2026 QEIv18™ Championship
QEIv18™ Championship 2026
Drivers Ranking
A physics-based performance ranking derived from race-state dynamics across the 2026 season. The QEIv18™ framework measures field control, stability, peak capability, and competitive conversion to identify the real performance hierarchy of the grid.
Built on QEIv18™ by NeoAmorfic™. This championship view is part of the broader NeoAmorfic™ intelligence ecosystem.
Season ranking
Top 10 drivers in the QEIv18™ Championship 2026.
Season-wide structural ranking
Drivers ordered by underlying competitive architecture across completed rounds.
| Physics rank | Driver | Team | Races | QEIv18™ score |
|---|---|---|---|---|
| 1 | ANT | Mercedes | 5 | 80.20 |
| 2 | RUS | Mercedes | 4 | 79.33 |
| 3 | HAM | Ferrari | 5 | 76.33 |
| 4 | NOR | McLaren | 3 | 72.58 |
| 5 | LEC | Ferrari | 5 | 70.92 |
| 6 | PIA | McLaren | 3 | 68.90 |
| 7 | GAS | Alpine | 4 | 63.46 |
| 8 | VER | Red Bull Racing | 5 | 63.25 |
| 9 | HAD | Red Bull Racing | 3 | 62.13 |
| 10 | BEA | Haas F1 Team | 4 | 61.79 |
Showing top 10. Races shows the number of scored race-state samples included in the season mean.
Race-level snapshots
Example event-level rankings feeding the championship structure.
Australia 2026
Race-level physics ranking for Australia.
| Physics rank | Driver | Team | QEIv18™ score |
|---|---|---|---|
| 1 | RUS | Mercedes | 88.56 |
| 2 | ANT | Mercedes | 82.39 |
| 3 | HAM | Ferrari | 80.89 |
| 4 | VER | Red Bull Racing | 75.70 |
| 5 | LEC | Ferrari | 75.50 |
| 6 | NOR | McLaren | 71.58 |
| 7 | BEA | Haas F1 Team | 70.24 |
| 8 | LIN | Racing Bulls | 66.89 |
| 9 | BOR | Audi | 65.62 |
| 10 | OCO | Haas F1 Team | 59.09 |
Showing top 10.
China 2026
Race-level physics ranking for China.
| Physics rank | Driver | Team | QEIv18™ score |
|---|---|---|---|
| 1 | ANT | Mercedes | 84.94 |
| 2 | RUS | Mercedes | 84.40 |
| 3 | HAM | Ferrari | 78.21 |
| 4 | LEC | Ferrari | 77.45 |
| 5 | GAS | Alpine | 65.47 |
| 6 | BEA | Haas F1 Team | 62.87 |
| 7 | HAD | Red Bull Racing | 54.72 |
| 8 | LIN | Racing Bulls | 39.84 |
| 9 | LAW | Racing Bulls | 39.72 |
| 10 | BOT | Cadillac | 34.91 |
Showing top 10.
Japan 2026
Race-level physics ranking for Japan.
| Physics rank | Driver | Team | QEIv18™ score |
|---|---|---|---|
| 1 | ANT | Mercedes | 83.55 |
| 2 | RUS | Mercedes | 77.40 |
| 3 | PIA | McLaren | 70.53 |
| 4 | HAM | Ferrari | 70.11 |
| 5 | VER | Red Bull Racing | 69.88 |
| 6 | LEC | Ferrari | 69.32 |
| 7 | GAS | Alpine | 67.82 |
| 8 | NOR | McLaren | 66.52 |
| 9 | HUL | Audi | 59.45 |
| 10 | LAW | Racing Bulls | 57.99 |
Showing top 10.
Miami 2026
Race-level physics ranking for Miami.
| Physics rank | Driver | Team | QEIv18™ score |
|---|---|---|---|
| 1 | NOR | McLaren | 79.63 |
| 2 | PIA | McLaren | 77.13 |
| 3 | ANT | Mercedes | 75.10 |
| 4 | COL | Alpine | 69.49 |
| 5 | RUS | Mercedes | 66.96 |
| 6 | HAM | Ferrari | 63.73 |
| 7 | SAI | Williams | 61.52 |
| 8 | ALB | Williams | 61.00 |
| 9 | VER | Red Bull Racing | 60.40 |
| 10 | LEC | Ferrari | 58.07 |
Showing top 10.
Canada 2026
Race-level physics ranking for Canada. Hamilton and Verstappen nearly tie at event-score level, while Antonelli remains the strongest raw-control diagnostic case.
| Physics rank | Driver | Team | QEIv18™ score |
|---|---|---|---|
| 1 | HAM | Ferrari | 88.70 |
| 2 | VER | Red Bull Racing | 88.67 |
| 3 | HAD | Red Bull Racing | 81.37 |
| 4 | ANT | Mercedes | 75.00 |
| 5 | LEC | Ferrari | 74.27 |
| 6 | COL | Alpine | 71.73 |
| 7 | LAW | Racing Bulls | 67.48 |
| 8 | GAS | Alpine | 61.75 |
| 9 | BEA | Haas F1 Team | 60.33 |
| 10 | SAI | Williams | 60.08 |
Showing top 10.
Why this matters
Championship intelligence should show more than points accumulation.
Structural reality
The championship reveals underlying competitive order, not only visible table position.
Pressure conversion
It helps identify which drivers convert control and stability into actual race outcomes.
Decision support
It supports executive review, performance assessment, and future live intelligence systems.
Additional modules
Further championship layers will be added as the season expands, including driver archetypes, constructor structure, and structural-result divergence.
Constructor structure
Team-level structural ranking derived from race-state performance architecture.
Coming soon
Driver archetype evolution
Race-by-race evolution of each driver across control, conversion, volatility, peak aggression, and championship sustainability.
Coming soon
Structural vs result divergence
Where finishing order, event-score conversion, and raw structural control disagree — the premium diagnostic layer.
Coming soon