Formula 1
Analytics Platform
Tyre pit strategy prediction · Monte Carlo engine · 1 000 simulations per run
Laps
58
DRS Zones
4
SC Risk
40%
Select which seasons to include in the degradation and pit window models. 2026-only gives the freshest data but smallest sample.
Configure and run a simulation
Select a driver, circuit, starting compound, and pit stops — the optimal tyre strategy will appear here.
The simulator runs 1 000 independent race scenarios for your selected driver, circuit, and factor set. Each scenario draws inputs from probability distributions calibrated against historical data from the Jolpica F1 API. The pit window that appears most frequently as the optimal outcome across all scenarios becomes the predicted strategy.
On each simulated lap the engine applies tyre degradation (per-compound, per-team), fuel burn improvement, and probabilistic event checks. At each valid pit window it evaluates whether stopping delivers a better projected outcome than staying out. The year selector controls which historical seasons feed the degradation and pit timing models — 2026-only gives the latest picture, all 5 years gives the largest sample.
1 000
Simulations
10
Factors
24
Circuits
22
Drivers
Safety Car Risk
Jolpica APIDerived from 5 years of SC deployment records via Jolpica F1 API. At high-probability circuits (Singapore ≥60%), the model triggers a random SC window in ~60% of simulations, forcing reactive pit decisions.
Tyre Degradation
Historical LapsEach constructor has a calibrated degradation multiplier derived from historical stint data. A team known for high tyre stress (e.g. rear-limited car) will degrade faster per lap, tightening the natural pit window.
Fuel Load Effect
FIA RegsSimulates progressive lap-time improvement as the fuel load reduces (~0.3–0.4s per kg). A heavier car in Lap 1 runs slower, shifting undercut windows to later in the stint where the pace offset diminishes.
Weather Variability
Weather ModelIntroduces a low-probability random rainfall event mid-race. If triggered, forces an intermediate tyre stint and resets the dry compound strategy from that lap — effectively randomising the remaining stops.
Overtake Index
Circuit DataCircuits with 3+ DRS zones reduce the cost of losing track position, making 1-stop strategies viable. Street circuits with 1 DRS zone heavily penalise a late pit stop — the model adjusts pit priority accordingly.
Grid Position Impact
Jolpica APIJolpica race result data is used to compute a per-driver, per-circuit first-lap position delta distribution. Higher variance at street circuits and high-SC-probability tracks. Can justify an early stop if position is lost.
Rival Pit Windows
Historical DataPulls the median pit window for each of the top 5 competing teams at this circuit from historical data. When a rival is modelled to stop, the simulator evaluates whether an undercut response changes the optimal outcome.
Track Evolution
Lap Time ModelGrip improves lap-by-lap as rubber builds up on the racing line. Early stints on a green track are modelled as slower. This makes very early pit stops less effective and extends viable stint lengths in opening laps.
ERS Deployment Profile
2026 RegsModels the 2026 hybrid deployment limits (350kW MGU-K, 8MJ battery). Overtake Mode boost availability is tracked lap-by-lap. A depleted battery entering a DRS zone reduces pace, influencing optimal pit timing.
Virtual Safety Car
Jolpica APISeparately models VSC probability (historically higher frequency than full SC). A VSC window cuts pit-lane time loss by ~10s, making it a high-value opportunity for a net-zero-cost pit stop if timed correctly.
High
≥60% of 1 000 simulations agree on the same pit lap. Low variance across SC and degradation draws.
Medium
40–59% agreement. Some divergence driven by SC probability or compound sensitivity at this circuit.
Low
<40% agreement. Multiple viable strategy variants exist — treat as directional guidance only.
Jolpica F1 API
Race results, grid positions, lap times, safety car records
Pirelli Motorsport
Official compound allocation, degradation benchmarks, tyre life estimates
FIA Technical Regs
2026 power unit rules — ERS deployment limits, fuel flow maxima
Historical Race Data
5-season pit window timing distributions, constructor deg coefficients
Disclaimer: This simulator is for analytical and entertainment purposes. Degradation coefficients are estimated from publicly available historical race data via the Jolpica API. Safety car probabilities are based on historical averages per circuit from 2021–2026. Real race strategy is also influenced by live tyre temperature telemetry, pit crew performance, competitor reactions, weather developments, and car-specific characteristics that this model approximates statistically. The output represents the most-likely pit strategy based on historical patterns — not a guarantee of race outcomes.