There is no public ledger of restaurant covers, but there is a powerful proxy: reservation availability. When a standard prime-time table shows sold out across a run of dates, that scarcity signals demand - and capturing it means restaurant reservation data scraping of publicly observable availability.
This report uses consistent web scraping of public availability signals - a fixed party size and prime slot - to read relative dining demand across US cities, using no personal data. It is a model for anyone building an alternative data panel from public web data extraction.
Key findings at a glance
Three patterns stand out across the availability data. (Figures are illustrative previews - the full report breaks them down by city, day and cuisine.)
Key finding 1: prime-time scarcity ranks dining demand
Cities with consistently higher prime-slot sold-out rates show stronger relative dining demand. Used in aggregate, availability is a credible proxy for how hard a market is to book.
The discipline behind this is consistency: restaurant reservation data scraping only works as a signal when the party size and reference slot are held constant, so differences reflect demand, not method.
Key finding 2: day-of-week patterns are strong
Availability follows a weekly rhythm, and the pattern itself is informative. The sample shows sold-out rate by day (illustrative).
| Day | Prime sold-out rate | Signal |
|---|---|---|
| Friday | 68% | Peak demand |
| Saturday | 71% | Peak demand |
| Wednesday | 41% | Midweek baseline |
| Monday | 28% | Softest |
Holding the slot and party size constant across the week is what makes these comparisons valid - a restaurant-day panel with clean controls.
Key finding 3: availability is a proxy - read it honestly
Availability is not a headcount. A large venue may show open tables despite high demand; a small one may sell out with modest demand. The signal works in aggregate and over time, and analysis should control for capacity. Restaurant reservation data scraping captured consistently, with ratings and review counts as controls, supports rigorous designs - but the proxy's limits must be stated, not hidden.
What the underlying data looks like
The report is built from restaurant-day observations like the one below (availability: 1 = available, 0 = sold out) - the structure buyers receive in a sample.
{
"restaurant_id": "OT-NYC-4471",
"city": "New York", "zip": "10036", "cuisine": "fine_dining",
"observation_date": "2026-03-15",
"slot": "18:30", "party_size": 2,
"availability": 0,
"rating": 4.4, "review_count": 980,
"captured_at": "2026-02-01T09:00:00Z"
}
Aggregated to a city-and-day view, the data rolls up into a flat file analysts can model on:
city,day,prime_sold_out_rate,sample_restaurants
New York,Saturday,0.71,180
New York,Wednesday,0.41,180
Austin,Saturday,0.49,90
Denver,Saturday,0.38,70
Who this report is for
This report is built for the analytical teams that use dining demand signals and alternative data from restaurant reservation data scraping.
- Prime-slot sold-out rates by city
- Day-of-week and cuisine patterns
- Restaurant-day panel methodology
- Proxy limitations & controls
- Complete methodology, sample size and sources
Methodology & data
The findings are based on restaurant reservation data scraping of publicly observable availability - a fixed party size and prime slot - across US cities in 2026, structured as a restaurant-day panel with ratings and review counts as controls. It uses no personal data - only whether a standard table shows available or sold out. The full report details the cities, method and how each metric is calculated.
The numbers and charts shown on this page are illustrative previews of the kind of analysis in the report. They are based on publicly available, non-personal web data in aggregate and do not represent any single named company. The full report contains the complete dataset, methodology and sources.
Frequently asked questions
Yes. Enter your details and we will email you the PDF.
No. It uses only publicly observable availability (available vs. sold-out) for a fixed party size and time - never personal data.
They are illustrative previews of the report's analysis. The full PDF contains the complete dataset, methodology and sources.
Yes - structured for causal analysis with controls, built through consistent restaurant reservation data scraping.
Availability is a demand proxy, not a headcount; it works in aggregate and over time, and analysis should control for capacity.