1992 Honda NSX-R vs 2013 Ford Shelby GT500
AI Telemetry Verdict:In this head-to-head, the 2013 Ford Shelby GT500holds the statistical edge in Performance Index (745). For the technical touge passes of Mount Fuji, the 1992 Honda NSX-Ris the superior technical chassis due to its refined lateral G-force profile.
1992 Honda NSX-R
Honda
2013 Ford Shelby GT500
Ford"Analyzing the raw telemetry, the 2013 Ford Shelby GT500 proves to be the more capable machine in all-around festival racing, outclassing the 1992 Honda NSX-R."
| 1992 Honda NSX-R | Metric | 2013 Ford Shelby GT500 |
|---|---|---|
| 710 | Performance Index | 745 |
| 6.7 | Speed | 7.6 |
| 7.2 | Handling | 6.3 |
| 6.2 | Acceleration | 6.4 |
| 5.9 | Launch | 6.6 |
| 6.8 | Braking | 6.5 |
| 3.5 | Offroad | 4 |
| 168 | Top Speed (MPH) | 185 |
| 2712 | Weight (lbs) | 3850 |
| RWD | Drivetrain | RWD |
| 120,000 | Price (CR) | 55,000 |
📈 Technical Data Analysis:
Speed & Acceleration Analysis
When it comes to straight-line performance, the 1992 Honda NSX-R boasts a speed rating of 6.7, while the 2013 Ford Shelby GT500 hits 7.6.
The 2013 Ford Shelby GT500 pulls ahead in long stretches, making it a formidable opponent on the Tokyo highways.
Handling & Cornering Dynamics
In the tight technical sections of the Mount Fuji passes, handling is everything. The 1992 Honda NSX-R features a handling score of 7.2, whereas the 2013 Ford Shelby GT500 manages 6.3.
The 1992 Honda NSX-R offers surgical precision in corners, allowing for later braking and earlier power application.
Launch & Braking Efficiency
Off the line, the 1992 Honda NSX-R uses its 5.9 launch rating to grip and go, while the 2013 Ford Shelby GT500 relies on its 6.6 rating.
Braking from high speeds is equally critical; the 1992 Honda NSX-R stops with a score of 6.8, while the 2013 Ford Shelby GT500 records 6.5.
🏁 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.