By developing a unique ratio based on various performance variables, the square of natural log of the power to weight to acceleration ratio, \(\left(\ln\!\left(\frac{\text{horsepower}}{\text{weight} \times \text{acceleration}}\right)\right)^2\) and mapping it against lap time. The final cubic regression model achieved an R² of 0.9053 across the dataset of 530 vehicles, ranging from real-legal compacts up to dedicated time attack race cars. The model predicts unseen Nordschleife lap times to within 14.2 seconds on average.