Data Structure
Detailed field definitions and data structures for SeatData's datasets. Understand the schema for sales data, listing data, and event reference tables with comprehensive field descriptions and examples.
CSV Format
All dataset exports, including Dataset Update services, adhere to RFC 4180.
Field Definitions
SeatData offers two datasets — Sales and Listings — delivered as the following files:
| Dataset | File | Schema |
|---|---|---|
| Sales | Sales | Sales Dataset |
| Listings | Listings (flat file) | Listing Dataset |
| Listings | Listing Changes | Listing Changes |
| Included with any purchase | Event Reference | Event Reference |
Sales Dataset
Each row represents a completed ticket sale with the following fields:
| Field | Type | Description | Example |
|---|---|---|---|
timestamp | String | ISO 8601 datetime (YYYY-MM-DD HH:MM:SS) | 2021-05-28 03:10:26 |
event_id | Integer | Marketplace Event ID | 0123456789 |
listing_id | Integer | Marketplace Listing ID | 9876543210 |
quantity_old | Integer | Quantity before the sale (0 if unknown) | 6 |
quantity | Integer | Quantity after the sale (0 if unknown) | 2 |
price | Float | Price per ticket in USD | 17.09 |
zone | String | Venue zone designation | Lower Bowl |
section | String | Venue section | 105 |
row | String | Venue row | 12 |
Sample Records
timestamp,event_id,listing_id,quantity_old,quantity,price,zone,section,row
2025-09-15 13:49:05,0123456789,9876543210,0,0,292.0,Orchestra,ORCH3,C
2025-09-15 13:30:22,0123456789,9876543210,0,0,135.0,3rd Mezzanine,3RDMZ2,
2025-09-15 21:30:22,0123456789,9876543210,0,0,137.0,Orchestra,ORCH3,
2025-09-15 16:38:27,0123456789,9876543210,0,0,179.0,Plaza Level,227,
2025-09-15 16:38:27,0123456789,9876543210,0,0,213.0,Plaza Level,202,S
2025-09-15 19:54:33,0123456789,9876543210,0,0,149.0,Main Floor,3,
2025-09-15 17:10:28,0123456789,9876543210,0,0,100.0,General Admission,General Admission,1
2025-09-15 13:38:27,0123456789,9876543210,0,0,154.0,Lower,127,W
2025-09-15 16:38:27,0123456789,9876543210,0,0,156.0,Lower,103,
2025-09-15 20:54:30,0123456789,9876543210,0,0,130.0,Orchestra,ORCHRT,RR
2025-09-15 16:54:30,0123456789,9876543210,0,0,154.0,First Dress Circle,DRA,L
2025-09-15 20:38:28,0123456789,9876543210,0,0,205.0,Lower,111,28
2025-09-15 23:38:28,0123456789,9876543210,0,0,514.0,Lower,114,6Listing Dataset
Current state or last known state of all tracked listings:
| Field | Type | Description | Example |
|---|---|---|---|
id | Integer | Unique row identifier | 123456789 |
active | Integer | 1 = the listing was available at the last scan; 0 = the listing is no longer available | 1 |
event_id | Integer | Event identifier | 0123456789 |
listing_id | Integer | Listing identifier | 9876543210 |
starting_qty | Integer | Initial quantity when first observed | 4 |
ending_qty | Integer | Current quantity | 2 |
price | Float | Current price per ticket in USD | 125.00 |
zone | String | Venue zone | Lower Bowl |
section | String | Venue section | 105 |
row | String | Venue row | 20 |
created_at | String | ISO 8601 datetime when first observed (YYYY-MM-DD HH:MM:SS) | 2021-05-28 12:30:45 |
updated_at | String | ISO 8601 datetime of last modification (YYYY-MM-DD HH:MM:SS) | 2021-05-29 14:15:20 |
Timestamp availability
created_at and updated_at were added to the dataset in September 2025. Rows recorded before their introduction have null values in those fields.
Row Uniqueness and Deduplication
The combination of event_id and listing_id is not unique — the same listing can appear in multiple rows (for example, after price changes). Each row has a unique id. To reduce to a single row per listing, use the row with the highest id; for listings with a non-null created_at, take the created_at value from the lowest id.
What a Row Represents
For events that have ended, rows reflect the system's final scan of the event — the resting state for many past events is active = 0. To reconstruct how listings changed over time, use the Listing Changes dataset.
Sample Records
id,active,event_id,listing_id,starting_qty,ending_qty,price,zone,section,row,created_at,updated_at
100000001,0,0123456789,9876543210,2,2,280.49,Suite,14,10,2021-05-15 09:20:15,2021-05-16 11:45:30
100000002,0,0123456789,9876543210,2,2,315.51,Lower,125,12,2021-05-15 10:30:22,2021-05-16 12:15:45
100000003,1,0123456789,9876543210,2,2,330.49,Suite,5,14,2021-05-15 11:15:33,2021-05-16 13:20:10
100000004,0,0123456789,9876543210,3,3,336.49,Lower,105,15,2021-05-15 12:45:18,2021-05-16 14:30:55
100000005,0,0123456789,9876543210,1,1,381.04,Floor,Floor 3,15,2021-05-15 13:20:45,2021-05-16 15:10:20
100000006,0,0123456789,9876543210,4,4,391.76,Lower,115,12,2021-05-15 14:10:12,2021-05-16 16:20:30
100000007,0,0123456789,9876543210,2,2,393.73,Lower,123,18,2021-05-15 15:30:55,2021-05-16 17:45:15
100000008,0,0123456789,9876543210,2,2,395.70,Lower,125,8,2021-05-15 16:20:33,2021-05-16 18:30:45
100000009,0,0123456789,9876543210,1,1,464.49,Suite,3,15,2021-05-15 17:15:20,2021-05-16 19:20:10
100000010,0,0123456789,9876543210,2,2,526.49,Suite,3,12,2021-05-15 18:45:10,2021-05-16 20:15:30
100000011,0,0123456789,9876543210,1,1,529.49,Suite,3,21,2021-05-15 19:30:45,2021-05-16 21:10:15
100000012,0,0123456789,9876543210,1,1,660.49,Suite,3,15,2021-05-15 20:15:22,2021-05-16 22:30:45
100000013,0,0123456789,9876543210,1,1,791.49,Suite,3,16,2021-05-15 21:20:30,2021-05-16 23:45:10
100000014,0,0123456789,9876543210,2,2,1312.49,,FLR1,6,2021-05-15 22:10:15,2021-05-17 08:30:20
100000015,0,0123456789,9876543210,2,2,1508.49,,FLR1,6,2021-05-15 23:30:45,2021-05-17 09:15:30
100000016,1,0123456789,9876543210,2,2,6.00,Mezzanine,Mezzanine,G,2021-05-16 08:20:10,2021-05-17 10:45:15
100000017,1,0123456789,9876543210,9,9,410.72,Mezzanine,Mezzanine,G,2021-05-16 09:15:30,2021-05-17 11:30:45
100000018,0,0123456789,9876543210,10,10,88.70,,BL24,S,2021-05-16 10:30:20,2021-05-17 12:15:10
100000019,0,0123456789,9876543210,5,5,89.86,,BL21,R,2021-05-16 11:45:15,2021-05-17 13:20:30
100000020,0,0123456789,9876543210,10,10,90.14,,BL22,U,2021-05-16 12:20:45,2021-05-17 14:30:55Listing Changes
Historical changes to listings since June 2, 2022 (over 9 billion records as of mid-2026):
| Field | Type | Description | Example |
|---|---|---|---|
timestamp | String | ISO 8601 datetime (YYYY-MM-DD HH:MM:SS) | 2022-11-04 23:33:30 |
event_id | Integer | Event identifier | 0123456789 |
listing_id | Integer | Listing identifier | 9876543210 |
change_type | String | Type of change | price_change |
field_changed | String | Field that changed (for *_change types) | price |
old_value | String | Previous value before change | 125.00 |
new_value | String | New value after change | 111.79 |
Change Types
| Change Type | Description | Date Added | Field Changed |
|---|---|---|---|
price_change | Price modification | Jun 2, 2022 | price |
deact_sold | Listing deactivated or sold | Jun 2, 2022 | active* |
new_listing | Listing first appeared | Jun 2, 2022 | (empty) |
found_listing_previously_deactivated | Reactivated listing | Jun 2, 2022 | active* |
section_change | Section modification | Jun 2, 2022 | section |
zone_change | Zone modification | Jul 22, 2025 | zone |
quantity_change | Available quantity changed | Jul 22, 2025 | quantity |
row_change | Row modification | Jul 22, 2025 | row |
*The active field tracking for deact_sold and found_listing_previously_deactivated was added in July 2025.
Data Evolution
The zone_change, quantity_change, and row_change types were introduced in July 2025, representing an enhancement to the tracking system that now captures more granular listing modifications.
Sample Records
timestamp,event_id,listing_id,change_type,field_changed,old_value,new_value
2022-11-14 19:27:20,0123456789,9876543210,deact_sold,,,
2022-07-05 08:20:49,0123456789,9876543210,found_listing_previously_deactivated,,,
2022-11-02 15:01:00,0123456789,9876543210,new_listing,,,
2022-11-04 23:33:30,0123456789,9876543210,price_change,price,125.00,100.91
2025-08-06 17:26:43,0123456789,9876543210,quantity_change,quantity,2,1
2025-07-22 19:42:04,0123456789,9876543210,row_change,row,KK,LL
2022-07-05 08:20:49,0123456789,9876543210,section_change,section,Orchestra Left,Orchestra Right
2025-08-07 14:28:03,0123456789,9876543210,zone_change,zone,Balkon,ParkettData Quality Notes
- Duplicate rows occur. Multiple collection processes can observe the same change at the same time, producing duplicate (occasionally triplicate) entries. Deduplicate before reconstructing listing history.
old_valuecan be stale when duplicates exist — place more weight onnew_value, which is the system's observation at the given time.- A small share of early historical rows (under 0.5%) have a null
timestamp.
Data Continuity Gaps
Known Data Gaps
The Listing Changes dataset has periods where no data was collected.
| Period | Duration |
|---|---|
| Nov 3, 2024 | 1 day |
| Nov 7, 2024 | 1 day |
| Apr 26 - Jul 21, 2025 | 66 days |
Event Reference
Event metadata included with all datasets:
| Field | Type | Description | Example |
|---|---|---|---|
event_id | Integer | Event identifier | 0123456789 |
event_name | String | Event name | Yankees vs Red Sox |
event_date | Date | Event date (YYYY-MM-DD) | 2024-07-15 |
event_time | Time | Event time local (HH:MM:SS) | 19:05:00 |
venue_name | String | Venue name | Yankee Stadium |
venue_city | String | Venue city | New York |
venue_state | String | Venue state/province | NY |
SeatData Event Ticket Datasets
Access comprehensive historical ticket sales and listing data with SeatData's event ticket datasets. Get 50M+ sales records, 1B+ listings, and daily updates for live events, sports, and entertainment venues.
Event Ticket Updates Subscription
Subscribe to daily incremental updates for ticket listings and sales data. Get automated delivery of new and modified records via email or S3 storage with flexible delivery options and comprehensive field-level change tracking.