rebrowser/seatgeek-dataset

SeatGeek Events & Ticket Listings Dataset

Daily sample of SeatGeek events, ticket listings, performers, and venues with Deal Score ratings, section-level seating, delivery types, and cross-platform IDs.

This dataset is a preview sample of the SeatGeek dataset published by Rebrowser. If you're doing academic research, you may be eligible for free access to a much larger slice — see Free Datasets for Research.

This dataset contains 4 entities, each in its own folder: Events (events), Event Listings (event-listings), Performers (performers), Venues (venues). See below for a full field breakdown, sample counts, and data distributions for each.

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Events

Daily sample of SeatGeek events with type, taxonomy, venue and performer IDs, schedule status, cross-platform IDs, and seat map availability.

14,981 total records from 2025-10-05 to 2026-10-11, up to 14,981 rows in this sample (100.0% of full dataset). Exported as one file per day, up to 1,000 rows each, last 30 days retained.

Field	Type	Fill Rate	Description
_primaryKey	string	100%	Unique identifier for this record
_firstSeenAt	datetime	100%	First time this record was seen
_lastSeenAt	datetime	100%	Last time this record was updated
eventId	float	100%	Unique event ID (e.g., 17601982)
name	string	100%	Full event name/title (e.g., NLDS: Chicago Cubs at Milwaukee Brewers)
shortName	string	100%	Short event name (e.g., NLDS: Cubs at Brewers)
type	string	100%	Event type (mlb, nba, nhl, nfl, stadium_tours, etc.)
datetimeUtc	datetime	100%	Event UTC datetime
endDatetimeUtc	datetime	77%	Event end datetime (UTC)
dateTbd	bool	100%	Event date is TBD (to be determined)
timeTbd	bool	100%	Event time is TBD
datetimeTbd	bool	100%	Event datetime is TBD
status	string	100%	Event status (normal, postponed, cancelled)
scheduleStatus	string	100%	Schedule status (as_originally_scheduled, rescheduled)
conditional	bool	100%	Event is conditional (e.g., playoff games)
contingent	bool	100%	Event is contingent on other events
isOpen	bool	100%	Event is open for ticket sales
isVisible	bool	100%	Event is visible on site
isHybrid	bool	100%	Event is a hybrid event
eventScore 🔒	float	100%	Event score/rank (0-1 scale)
popularityScore 🔒	float	100%	Event popularity score (0-1 scale)
url	string	100%	Full SeatGeek URL for the event
createdAt	datetime	100%	Event creation timestamp
announceDate	datetime	100%	Event announcement date
visibleAt	datetime	100%	When event became visible
visibleUntilUtc	datetime	100%	When event stops being visible (UTC)
listingCount 🔒	float	100%	Number of active ticket listings
ticketCount 🔒	float	100%	Total tickets available across listings
averagePrice 🔒	float	100%	Average ticket price in dollars
lowestPrice 🔒	float	100%	Lowest ticket price in dollars
highestPrice 🔒	float	100%	Highest ticket price in dollars
medianPrice 🔒	float	100%	Median ticket price in dollars
lowestSgBasePrice 🔒	float	100%	Lowest SeatGeek base price in dollars
venueId	float	100%	Venue ID (join with seatgeek_venues)
performerIds	array	100%	Performer IDs (join with seatgeek_performers)
taxonomyName	string	100%	Top-level category (sports, concerts, theater)
taxonomySubName	string	100%	Sub-category (baseball, basketball, hockey, football)
ticketmasterId	string	40%	Ticketmaster event ID (for cross-platform matching)
stubhubId	string	49%	StubHub event ID (for cross-platform matching)
integratedProvider	string	58%	Integrated ticket provider (OPEN, TICKETMASTER, TDC)
integratedProviderId	string	58%	Provider-specific event ID
isMapped	bool	100%	Venue has seat map available
isGa	bool	100%	Event is general admission
seatSelectionEnabled	bool	100%	Seat selection is enabled

🔒 Premium fields are included in the data files but their values are replaced with [PREMIUM]. To access real values, use our website.

Field Distributions

Event Type Distribution (type)

Value	Count	Share
mlb	5,840	████████░░░░░░░░░░░░ 39.0%
nhl	3,060	████░░░░░░░░░░░░░░░░ 20.4%
nba	2,980	████░░░░░░░░░░░░░░░░ 19.9%
stadium_tours	2,272	███░░░░░░░░░░░░░░░░░ 15.2%
nfl	760	█░░░░░░░░░░░░░░░░░░░ 5.1%
baseball	69	░░░░░░░░░░░░░░░░░░░░ 0.5%

Top-Level Event Category (taxonomyName)

Value	Count	Share
sports	14,981	████████████████████ 100.0%

Event Status (status)

Value	Count	Share
normal	14,981	████████████████████ 100.0%

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Event Listings

Daily sample of SeatGeek ticket listings with section, row, quantity, delivery type, marketplace, and deal bucket per event.

114,516,496 total records from 2025-10-05 to 2026-10-18, up to 30,000 rows in this sample (0.03% of full dataset). Exported as one file per day, up to 1,000 rows each, last 30 days retained.

Field	Type	Fill Rate	Description
_primaryKey	string	100%	Unique identifier for this record
_firstSeenAt	datetime	100%	First time this record was seen
_lastSeenAt	datetime	100%	Last time this record was updated
listingId	string	100%	Unique listing ID (e.g., qVjH2vAdbzA, 05VT8679aVX)
eventId	string	100%	Event ID this listing belongs to (join with seatgeek_events)
price 🔒	float	100%	Ticket price in dollars before fees
priceWithFees 🔒	float	100%	Total ticket price in dollars with fees
fee 🔒	float	100%	Fee amount in dollars
section	string	100%	Section name/number (e.g., 101, 506WC, C129)
sectionFull	string	100%	Full section name including tier/level (e.g., Section 101, Club 129, Section 506 WC)
row	string	100%	Row within section - can be numeric (1-50+) or letter (a-z, w, h)
quantity	float	100%	Number of tickets available in this listing, typically 1-20
seats	array	21%	Specific seat numbers if assigned, empty array if GA/unassigned
inHandDate	datetime	97%	Date when tickets will be in hand for delivery
deliveryType	string	100%	Ticket delivery method: electronic, sg_app, shipped, local
marketplace	string	100%	Ticket marketplace/seller: exchange, open_marketplace, marketplace, open, fan_to_fan
dealBucket	float	100%	Deal quality bucket: 0=Amazing, 1=Great, 2=Good, 3=Okay, 4-6=Price tiers, 7=Other
dealScore 🔒	float	99%	Deal quality score 0-10, higher=better value
splitType	string	100%	How tickets can be split - comma-separated quantities (e.g., "2", "1,2,4")

🔒 Premium fields are included in the data files but their values are replaced with [PREMIUM]. To access real values, use our website.

Field Distributions

Listing Marketplace (marketplace)

Value	Count	Share
exchange	111,749,273	████████████████████ 97.6%
marketplace	1,370,114	░░░░░░░░░░░░░░░░░░░░ 1.2%
open	837,277	░░░░░░░░░░░░░░░░░░░░ 0.7%
open_marketplace	505,567	░░░░░░░░░░░░░░░░░░░░ 0.4%
fan_to_fan	54,265	░░░░░░░░░░░░░░░░░░░░ 0.0%

Delivery Type (deliveryType)

Value	Count	Share
electronic	63,849,508	███████████░░░░░░░░░ 55.8%
mobile_transfer	31,037,688	█████░░░░░░░░░░░░░░░ 27.1%
sg_app	16,522,795	███░░░░░░░░░░░░░░░░░ 14.4%
seatgeek_app	2,833,829	░░░░░░░░░░░░░░░░░░░░ 2.5%
shipped	250,575	░░░░░░░░░░░░░░░░░░░░ 0.2%
pdf	21,389	░░░░░░░░░░░░░░░░░░░░ 0.0%
local	662	░░░░░░░░░░░░░░░░░░░░ 0.0%
willcall	50	░░░░░░░░░░░░░░░░░░░░ 0.0%

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Performers

SeatGeek performers including teams, artists, and acts with type, taxonomy, division, popularity score, and home venue.

263 total records from 2025-10-05 to 2026-09-27, 263 rows in this sample (100.0% of full dataset). Exported as a single file, overwritten daily.

Field	Type	Fill Rate	Description
_primaryKey	string	100%	Unique identifier for this record
_firstSeenAt	datetime	100%	First time this record was seen
_lastSeenAt	datetime	100%	Last time this record was updated
performerId	float	100%	Unique performer ID (e.g., 11, 793010)
name	string	100%	Full performer name (e.g., Chicago Cubs, MLB Postseason)
shortName	string	100%	Short name (e.g., Cubs, Dodgers)
type	string	100%	Performer type (mlb, nba, nhl, nfl, etc.)
slug	string	100%	URL-friendly slug (e.g., chicago-cubs)
url	string	100%	Full SeatGeek URL for the performer
heroImageUrl 🔒	string	100%	Hero/large image URL
bannerImageUrl 🔒	string	100%	Banner image URL
score	float	100%	Performer score (0-1 scale)
popularity	float	100%	Performer popularity score (raw count)
homeVenueId	float	55%	Home venue ID (for teams)
primaryColor	string	50%	Primary brand color hex (e.g., #0E3386)
iconicColor	string	50%	Iconic brand color hex
isEvent	bool	100%	Is an event/competition performer (e.g., playoffs, series)
divisionName	string	47%	Division display name (e.g., National League Central)
divisionShortName	string	47%	Division short name (e.g., NL Central)
taxonomyName	string	100%	Top-level category (sports, concerts, theater)
taxonomySubName	string	98%	Sub-category (baseball, basketball, hockey, football)

🔒 Premium fields are included in the data files but their values are replaced with [PREMIUM]. To access real values, use our website.

Field Distributions

Performer Type (type)

Value	Count	Share
nfl	68	█████░░░░░░░░░░░░░░░ 25.9%
nba	50	████░░░░░░░░░░░░░░░░ 19.0%
mlb	50	████░░░░░░░░░░░░░░░░ 19.0%
nhl	49	████░░░░░░░░░░░░░░░░ 18.6%
baseball	25	██░░░░░░░░░░░░░░░░░░ 9.5%
stadium_tours	6	░░░░░░░░░░░░░░░░░░░░ 2.3%
minor_league_baseball	6	░░░░░░░░░░░░░░░░░░░░ 2.3%
band	5	░░░░░░░░░░░░░░░░░░░░ 1.9%
ncaa_baseball	2	░░░░░░░░░░░░░░░░░░░░ 0.8%
basketball	2	░░░░░░░░░░░░░░░░░░░░ 0.8%

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Venues

SeatGeek venues with name, full address, city, state, country, GPS coordinates, capacity, and popularity score.

194 total records from 2025-10-05 to 2026-10-11, 194 rows in this sample (100.0% of full dataset). Exported as a single file, overwritten daily.

Field	Type	Fill Rate	Description
_primaryKey	string	100%	Unique identifier for this record
_firstSeenAt	datetime	100%	First time this record was seen
_lastSeenAt	datetime	100%	Last time this record was updated
venueId	float	100%	Unique venue ID (e.g., 15, 181)
name	string	100%	Venue name (e.g., American Family Field, Capital One Arena)
slug	string	100%	URL-friendly slug (e.g., american-family-field)
url	string	100%	Full SeatGeek URL for the venue
addressStreet	string	96%	Street address (e.g., 1 Brewers Way)
addressCity	string	100%	City name (e.g., Milwaukee)
addressState	string	97%	State/province code (e.g., WI, ON)
addressCountry	string	99%	Country (US, Canada, Germany, UK)
addressPostalCode	string	96%	Postal/ZIP code (e.g., 53214)
timezone	string	100%	IANA timezone (e.g., America/Chicago)
latitude	float	100%	Venue latitude coordinate
longitude	float	100%	Venue longitude coordinate
capacity	float	100%	Venue seating capacity
score	float	100%	Venue score (0-1 scale)
popularity	float	100%	Venue popularity score (raw count)
metroCode	float	100%	Metro area code

Field Distributions

Venue Countries (addressCountry)

Value	Count	Share
US	174	██████████████████░░ 90.6%
Canada	12	█░░░░░░░░░░░░░░░░░░░ 6.3%
UK	2	░░░░░░░░░░░░░░░░░░░░ 1.0%
Germany	2	░░░░░░░░░░░░░░░░░░░░ 1.0%
Spain	1	░░░░░░░░░░░░░░░░░░░░ 0.5%
Mexico	1	░░░░░░░░░░░░░░░░░░░░ 0.5%

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Pre-built Views on Rebrowser

Rebrowser web viewer lets you filter, sort, and export any slice of this dataset interactively. These pre-built views are ready to open:

Events

Events with Pricing Data — 14,722 records

↳ [{"field":"averagePrice","op":"gt","value":0},{"sort":"averagePrice DESC"}]

Sports Events — 14,771 records

↳ [{"field":"taxonomyName","op":"is","value":"sports"},{"sort":"datetimeUtc ASC"}]

Events Open for Ticket Sales — 2,872 records

↳ [{"field":"isOpen","op":"isTrue"},{"sort":"datetimeUtc ASC"}]

MLB Baseball Events — 5,776 records

↳ [{"field":"type","op":"is","value":"mlb"},{"sort":"datetimeUtc ASC"}]

NBA Basketball Events — 2,980 records

↳ [{"field":"type","op":"is","value":"nba"},{"sort":"datetimeUtc ASC"}]

See all 24 views →

Event Listings

Listings with Deal Score — 93,078,305 records

↳ [{"field":"dealScore","op":"gt","value":0},{"sort":"dealScore DESC"}]

Best Deal Listings (Deal Score 8+) — 40,862,587 records

↳ [{"field":"dealScore","op":"gte","value":8},{"sort":"dealScore DESC"}]

Listings by Price (Low to High) — 93,800,358 records

↳ [{"sort":"price ASC"}]

Listings by Price (High to Low) — 93,771,052 records

↳ [{"sort":"price DESC"}]

Electronic Delivery Listings — 63,848,832 records

↳ [{"field":"deliveryType","op":"is","value":"electronic"},{"sort":"price ASC"}]

See all 25 views →

Performers

Sports Performers — 258 records

↳ [{"field":"taxonomyName","op":"is","value":"sports"},{"sort":"name ASC"}]

MLB Performers — 50 records

↳ [{"field":"type","op":"is","value":"mlb"},{"sort":"name ASC"}]

NBA Performers — 50 records

↳ [{"field":"type","op":"is","value":"nba"},{"sort":"name ASC"}]

NHL Performers — 49 records

↳ [{"field":"type","op":"is","value":"nhl"},{"sort":"name ASC"}]

NFL Performers — 68 records

↳ [{"field":"type","op":"is","value":"nfl"},{"sort":"name ASC"}]

See all 18 views →

Venues

Venues by Capacity — 150 records

↳ [{"field":"capacity","op":"gt","value":0},{"sort":"capacity DESC"}]

Venues in United States — 173 records

↳ [{"field":"addressCountry","op":"is","value":"US"},{"sort":"addressState ASC"}]

Venues in California — 17 records

↳ [{"field":"addressState","op":"is","value":"CA"},{"sort":"name ASC"}]

Venues in Florida — 23 records

↳ [{"field":"addressState","op":"is","value":"FL"},{"sort":"name ASC"}]

Venues in Arizona — 15 records

↳ [{"field":"addressState","op":"is","value":"AZ"},{"sort":"name ASC"}]

See all 19 views →

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Code Examples

import pandas as pd
from pathlib import Path

# ── Performers (dimension table) ─────────────────────────────────────────────
performers = pd.read_parquet('rebrowser/seatgeek-dataset/performers/data.parquet')

# Top 20 performers by popularity
print(performers.nlargest(20, 'popularity')[['name', 'type', 'taxonomyName', 'popularity']]
      .to_string(index=False))

# Count performers per type (mlb, nba, nhl, nfl, ...)
print(performers['type'].value_counts().head(15).to_string())

# Sports performers with a home venue
home_teams = performers[performers['homeVenueId'].notna()]
print(home_teams[['name', 'type', 'divisionShortName', 'homeVenueId']].sort_values('type'))

# ── Venues (dimension table) ─────────────────────────────────────────────────
venues = pd.read_parquet('rebrowser/seatgeek-dataset/venues/data.parquet')

# Largest venues by capacity
print(venues.nlargest(15, 'capacity')[['name', 'addressCity', 'addressState', 'capacity']]
      .to_string(index=False))

# Venue count by state
print(venues['addressState'].value_counts().head(15).to_string())

# ── Events (daily append) ────────────────────────────────────────────────────
files = sorted(Path('rebrowser/seatgeek-dataset/events/data').glob('*.parquet'))[-7:]
events = pd.concat([pd.read_parquet(f) for f in files])

# Events by type
print(events['type'].value_counts().head(15).to_string())

# Upcoming sports events with normal status
sports = events[(events['taxonomyName'] == 'sports') & (events['status'] == 'normal')]
print(sports[['name', 'type', 'datetimeUtc', 'venueId']].head(20).to_string(index=False))

# Events with cross-platform Ticketmaster IDs
tm_events = events[events['ticketmasterId'].notna()]
print(f"Events with Ticketmaster ID: {len(tm_events)} / {len(events)}")

# ── Event Listings (daily append) ────────────────────────────────────────────
files = sorted(Path('rebrowser/seatgeek-dataset/event-listings/data').glob('*.parquet'))[-7:]
listings = pd.concat([pd.read_parquet(f) for f in files])

# Distribution of delivery types
print(listings['deliveryType'].value_counts().to_string())

# Listings by marketplace
print(listings['marketplace'].value_counts().to_string())

# Average quantity per listing by delivery type
print(listings.groupby('deliveryType')['quantity'].mean().round(1).to_string())

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Use Cases

Cross-Platform Event Matching

Use ticketmasterId and stubhubId fields to match events across SeatGeek, Ticketmaster, and StubHub. Build cross-marketplace comparisons and inventory analysis.

Venue Capacity Analysis

Combine venue capacity data with event listing counts to study sell-through rates. Compare demand patterns across venue sizes, states, and time zones.

Delivery Method Research

Analyze how electronic vs. shipped vs. app delivery options distribute across event types and marketplaces. Study the industry shift toward mobile ticketing.

Performer Demand Tracking

Join events with performers to measure which artists and teams generate the most listings. Rank performers by event frequency and marketplace activity.

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Full Dataset on Rebrowser

This is a 1,000-row preview sample. The full dataset is at rebrowser.net/products/datasets/seatgeek

Doing academic research? You may qualify for free access to a larger slice. See Free Datasets for Research.

On Rebrowser you can:

• Filter before you buy — use the web UI to apply documented filters and sortable columns. Preview results before purchasing; paid exports freeze their exact selected identities before billing.
• Export in your format — CSV, JSON, JSONL, or Parquet depending on your plan.
• Access via API — integrate dataset queries into your pipelines and workflows.
• Choose your freshness — plans range from a 14-day lag to real-time data with no delay.
• Select only the fields you need — keep exports lean. Premium fields with richer data are available on higher plans.

Pricing starts at $2 per 1,000 rows with volume discounts.

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License & Terms

Free for research and non-commercial use with attribution. See license terms and how to cite.

@misc{rebrowser_seatgeek,
  author       = {Rebrowser},
  title        = {SeatGeek Events & Ticket Listings Dataset},
  year         = {2026},
  howpublished = {\url{https://rebrowser.net/products/datasets/seatgeek}},
  note         = {Accessed: YYYY-MM-DD}
}

Commercial use requires a paid license — see pricing. Use of this data is governed by the Rebrowser Terms of Use, which may be updated at any time independently of this dataset.

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Disclaimer

Rebrowser is an independent data provider and is not affiliated with, endorsed by, or sponsored by SeatGeek. Any trademarks are the property of their respective owners. This dataset is compiled from publicly available information; we do not request or collect SeatGeek user credentials. By using this dataset, you agree to comply with SeatGeek's Terms of Service and all applicable laws and regulations. Images, logos, descriptions, and other materials included in this dataset remain the intellectual property of their respective owners and are provided solely for informational purposes. Rebrowser makes no warranties regarding the accuracy, completeness, or legality of the data and assumes no liability for how the data is used. You are solely responsible for ensuring that your use of this dataset does not infringe on the rights of any third party.

You can also find this data on GitHub, Kaggle, Zenodo.