Abstract
With the recent explosive developments in sensoring capabilities and ubiquitous computing in road cycling, large quantities of detailed data about performance are becoming available. In this paper, we will demonstrate that this rich data in cycling offers several non-trivial data science challenges. The primary task that we focus on is a regression task: given a collection of results in previous races of a specific rider, predict the performance in a future race solely based on the characteristics of said rider and the stage profile. To make these predictions, we have developed a predictive pipeline that consists of three consecutive rider-specific models. First, we transform the distance-altitude profile into a time profile, by using a climb-descent model that describes the relationship between the speed of the cyclist and the slope of the terrain. Second, we introduce an effective profile that includes the rider-specific physiological capabilities. Third, we predict the performance based on the characteristics of the effective profile, by using a model constructed from the historical records of our cyclist. To demonstrate the relevance of this work, we show that for a professional cycling team, important information for making tactical decisions can be obtained from our modeling approach.
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Notes
- 1.
The most important stage races: Tour de France, Giro d’Italia and Vuelta a España.
- 2.
This specific information is only available at the end of the race, which makes our analysis a post-hoc one.
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de Leeuw, AW., Heijboer, M., Hofmijster, M., van der Zwaard, S., Knobbe, A. (2020). Time Series Regression in Professional Road Cycling. In: Appice, A., Tsoumakas, G., Manolopoulos, Y., Matwin, S. (eds) Discovery Science. DS 2020. Lecture Notes in Computer Science(), vol 12323. Springer, Cham. https://doi.org/10.1007/978-3-030-61527-7_45
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