In this thesis, a telemetry-informed race simulator for Formula One is developed to investigate the impact of strategic decisions and performance variability across full race distances. Lap time evolution is modelled as the sum of deterministic and stochastic components, calibrated using historical data and telemetry-derived parameters for fuel consumption, tire wear, driver lap time consistency and pit stop losses. Monte Carlo methods are employed to simulate multiple race iterations, reflecting the randomness inherent in motorsport events such as Full Course Yellow phase, mechanical failures and overtaking maneuvers. This probabilistic modelling enables the study of not just expected results but also the confidence intervals and failure risks associated with different strategies.