From Data Analysis to On-Track Performance: Optimizing Charles Leclerc's Strategy for a Top 5 Result at the 2025 Chinese GP via Simulation

Pit stop strategies are decisive in Formula 1, a high-tech sport where strategic mistakes still impact results. This study optimizes, via data-driven simulation, Charles Leclerc's one-stop (Medium-Hard) strategy at the 2025 Chinese Grand Prix, where his original fifth place was lost by disqualification. Modelling the linear degradation of the tyres, we compared the projection for the M15->H41 strategy (used by the driver) with alternatives. The model identified an optimized strategy (M10->H46) as 3.904 seconds faster. Applying this gain to Leclerc's actual performance, it is projected that he would secure the fifth final position, adding points. The work demonstrates how accessible simulation models can provide valuable strategic insights to maximize performance, even in technologically advanced contexts where decision-making remains a challenge.