Simulation tools are essential for designing and testing car setups in modern racecar competitions: state-of-the-art full-body simulators replicate driving conditions, but they require detailed tuning (alignment) of hundreds or thousands of parameters to reduce the reality gap. This procedure is performed by comparing simulations with real-world data (Formula 1 teams collect data in the order of terabytes every race weekend), most often visually, resulting in a time-consuming operation that requires advanced expertise. Additionally, the process is not formally encoded, relies on human intuition and expertise, and thus results are highly subjective and may vary depending on who is performing the operation. In this paper, we present an automated pipeline for parameter tuning in full-body racing simulators. This pipeline replicates the manual tuning workflow, but substituting subjective visual comparisons with an objective cost function—the Residual Sum of Squares (RSS). We validate the proposed approach by comparing the quality of the alignment and the time required to achieve it between experts performing manual tuning and the proposed automated pipeline. We find that automating the process requires a similar number of simulations to be performed compared to manual tuning, but the automated pipeline is significantly faster and more consistent.