IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v57y2009i4p812-822.html
   My bibliography  Save this article

OR Practice---Training Optimization for the Decathlon

Author

Listed:
  • Yori Zwols

    (Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027)

  • Gerard Sierksma

    (Department of Operations, University of Groningen, 9700 AB Groningen, The Netherlands)

Abstract

Decathlon coaches usually spend days or even weeks on designing training schedules for their athletes. A major complication is the fact that the athlete has only limited time for an extensive range of training exercises, whereas the effects of exercises on the athlete's performance in the various events are interrelated. This paper presents a mathematical model for optimizing the use of the total available training time by assigning time to training exercises. The data used in this time capacity planning model concerns a decathlete preparing for the Olympic Games.

Suggested Citation

  • Yori Zwols & Gerard Sierksma, 2009. "OR Practice---Training Optimization for the Decathlon," Operations Research, INFORMS, vol. 57(4), pages 812-822, August.
    • Handle: RePEc:inm:oropre:v:57:y:2009:i:4:p:812-822
    DOI: 10.1287/opre.1080.0616
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.1080.0616
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.1080.0616?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
    ---><---

    References listed on IDEAS

    as
    1. Paul Kvam & Joel S. Sokol, 2006. "A logistic regression/Markov chain model for NCAA basketball," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(8), pages 788-803, December.
    2. Kenny Ian Christopher & Sprevak Dan & Sharp Craig & Boreham Colin, 2005. "Determinants of Success in the Olympic Decathlon: Some Statistical Evidence," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 1(1), pages 1-8, October.
    3. N Hirotsu & M Wright, 2003. "Determining the best strategy for changing the configuration of a football team," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 878-887, 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. Guillaume Roels, 2020. "High-Performance Practice Processes," Management Science, INFORMS, vol. 66(4), pages 1509-1526, April.

    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. Baback Vaziri & Shaunak Dabadghao & Yuehwern Yih & Thomas L. Morin & Mark Lehto, 2020. "Crowd-Ranking: a Markov-based method for ranking alternatives," Operational Research, Springer, vol. 20(1), pages 279-295, March.
    2. Matthew J. Drake & Serhan Duran & Paul M. Griffin & Julie L. Swann, 2008. "Optimal timing of switches between product sales for sports and entertainment tickets," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(1), pages 59-75, February.
    3. Jarvandi Ali & Sarkani Shahram & Mazzuchi Thomas, 2013. "Modeling team compatibility factors using a semi-Markov decision process: a data-driven approach to player selection in soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(4), pages 347-366, December.
    4. S. S. Dabadghao & B. Vaziri, 2022. "The predictive power of popular sports ranking methods in the NFL, NBA, and NHL," Operational Research, Springer, vol. 22(3), pages 2767-2783, July.
    5. Nikolaos Stavropoulos & Alexandra Papadopoulou & Pavlos Kolias, 2021. "Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    6. B. Jay Coleman, 2014. "Minimum violations and predictive meta‐rankings for college football," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(1), pages 17-33, February.
    7. Nicholas G. Hall & Chris N. Potts, 2012. "A Proposal for Redesign of the FedEx Cup Playoff Series on the PGA TOUR," Interfaces, INFORMS, vol. 42(2), pages 166-179, April.
    8. Fröhlich, Michael & Gassmann, Freya & Emrich, Eike (ed.), 2015. "Zur Strukturanalyse des Mehrkampfes in der Leichtathletik: Eine empirische Studie zum Zusammenhang von Leistung und Erfolg im Siebenkampf der Frauen und Zehnkampf der Männer," Schriften des Europäischen Instituts für Sozioökonomie e.V., European Institute for Socioeconomics (EIS), Saarbrücken, volume 11, number 11, July.
    9. Hirotsu Nobuyoshi & Wright Mike B, 2006. "Modeling Tactical Changes of Formation in Association Football as a Zero-Sum Game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(2), pages 1-22, April.
    10. David Bergman & Jason Imbrogno, 2017. "Surviving a National Football League Survivor Pool," Operations Research, INFORMS, vol. 65(5), pages 1343-1354, October.
    11. Yiannis Nikolaidis, 2015. "Building a basketball game strategy through statistical analysis of data," Annals of Operations Research, Springer, vol. 227(1), pages 137-159, April.
    12. Poojan Thakkar & Manan Shah, 2021. "An Assessment of Football Through the Lens of Data Science," Annals of Data Science, Springer, vol. 8(4), pages 823-836, December.
    13. Singh, Aaditya & Scarf, Phil & Baker, Rose, 2023. "A unified theory for bivariate scores in possessive ball-sports: The case of handball," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1099-1112.

    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:inm:oropre:v:57:y:2009:i:4:p:812-822. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.