||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.