Motorsport is one of the most competitive environments, in which small details can have a very significant impact on the final performance. In recent years data started to play a crucial role in almost every domain, and motorsport is no exception; every vehicle nowadays presents a wide variety of sensors to monitor different aspects during the race, with the purpose of enhancing the performance by applying data-driven methodologies at the cutting edge. Starting from telemetry data gathered during official competitions by 2WheelsPoliTO, a racing motorcycle university team from Polytechnic of Turin, we developed a complete pipeline for the analysis of telemetries with the purpose of extracting useful metrics and insights regarding the rider behavior and performance using Machine Learning algorithms, with a major focus on time series segmentation and clustering.