2024 Lotus Emira V6 vs 2024 Nissan Z Nismo
AI Telemetry Verdict:In this head-to-head, the 2024 Lotus Emira V6holds the statistical edge in Performance Index (742). For the technical touge passes of Mount Fuji, the 2024 Lotus Emira V6is the superior technical chassis due to its refined lateral G-force profile.
2024 Lotus Emira V6
Lotus
2024 Nissan Z Nismo
Nissan"Analyzing the raw telemetry, the 2024 Lotus Emira V6 proves to be the more capable machine in all-around festival racing, outclassing the 2024 Nissan Z Nismo."
| 2024 Lotus Emira V6 | Metric | 2024 Nissan Z Nismo |
|---|---|---|
| 742 | Performance Index | 740 |
| 7.2 | Speed | 7.1 |
| 7.3 | Handling | 7.2 |
| 6.5 | Acceleration | 6.8 |
| 6.2 | Launch | 7 |
| 7.1 | Braking | 7.3 |
| 3 | Offroad | 3.5 |
| 180 | Top Speed (MPH) | 175 |
| 3100 | Weight (lbs) | 3704 |
| RWD | Drivetrain | RWD |
| 95,000 | Price (CR) | 65,000 |
📈 Technical Data Analysis:
Speed & Acceleration Analysis
When it comes to straight-line performance, the 2024 Lotus Emira V6 boasts a speed rating of 7.2, while the 2024 Nissan Z Nismo hits 7.1.
The 2024 Lotus Emira V6 has the edge in top-end velocity, reaching 180 MPH compared to the 2024 Nissan Z Nismo's 175 MPH.
Handling & Cornering Dynamics
In the tight technical sections of the Mount Fuji passes, handling is everything. The 2024 Lotus Emira V6 features a handling score of 7.3, whereas the 2024 Nissan Z Nismo manages 7.2.
The 2024 Lotus Emira V6 offers surgical precision in corners, allowing for later braking and earlier power application.
Launch & Braking Efficiency
Off the line, the 2024 Lotus Emira V6 uses its 6.2 launch rating to grip and go, while the 2024 Nissan Z Nismo relies on its 7 rating.
Braking from high speeds is equally critical; the 2024 Lotus Emira V6 stops with a score of 7.1, while the 2024 Nissan Z Nismo records 7.3.
🏁 Race Scenario Breakdown
Higher top speed rating allows for sustained high-velocity overtaking.
Superior braking and handling allow for more aggressive entry and exit speeds.
Suspension travel and tire compound optimization for loose surfaces.