Race car drivers can offer insights into vehicle control during extreme manoeuvres; however, little data from race teams is publicly avail- able for analysis. The Revs Program at Stanford has built a collection of vehicle dynamics data acquired from vintage race cars during live racing events with the intent of making this database publicly available for future analysis. This paper discusses the data acquisi- tion, post-processing, and storage methods used to generate the database. An analysis of available data quantifies the repeatability of professional race car driver performance by examining the sta- tistical dispersion of their driven paths. Certain map features, such as sections with high path curvature, consistently corresponded to local minima in path dispersion, quantifying the qualitative concept that drivers anchor their racing lines at specific locations around the track. A case study explores how two professional drivers employ dis- tinct driving styles to achieve similar lap times, supporting the idea that driving at the limits allows a family of solutions in terms of paths and speed that can be adapted based on specific spatial, temporal, or other constraints and objectives.