System_Calculus

// LOGIC_DOCUMENTATION_V3.1
[01_CORE_OBJECTIVE]

Algorithmic
Neutrality

Opticars formulas are designed to isolate performance vectors from marketing noise. By normalizing disparate units (Force, Mass, Currency) into a dimensionless 0-1 range, we achieve absolute comparison parity.

VARIABLE_DEFINITIONS
[HP] Horsepower [W] Weight (KG) [€] Market Price [G] Lateral G-Force [A] 0-100 km/h (sec) [B] 100-0 km/h (m) [S] Top Speed (km/h) [Cx] Drag Coefficient [Cons] Fuel (L/100km) [Rel] Reliability (1-5) [Boot] Boot Capacity (L)
[ACTIVE_MATRICES_V2]
[SCORE_01]

Pure Performance

f(x) = ((HP/W)*100) * (S/100) * (G/0.85) * (8/A) * (38/B)

The ultimate measurement of physical capability. Neutralizes weight and rewards efficiency across all dynamic vectors.

[SCORE_02]

Bang For Buck

f(x) = (Performance * 10,000) / €

The Great Equalizer. Calculates raw performance points delivered per 10,000 units of currency.

[SCORE_03]

Trackday Potential

f(x) = (HP / (W/1000)1.2) * G2 * (38/B) * (0.32/Cx)

Aggressively penalizes mass and rewards cornering stability (G²) and aerodynamic efficiency (Cx).

[SCORE_04]

Daily Commuter

f(x) = (6/Cons) * (Rel/5) * (15k/€) * (Boot/350) * 100

Focuses on economic sustainability, reliability, and practical utility for everyday life.

[SCORE_05]

Family Trip Score

f(x) = (Boot/400) * (Seats/5) * (Rel/5) * (7/Cons) * (Tank/50) * 100

Prioritizes volume (Boot & Seats), range (Tank Size), and reliability for long-distance comfort.

[DYNAMIC_SORTING_ENGINE]

How /cars Sorting Works

STEP_01 // DYNAMIC_BOUNDING

The engine first scans the *current results* of your search to find the minimum and maximum values for every attribute. Your score is relative to the competition in your view.

STEP_02 // UNIT_NORMALIZATION

Every variable (HP, Price, Cons) is converted to a 0-1 range.
score = (value - min) / (max - min)

STEP_03 // DIRECTIONAL_POLARITY

The engine automatically detects if "Higher is Better" (e.g. HP) or "Lower is Better" (e.g. Price). If lower is better, the score is inverted (1 - score).

STEP_04 // WEIGHTED_AVERAGE

Finally, the engine applies your custom weights from the sliders. Each normalized score is multiplied by your weight, summed, and averaged to produce the final sort order.