Methodology

Limitations — what this score is, and is not

The score is a published policy applied to documented evidence — a weighted average with disclosed, editable parameters — not an objective measurement of which rocket is best. That distinction is standard in decision science, and it has concrete consequences we'd rather you read here than discover:

• — Strengths can buy back weaknesses. A weighted sum is compensatory: a terrible value on one metric can be offset by excellence elsewhere. If something is a hard requirement for you, don't read the ranking — use the filters or the decide tool, which fails an item outright on an unmet or unknown requirement.
• — Scores are relative to today's cohort. Every metric is scaled to the current min and max, so adding or removing one rocket can shift — and occasionally reorder — the scores of rockets whose data never changed. A single extreme value compresses everyone else on that metric (one free entry, for instance, flattens price differences among paid ones). For the same reason, scores aren't comparable across time or across workspaces: a 78 today and a 78 last month were measured against different cohorts. Which items we include is an editorial decision, and it moves scores.
• — Default weights are a starting point, not the truth. They originate from AI analysis and editorial review — not from elicited preferences of any user, and not validated by independent domain experts. A weight also says nothing about spread: a heavily weighted metric on which every rocket is nearly identical contributes almost nothing in practice. The published ranking is one defensible weighting among many — the what-if weights panel exists so you can substitute yours, and the winner-flip analysis shows how close the call is.
• — A low score can mean bad data, not a bad rocket. Confidence and freshness multiply the score down, so poorly documented merit and well-documented mediocrity can land on similar numbers. The per-item breakdown shows which is which. The confidence multipliers themselves (1.00 down to 0.80) express an editorial ordering, not measured error rates; they average across metrics regardless of weight; and a value labeled stale can be discounted twice — once by its label, once by the freshness decay. The 15% freshness penalty and its 2× window are policy constants, not estimated decay rates.
• — Missing data is handled by policy, and each policy has a cost. Under penalize, the score measures documented merit — an under-documented rocket ranks low even if it's excellent (deliberate, to reward disclosure). Under exclude, items are effectively scored on different metric subsets, so two scores may summarize different questions. Under insufficient-data, coverage below half the total weight means unranked — a hard line, not a gradient.
• — Bands are display buckets. Strong / fair / weak cut the cohort-relative score at fixed thresholds (70 / 40). "Strong" means strong within this cohort under this methodology — not against any external standard, and a rocket can change band because a rival entered the cohort.
• — The math makes simplifying assumptions. Adding normalized metrics assumes they contribute independently to overall value (interactions between metrics aren't modeled) and that the normalization curves — linear, log, inverse — are reasonable shapes, which is a modeling judgment, not an elicited fact. The winner-flip sensitivity is computed under a simplified linear model and locates flips to the nearest 5 percentage points, so it can differ slightly from the published configuration.

Why publish a single number at all? Because a transparent, reproducible, versioned policy you can inspect, contest, and re-weight beats an opaque one — not because any single number can settle what "best" means. Everything above is why the reproduce widget, the per-value provenance, and the editable weights exist.