Pinheiro & Stefan Szymanski, 2022. "All Runs Are Created Equal: Labor Market Efficiency in Major League Baseball," Journal of Sports Economics, , vol. 23(8), pages 1046-1075, December.
    16. Chia-Hao Chang, 2021. "Construction of a Predictive Model for MLB Matches," Forecasting, MDPI, vol. 3(1), pages 1-11, February.
    17. John Charles Bradbury & Douglas J. Drinen, 2008. "Pigou at the Plate," Journal of Sports Economics, , vol. 9(2), pages 211-224, April.
    18. Damásio, Bruno & Nicolau, João, 2024. "Time inhomogeneous multivariate Markov chains: Detecting and testing multiple structural breaks occurring at unknown dates," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    19. Woojin Doo & Heeyoung Kim, 2018. "Modeling the probability of a batter/pitcher matchup event: A Bayesian approach," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-11, October.
    20. Jim Downey & Joseph McGarrity, 2015. "Pick off Throws, Stolen Bases, and Southpaws: A Comparative Static Analysis of a Mixed Strategy Game," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 43(3), pages 319-335, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:5:y:2009:i:2:n:8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.