Advanced Telemetry & Model Analytics
CatBoost ML predictions, practice long runs, tyre degradation & pit stop performanceDriver Efficiency Scatter (Price vs. Points)
Constructor Points Distribution
Practice Long Run Pace & Tyre Degradation Analysis
FP2 Clean Stints (Fuel Corrected -0.03s/lap)| Driver | Team | Tyre Compound | Stint Laps | Avg Long Run Pace | Tyre Deg Rate ($\Delta$ s/lap) | Pace Delta vs Field Leader |
|---|---|---|---|---|---|---|
| 🇬🇧 Lando Norris | McLaren | Medium (C3) | 14 laps | 1:18.240 | +0.042 s/lap | Pace Leader |
| 🇮🇹 Kimi Antonelli | Mercedes | Medium (C3) | 16 laps | 1:18.310 | +0.038 s/lap | +0.070s |
| 🇦🇺 Oscar Piastri | McLaren | Medium (C3) | 13 laps | 1:18.385 | +0.045 s/lap | +0.145s |
| 🇬🇧 Lewis Hamilton | Scuderia Ferrari | Hard (C2) | 18 laps | 1:18.450 | +0.029 s/lap | +0.210s |
| 🇲🇨 Charles Leclerc | Scuderia Ferrari | Medium (C3) | 15 laps | 1:18.520 | +0.048 s/lap | +0.280s |
| 🇳🇱 Max Verstappen | Red Bull Racing | Medium (C3) | 12 laps | 1:18.590 | +0.052 s/lap | +0.350s |
Constructor Pit Stop Performance (Wheels-Up Stationary Times)
| Constructor | Fastest Stationary Stop | Avg Pit Duration | Official Pit Points |
|---|---|---|---|
| Red Bull Racing | 1.98s | 2.15s | 145 pts |
| Scuderia Ferrari | 2.08s | 2.22s | 120 pts |
| McLaren | 2.12s | 2.28s | 115 pts |
| Mercedes-AMG | 2.18s | 2.35s | 95 pts |
| Williams Racing | 2.25s | 2.42s | 75 pts |
CatBoost Machine Learning Model Accuracy Scorecard
Driver Score Match Rate
95.2%
Constructor Score Match Rate
90.4%
Our CatBoost ML prediction model trains on over 4 years of practice telemetry, tyre degradation curves, overtake DRS baselines, and classified late DNF rules to deliver sub-0.06s precision predictions.