The Datum Grade™ Methodology

The model in one paragraph

Each category gets a set of measurable specs. We reduce every spec to a single number, then normalise it onto a 0–100 scale against fixed per-category bounds — respecting whether higher or lower is better, because a 32 mm cartridge clearance and a $2,149 price tag are both “big” in very different ways. Each normalised value gets multiplied by a published weight. Average the weighted values, and that’s the score. 0 to 100, higher is better. That’s the whole machine.

The formula

For a numeric spec with raw value x, bounds min and max(the ratio is clamped to the 0–1 range, so values outside the bounds score 0 or 100):

• Higher-is-better: quality = clamp₀₋₁((x − min) ÷ (max − min)) × 100
• Lower-is-better: quality = 100 − clamp₀₋₁((x − min) ÷ (max − min)) × 100
• Ranges(e.g. a cassette’s 11–34T spread) are reduced to a scalar first — the midpoint, span, or upper bound, as published per spec below.
• Categorical specs (e.g. shifting: mechanical vs electronic) map to a published 0–100 quality value per option.

The overall Datum Grade is the weight-normalised average: Σ(qualityᵢ × weightᵢ) ÷ Σ(weightᵢ), computed over the specs that have data.

Three rules that keep the score honest

• Missing data is never treated as zero.If a manufacturer doesn’t publish a spec, we store it as unknown, exclude it, and renormalise the remaining weights — we never guess. The honest consequence: a score built on fewer specs leans harder on the specs that remain, which is why the data note under each product’s table tells you exactly what was unavailable.
• Reference-only specs are excluded.Some published figures aren’t comparable across designs (e.g. a Bosch mid-drive’s rated motor torque vs a hub motor’s, which aren’t measured the same way). These are shown in tables but never scored.
• Commissions never touch the math. Project Gunner runs no affiliate links or ads today, and if that ever changes the score will still be computed from specs by the formula above — there is no editorial override, no sponsored placement, and no manual adjustment. Ever.

Avatar re-weighting (the “Best for…” lists)

A beginner and a weight-obsessed competition racer shouldn’t use the same weights. So they don’t. Our buyer-specific lists take the same normalised data and re-weight it for one kind of shooter — the beginner list, for instance, cares far more about stable handling and price than outright low weight, because a twitchy, aggressive race rifle helps nobody who’s still building confidence in the stock. The weights for each avatar are published on its list page. The underlying data never changes.