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Empirical Analysis of Pacing in Road Cycling

Published: 15 October 2019 Publication History

Abstract

The pacing profile adopted throughout a competitive time trial may be decisive in the overall outcomes of the event. Riders distribute their energy resources based on a range of factors including prior experience, perception of effort, knowledge of distance to cover and potential motivation. Some athletes and professional cycling teams may also quantify individual pacing strategies derived from computational scientific methods. In this work we collect and analyze data of self-selected individual pacing profiles from approximately 12,000 competitive riders on a well-known hill climbing road segment in the Adelaide Hills, South Australia. We found that riders chose from a variety of very different pacing profiles, including some opposing profiles. For the classification of pacing this paper describes the pipeline of collection GPS-based and time stamped performance data, data filtering, augmentation of road gradient and power values, and the classification procedure.

References

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Thorsten Dahmen, Roman Byshko, Dietmar Saupe, Martin Röder, and Stephan Mantler. 2011. Validation of a model and a simulator for road cycling on real tracks. Sports Engineering, Vol. 14, 2--4 (2011), 95--110.
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Arie-Willem de Leeuw, Laurentius A. Meerhoff, and Arno Knobbe. 2018. Effects of Pacing Properties on Performance in Long-Distance Running. Big Data, Vol. 6, 4 (2018), 1--14.
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Ryszard Maro'nski. 1994. On optimal velocity during cycling. Journal of Biomechanics, Vol. 27, 2 (1994), 205--213.
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James C Martin, Douglas L Milliken, John E Cobb, Kevin L McFadden, and Andrew R Coggan. 1998. Validation of a mathematical model for road cycling power. Journal of Applied Biomechanics, Vol. 14, 3 (1998), 276--291.
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Strava. 2019. Norton Summit road cycling segment, Magill, SA, Australia. https://www.strava.com/segments/1013950 [Online; accessed 20-June-2019].

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        cover image ACM Conferences
        MMSports '19: Proceedings Proceedings of the 2nd International Workshop on Multimedia Content Analysis in Sports
        October 2019
        120 pages
        ISBN:9781450369114
        DOI:10.1145/3347318
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        New York, NY, United States

        Publication History

        Published: 15 October 2019

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        Author Tags

        1. data driven performance analysis
        2. datasets

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        Overall Acceptance Rate 29 of 49 submissions, 59%

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