Profiling NASCAR qualifying performance with functional data analysis

NASCAR is the premier American motorsport, with millions of fans who tune in each week to watch drivers compete in a test of skill, endurance, and above all, speed. As with any sport, winning is paramount, and prior research has focused on modeling driver and team characteristics, as well as strategies to optimize race-day performance. However, no prior work has investigated ways to improve driver qualifying performance, a key factor that would naturally improve the likelihood of achieving a strong finishing position in the race that follows. To address this gap, we analyze qualifying lap data from the NASCAR AdventHealth400 using functional principal components analysis followed by agglomerative hierarchical clustering. This allows us to uncover distinct groups of drivers and extract typical behavior. We identify several braking, throttle, and steering strategies that are differentially associated with qualifying performance. By isolating the highest-performing clusters, we offer actionable insights that can be used to enhance qualifying efforts and, ultimately, race-day results.