TY - GEN
T1 - Time Series Regression in Professional Road Cycling
AU - de Leeuw, Arie Willem
AU - Heijboer, Mathieu
AU - Hofmijster, Mathijs
AU - van der Zwaard, Stephan
AU - Knobbe, Arno
N1 - Publisher Copyright: © 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Predictive modeling
KW - Temporal data mining
KW - Time series regression
UR - http://www.scopus.com/inward/record.url?scp=85094145385&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-030-61527-7_45
DO - https://doi.org/10.1007/978-3-030-61527-7_45
M3 - Conference contribution
SN - 9783030615260
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 689
EP - 703
BT - Discovery Science - 23rd International Conference, DS 2020, Proceedings
A2 - Appice, Annalisa
A2 - Tsoumakas, Grigorios
A2 - Manolopoulos, Yannis
A2 - Matwin, Stan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Discovery Science, DS 2020
Y2 - 19 October 2020 through 21 October 2020
ER -