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The dynamic of bicycle finals: A theoretical and empirical analysis of slipstreaming

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  • Dilger, Alexander
  • Geyer, Hannah

Abstract

The finals of bicycle races have certain peculiarities compared to other sports. The leading group in a bicycle race rides comparatively slowly up to a few meters before the finishing line, until one of the competitors tries to shake off his opponents. Only then do all riders perform to the limit. This raises the question of who takes the thankless early lead and why. The rider who is in front just before the final sprint is seldom the one who wins in the end. By means of the relevant physics it can be shown theoretically that on the one hand the better rider will always be able to win the race and, more surprisingly on the other hand, the better rider will definitely be the rider in the slipstream. These findings are confirmed empirically by means of several logistic regressions. 49 final sprints of road races between two up to seven professional racing cyclists with varying performance potentials were analyzed concerning the order of the riders at the beginning of the final sprint and the final outcome of the race. Subsequently, possibilities for further research and implications for sport economics are described.

Suggested Citation

  • Dilger, Alexander & Geyer, Hannah, 2009. "The dynamic of bicycle finals: A theoretical and empirical analysis of slipstreaming," IÖB-Diskussionspapiere 4/09, University of Münster, Institute for Economic Education.
    • Handle: RePEc:zbw:ioebdp:409
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    Cited by:

    1. Nicolas Scelles & Jean-François Mignot & Benjamin Cabaud & Aurélien François, 2018. "Temporary organizational forms and coopetition in cycling," Post-Print hal-02366610, HAL.
    2. Brouwer, Thijs & Potters, Jan, 2019. "Friends for (almost) a day: Studying breakaways in cycling races," Journal of Economic Psychology, Elsevier, vol. 75(PB).
    3. César Rodríguez & Levi Pérez & Víctor Puente & Plácido Rodríguez, 2015. "The Determinants of Television Audience for Professional Cycling," Journal of Sports Economics, , vol. 16(1), pages 26-58, January.
    4. Travis J. Lybbert & Troy C. Lybbert & Aaron Smith & Scott Warren, 2012. "Does the Red Flag Rule Induce Risk Taking in Sprint Finishes? Moral Hazard Crashes in Cycling’s Grand Tours," Journal of Sports Economics, , vol. 13(6), pages 603-618, December.
    5. Trenchard, Hugh & Ratamero, Erick & Richardson, Ashlin & Perc, Matjaž, 2015. "A deceleration model for bicycle peloton dynamics and group sorting," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 24-34.

    More about this item

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

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