Ticket data extraction, at the depth the market actually trades at
Every listing on every event, across TickPick, StubHub, Ticketmaster and SeatGeek. Section, row, quantity, fees and the price a buyer really pays, collected on a schedule and delivered in your schema.
Read off a live pipeline's transfer manifest, not estimated.
Ticket prices are not a number, they are a distribution that changes hourly
A single popular event carries thousands of live listings, each with its own section, row, quantity, delivery method and fee treatment. The cheapest seat moves several times a day. Sellers reprice against each other continuously. Nothing about that is captured by an average, and none of it can be reconstructed after the fact, because listing history is not published anywhere. If it was not collected that day, it does not exist.
- The advertised price and the paid price differ, by a margin that varies per site and per listing
- The same physical seat can appear on three marketplaces at three prices
- Marketplaces title the same event differently, so matching on name silently mismatches events
- Listing pages paginate deeply, and stopping early biases every figure toward the cheap end
- On-sale windows produce most of the interesting movement and last minutes, not days
Full depth, every event, on a schedule, with the fees kept separate
We take the complete listing table rather than the first page, keep every price component in its own column instead of blending them, and match events across marketplaces on venue and start time rather than title. Each run is validated against the agreed schema before it leaves us, and arrives with a manifest that says what should be there and what is.
- Full depth
- The entire listing table per event, not a sample of it
- Separated fees
- Display price, each fee component and the all-in figure, individually
- Real joins
- Event, performer and venue IDs that survive a title change
- Kept history
- Stable listing IDs, so one seat can be followed to sale
- Verified transfer
- A manifest per delivery, checked byte for byte
Four marketplaces, four genuinely different problems
These are not the same job with the logo swapped. Each site structures price, inventory and identity differently, and the part that takes the work is different on each one. Each page below says what that part is.
TickPick data extraction
Running dailyAll-in pricing and value scores. Our longest-running ticket pipeline.
All-in pricing changes what you can compare
StubHub data extraction
On requestThe largest resale inventory, with the real all-in price worked out.
The displayed price is not the paid price
Ticketmaster data extraction
On requestPrimary inventory and Verified Resale, held apart in the schema.
Two markets wearing one coat of paint
SeatGeek data extraction
On requestAggregated listings, with duplicate seats resolved rather than counted twice.
The same seat, listed more than once
The same seat, listed in four places, at four prices
This is the question most people are really asking when they ask for ticket data. It sounds like a lookup and it is not. Two marketplaces will name the same concert differently, apply fees at different points, and describe the same section with different labels. Answering it properly means resolving the event, resolving the seat, and reconstructing the price a buyer would actually be charged on each site.
We match events on venue and local start time, normalise section and row against the venue's own map, and keep every fee component separate so the all-in comparison is auditable rather than asserted.
| marketplace | listed | fees | all_in |
|---|---|---|---|
| tickpick | $141.00 | included | $141.00 |
| stubhub | $118.00 | $31.40 | $149.40 |
| seatgeek | $124.00 | $28.15 | $152.15 |
| ticketmaster | $135.00 | $26.80 | $161.80 |
What this pipeline has actually moved
This is a live event-pricing pipeline on TickPick, running continuously. Not a projection, and not a capability estimate. Figures are rounded and kept current rather than quoted to the digit, because the pipeline adds to them every morning. The same infrastructure and the same schema carry the other three marketplaces.
files_expected ✓ matched files_verified ✓ matched bytes_verified ✓ byte for byte checksums ✓ all pass errors [ ]
- An ordinary day
- 3M+ listings across 30,000+ events
- The busiest day
- 6M+ listings in a single delivery
- NFL season week
- 60M+ rows over 7 days
- World Cup window
- 5M+ rows over 5 weeks
- Super Bowl run-up
- 1M+ rows over 2 weeks
Sample data is available on request
What is above is a summary. Ask and we will show you the rest directly, on a call or a screen share: the run history, the manifests, and a working sample built from your own target events in the schema you want it in. That sample is yours to keep and to test against.
What you will never be shown is another client's delivered dataset, and that is the same undertaking we make to you about yours.
The schema, including the awkward parts
Two joined tables. Several price fields are nullable, because not every marketplace exposes a fee breakdown before checkout, and a column full of estimates is worse than an empty one. This is the working schema, not a cleaned-up version of it.
- marketplacestring
Which site the listing came from
- event_idstring
Joins to the event table
- listing_idstring
Stable per listing, so you can track one seat over time
- section_namestring
Section as the site labels it
- row_namestring
Row within the section
- seat_numbersstring
Specific seats where the listing exposes them
- ticket_quantityint
How many seats the listing covers
- value_scorefloat
The marketplace's own value rating, where it has one
- listing_notesstring
Delivery method, restrictions, obstructed view
- display_price_pre_checkoutdecimal
The price shown on the listing page
- all_in_price_pre_checkoutdecimal
Display price with fees folded in, where shown up front
- display_price_checkoutdecimal
Price carried through to the checkout screen
- buyer_fee_checkoutdecimal
Service fee, itemised
- other_fee_checkoutdecimal
Delivery and processing charges
- sales_tax_checkoutdecimal
Tax, where it is broken out
- all_in_price_checkoutdecimal
What the buyer actually pays
- cache_timetimestamp
When this observation was taken
- marketplacestring
Source site
- event_idstring
Primary key for the event
- event_namestring
Full event title as listed
- event_categorystring
MLB Baseball, NHL Hockey, Concerts, and so on
- performer_idstring
Stable ID for the team or artist
- performer_namestring
Home team, headline act
- venue_idstring
Stable ID for the venue
- venue_namestring
Stadium, arena or hall
- event_timestamp_localtimestamp
Local start time, not UTC-shifted
This is what our running pipeline delivers. Yours is whatever you sign off on: rename the fields, drop the ones you will never query, add the ones we have not thought of. The schema is agreed before any collection starts, and it does not change underneath you afterwards.
Concerts, every major league, and the long tail underneath
The counts below are events seen in a single day of collection. The mix is lopsided because the real market is lopsided: concerts outnumber everything else by an order of magnitude, and most sports volume sits in a handful of leagues.
Full market, daily
Every event on the marketplace, collected once a day. The baseline our running pipeline uses.
Scoped to a league or tour
One competition, one artist, one venue. Cheaper, faster to validate, and usually where a first project should start.
Short intensive windows
A single week, an on-sale, a tournament. We have run a seven-day league collection that produced over 60 million rows.
Bring it to us properly and the job gets done
The projects that go well all start the same way. Somebody arrives knowing which marketplaces matter, which events or categories they care about, and what they intend to do with the data once it lands. Give us that much and the rest is our problem: the collection, the schema, the validation, the schedule, and every site change that comes afterwards.
You do not need the technical half worked out. You do not need to know whether a site is difficult, how deep the pagination goes, or what the fee structure does at checkout. That is what you are hiring. What you do need is a clear picture of the question you are trying to answer, because a brief with that in it produces data worth having, and one without it produces columns nobody uses.
- 01Which marketplacesOne to start with, or all four.
- 02Which eventsA league, a tour, a venue, a category, or the whole market.
- 03What you need to knowThe question the data is meant to answer.
- 04How oftenDaily is the usual baseline. Tighter around an on-sale.
- 05Where it landsYour bucket, your warehouse, an endpoint, a file drop.
Missing a few of these is fine. Send what you have and we will fill the gaps on the call. Start with the project brief.
What people ask before starting a ticket project
TickPick, StubHub, Ticketmaster and SeatGeek are the four we have dedicated pages for, because they are what people ask for. The pipeline is not limited to them. Any publicly accessible ticketing or resale site can be added, and regional marketplaces are a routine request.
Over 1.2 billion listing records and 14 million event records, across more than 400 daily deliveries on a live event-pricing pipeline that is still running. Zero failed transfers in that time. That record is on TickPick; the other three marketplaces run on the same infrastructure and the same schema. We keep these figures rounded rather than quoted to the digit, because the pipeline adds to them every morning.
Yes. Sample data is available on request, and we would rather you asked. Send us three events or a category you care about and we will build a working sample from your own targets, in the schema you want, then walk you through it live including which fields come back null and why. That sample is yours to keep and test against. What you will never be shown is another client's delivered dataset, and that is the same undertaking we make to you about yours.
Daily is the common baseline and what our running pipeline does. Intraday and near-real-time collection are available where the event set justifies the cost. Around a major on-sale, a single event can warrant a cadence the rest of the catalogue does not.
Both, in separate columns. Display price, buyer fee, delivery and processing charges, tax and the resulting all-in price are each their own field. This matters more than it sounds: marketplaces apply fees at different points, so comparing advertised prices across sites reliably produces the wrong answer.
The marketplaces that matter, the events or categories you care about, and what you intend to do with the data once it lands. You do not need the technical half worked out. Whether a site is difficult, how deep the pagination goes, what the fees do at checkout, all of that is ours to solve. Bring the question you are trying to answer and the job gets done.
Parquet over SFTP is the usual choice at this volume, and it is what the running pipeline uses. CSV, JSON, a REST endpoint or a direct load into your warehouse are all available. Every delivery carries a manifest of expected files, verified files and byte counts.
Yes, and it is usually the better place to start. Scoped collections cost less and validate faster. We have run single-league weeks, a tournament window and a single event's run-up alongside a full-market daily job.
Send us three events and see what comes back
Name the marketplace, the events and the fields you care about. You get a feasibility read, a proposed schema and a real sample pulled from your own targets, before any commitment.
No retainer required to find out whether your sources are feasible.