Optimizing Sim Racing Performance Using Machine Learning and Evolutionary Algorithms

The ideal racing line is a key determinant of lap-time performance, defining the trajectory that minimizes time while maximizing vehicle stability and control. Despite the rise of esports, methods for systematically analysing and optimizing driver performance in sim racing remain limited. This study demonstrates how the integration of machine learning (ML) and evolutionary algorithms (EA) can identify critical telemetry metrics and guide performance optimization. Using a professional racing simulator and MoTeC i2 Pro, telemetry data were collected from 135 participants who completed 1,180 laps on the Laguna Seca circuit in Assetto Corsa Competizione (v1.9). Laps were clustered by performance, and a hybrid feature-selection approach combining correlation analyses and ML models identified the top metrics predictive of lap times, including speed, trailbraking duration, steering angle, oversteer, and lane deviation. These metrics were incorporated into an EA fitness function to optimize sector-level KPIs and generate idealized laps. The EA converged rapidly, achieving substantial reductions in predicted lap times within the first 50 generations and producing smoother, more stable, and faster laps than the human best. Lane deviation, oversteer, trail braking duration, and longitudinal acceleration showed the largest improvements. The EA-optimized laps offer actionable insights for high-performance driving, demonstrating measurable gains in speed, control, and stability, and providing practical guidance for driver coaching and performance enhancement in sim racing.