Athlete monitoring in professional road cycling using similarity search on time series data - de Leeuw, A. W.; Oberkofler, T.; Heijboer, M.; Knobbe, A. (Machine Learning and Data Mining for Sports Analytics. MLSA 2022. Communications in Computer and Information Science, 2022)
In sports, athlete monitoring is important for preventing injuries and optimizing performance. The multitude of relevant factors during the exercise sessions, such as weather conditions, makes proper individual athlete monitoring labour intensive. In this work, we develop an automated approach for athlete monitoring in professional road cycling that takes into account the terrain on which the ride is executed by finding segments with similar elevation profiles. In our approach, the matching is focused on the shapes of the segments. We use 2.5 years of data of a single rider of Team Jumbo-Visma and assess the performance of our approach by determining the quality of the best matches for a selection of 700 distinct segments, consisting of the most representative shapes for the elevation profiles. We demonstrate that the execution time is within seconds and more than ten times faster than exhaustive search. Therefore, our method enables real-time deployment in large scale applications with potentially many requests from multiple users. Moreover, we show that on average our approach has similar accuracy when considering the correlation to a target segment and approximately only has a twice as large mean squared error when compared to exhaustive search. Finally, we discuss a practical example to demonstrate how our approach can be used for athlete performance monitoring.