2026 ASEE Annual Conference & Exposition

Reliability is a foundational concept in engineering, defined as the probability that a system performs its intended function under specified conditions for a given period of time. This study extends that principle to the competitive and stochastic environment of Formula 1 racing, employing a data-driven visualization framework to examine performance stability across teams and drivers. The analysis focuses on reformulating classical reliability metrics to characterize consistency in race outcomes over extended operational periods. The dataset spans the 2010–2024 Formula 1 seasons, corresponding to the post-regulatory era that standardized vehicle architectures and power-unit designs, thus providing a consistent basis for longitudinal reliability assessment. Rather than adopting predictive modeling, which is often impractical due to the extensive number of non-controllable parameters such as weather variability, mechanical failures, and unpredictable race events, this work emphasizes interpretability through visual analytics. Reliability indicators, including positional variance, finishing consistency, and the probability of maintaining or improving qualifying position, are computed through Python-based preprocessing and analytical routines. The resulting data are visualized in Tableau using interactive dashboards that integrate reliability trend lines, heat maps of cumulative points for constructors and drivers, and grid-based probability matrices illustrating positional stability. By transforming a deterministic engineering metric into an interpretable visual analytic framework, this study demonstrates how reliability can serve as a quantitative and explanatory measure for complex, dynamic systems. The approach offers a straightforward method for evaluating performance consistency and highlights the importance of visualization as a rigorous and indispensable tool in data-driven engineering analysis.