Methodology

How Trampoline Park Data measures demand

One written rulebook for every venue; a changed rule is a new model version.

Polling

Every park's public booking calendar is read every 15 minutes (about 95 times a day) for the days it offers ahead, until each session starts.
Each calendar is collected once, by a shared collection fleet that archives every response verbatim; Trampoline Park Data copies those archives byte for byte and never polls a park a second time.
Polls read public booking calendars with no logins and no private APIs; failed polls are kept.

A slot is one bookable start time with a capacity counter, in the venue’s own unit (an admission to a timed jump session or to an arrival hour).
Unavailable units U = capacity − remaining. A rise in U between polls within 20 minutes, at equal capacity, is a sale; a fall is a reopen; anything else is ambiguous.
Units gone at the first poll count toward the final state, not observed sales.
A final state counts only if seen within 20 minutes of the start; otherwise sold units are a modeled range up to capacity.

Utilization: Σ sold ÷ Σ capacity at entry, with coverage (share of capacity whose final state was seen).
Revenue (GBV): units sold × sourced prices, packages and memberships, as a log-normal estimate. Prices are never backdated.
Every estimate carries an 80 % interval (P10–P90) and an A–D grade for its width.
Markets: venue revenues add as independent estimates; utilization is unit-hour weighted; unlike units never add.
Next-day forecast: current bookings plus comparable slots’ pickup from the same lead time, withheld unless 80 % of open capacity has comparables.

Unknown is never zero: missing or conflicting observations widen intervals and lower coverage.
Raw is truth: responses are stored verbatim by SHA-256 hash and never edited; every figure is rebuilt from them.
Models are versioned and proven against each venue’s raw data; released versions are frozen.