Visual Analytics for NASCAR Motorsports

Abstract The National Association of Stock Car Auto Racing (NASCAR) is consistently ranked among the top ten most popular sports in the United State1,2 . It is the most lucrative, generating an estimated $3 billion annually3 . Teams are financially well-supported and competitive in all areas where an advantage might be gained. NASCAR events are characterized by on-track racing punctuated by pit stops since cars must refuel, replace tires, and modify their setup throughout a race. A well-executed pit stop can allow drivers to gain multiple seconds on their opponents. Strategies around when to pit and what to perform during a pit stop are under constant evaluation. One currently unexplored area is publically available communication between each driver and their pit crew during the race. Due to the many hours of audio, manual analysis of even one driver’s communications is prohibitive. We propose a fully automated approach to analyze driver–pit crew communication. Our work was conducted in collaboration with NASCAR domain experts—a simulation manager, a crew chief, and a software engineering manager—to validate our findings. Audio communication is converted to text and summarized using cluster-based Latent Dirichlet Analysis (LDA) to provide an overview of a driver’s race performance. The transcript is then analyzed to extract important events related to pit stops and driving balance: whether a car is understeering (pushing) or oversteering (over-rotating). Named entity recognition (NER) and relationship extraction provide context to each event. A combination of the race summary, events, and real-time race data provided by NASCAR are presented using Sankey visualizations. Statistical analysis and evaluation by our domain expert collaborators confirmed we can accurately identify important race events and driver interactions, presented in a novel way to provide useful, 1 important, and efficient summaries and event highlights for race preparation and in-race decision-making.