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What each online sportsbook and online casino took from players, state by state, as the state regulators publish it: New Jersey's internet gaming win and online sportsbook revenue by brand, New York's weekly mobile sports handle and gross gaming revenue by operator, and Massachusetts's handle, revenue and taxable revenue by operator. Pennsylvania publishes by casino licence, not by brand, so its rows are served only by the market-level gaming_revenue endpoint (see coverage).

A regulator prints these two to six weeks after the month, and New York every week, so DraftKings' and FanDuel's share of the two largest online states is public a quarter before either company's revenue line is. A brand maps to its listed parent: BetMGM to MGM Resorts and Entain in equal halves, FanDuel to Flutter, Caesars Sportsbook to Caesars; Fanatics, bet365 and Hard Rock have no listed parent and are left in the file under their own names.

Request it for one listing and you get every state ORTEX counts that operator in, each row naming its own state in market (US-NJ, US-NY, US-MA) and the brand in entityRef.

Available Data Points:

• Period Date: The period the value describes — what the number is about (the valid clock).
• Published At: When the value first became knowable — when the publisher released it (the knowledge clock). Filter on this for anything where look-ahead bias would matter.
• Value: The numeric value, where the series has one.
• Value Text: The textual value, for categorical series such as a trial's status or phase.
• Unit: Unit of the value, e.g. vehicles, percent, USD.
• Value Type: number, percent, currency, count or text.
• Frequency: How often the publisher releases the series.
• Market: The market the reading describes — an ISO-3166 alpha-2 code (DE), a sub-national one (US-CA), a supranational aggregate (EA, EU) or GLOBAL.
• Series Slug: Which series the reading belongs to. Unique within a publisher, not globally.
• Series Name: Human-readable name of the series.
• Source Slug: Publisher/feed identifier, e.g. kba_registrations.
• Source Name: Human-readable name of the publisher or feed.
• Publisher: Who publishes the underlying data.
• Entity Ref: The publisher's own identifier for what the reading is about — a manufacturer name, a securitisation trust, a reactor unit, a country code. Kept so any value can be traced back to the row it was parsed from.

Coverage

Four regulators: the New Jersey Division of Gaming Enforcement, the Pennsylvania Gaming Control Board, the New York State Gaming Commission and the Massachusetts Gaming Commission. Monthly, except New York, which is weekly and on a cash basis. New Jersey names brands from January 2025 and publishes no handle; New York runs from January 2022, Massachusetts from March 2023, and Massachusetts prints a losing month as zero.

Pennsylvania names casino licences rather than the online brands operating under them, so its rows are filed under the licence and not mapped to any company: FanDuel's Pennsylvania revenue sits under Boyd Gaming's Valley Forge licence, and mapping that licence to Boyd would hand Flutter's revenue to Boyd. No US-PA row is ever returned on this endpoint; Pennsylvania is read from the market-level gaming_revenue endpoint. Each state measures differently, so every state is its own series under its own market, and the states are never added together.

What to look for

Read a brand's share of its state, and how that share moves, rather than the dollar figure: the state totals the shares are taken against are served by the market-level gaming_revenue endpoint. New York and New Jersey together are a large part of every national operator's online revenue, so weight states by size when adding them up for a company.

Sportsbook revenue swings with game results, so a single week or month says more about sporting outcomes than about the business; handle is the steadier measure of activity where a state publishes it. Promotional credits separate the revenue a book took from the revenue it paid out to win customers.

Two dates on every reading, and the difference is the edge

periodDate is what the number is about. publishedAt is when it first became knowable. The gap between them is the whole point of alternative data — and it is also the trap. A Form 10-D describing July is filed in mid-August, so a backtest that treats the period as the knowledge date reads a document six weeks before it existed.

from_date and to_date filter on periodDate, because that is the axis you plot against. Join on publishedAt when you are joining against returns.

Filtering: one market or all of them

Called with no filters, the endpoint returns every series in the dataset, for every market ORTEX covers it in. Each row names its own market, seriesSlug and sourceSlug, so a single request is enough to chart the whole dataset.

Narrow it with any combination of market (one market), series (one measure) and source (one publisher). Filter values that are each valid but select nothing together return 400 invalidSelection rather than a misleading empty result.

Raw readings here; comparable ones next door

This endpoint serves the publisher's numbers. Raw values are frequently not comparable — receivables of $10.9bn against $4.9bn say nothing without each book's size, and a 5.3% charge-off rate means something different on a subprime book than a prime one. The Alt Data signals endpoint serves the same series as percentiles and z-scores against a stated comparison set, which is the form a screen or a backtest wants.

Why a company you expect may be missing

Three reasons, and none of them is a bug:

• The dataset is a window, not a census: Several of these publishers release a rolling window rather than a complete history.
• The filer is not the listed company: Registers name a marque, a charter or a trust. ORTEX maps those to the listed parent where the link is defensible and leaves them unmapped where it is not — a wrong mapping silently attributes another company's data to your ticker.
• The population is genuinely narrow: Not every listed company appears in every register, and no amount of mapping changes that.

How much history there is

Several of these publishers overwrite their own pages and keep no archive. Where that is so, the series begins on the day ORTEX started capturing it and lengthens from there — nobody, including the publisher, can supply the days before that. Everything ORTEX has ever fetched is archived on capture, so the series only ever grows.

Try it out! You can test the API with the trial key TEST. Simply include it in your request headers; either here, or add the header "Ortex-Api-Key": "TEST" to your http request.

Trial key restrictions: Trial keys may be limited to a specific date range and set of regions. Requests outside the allowed regions or entirely outside the allowed date range are rejected; a date window that partially overlaps the allowed range is trimmed to it, reported in meta.restriction.

Get full access! Get a key at app.ortex.com/apis