The sport of professional cyclingCycling offers unique opportunities for coaches and athletes to analyze a wide variety of data in real time or retrospectively. Athletes can now be onboarded with instrumentation to measure a variety of metrics, and modern racing bicycles are also equipped with multiple sensors to better define both training and racing performance. Numerous websites also exist to collect and report race results, performances, and allow comparisons from all competitions and rider/team comparisons. These diverse tools provide a rich amount of data, which, combined with the advancement of data science, creates various opportunities for innovative projects. One such project is to use this information for better decision-making in both training and racing. This includes better matching of riders to the given profiles of competitions. Being able to make better decisions on race calendars and athlete selections will optimize opportunities for success. In this paper, we review the availability of data relevant to the field of professional cyclingCycling, discuss current cycling analytics studies, review existing relevant analytics frameworks, and finally, discuss what is expected next.