rebrowser/stubhub-dataset

StubHub Ticket Marketplace Dataset

Daily snapshots of StubHub resale ticket listings, events, and venues with seating details, delivery types, and availability data across sports, concerts, and theater.

This dataset is a preview sample of the StubHub 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 3 entities, each in its own folder: Event Listings (event-listings), Events (events), Venues (venues). See below for a full field breakdown, sample counts, and data distributions for each.

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Event Listings

Per-event ticket listings from StubHub with section, row, seat, quantity, delivery type, ticket class, and creation timestamp.

94,457,940 total records from 2024-03-31 to 2026-03-08, 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 (numeric, e.g., 9833690568)
eventId	string	100%	Event ID this listing belongs to (join with stubhub_events)
price 🔒	float	100%	Ticket price in dollars including all fees (e.g., 212.35)
faceValue 🔒	float	84%	Face value of ticket in dollars (original printed price, 0 or null if not available)
section	string	100%	Section name/number (e.g., 116, 325, 104)
row	string	92%	Row within section - letter (A, B, GG), numeric (1-20+), or null if unassigned
seat	string	38%	Seat range (e.g., "5_6", "1_6", "12_13") or null if unassigned
seatFrom	string	33%	Starting seat number (e.g., "1", "5", "12")
seatTo	string	11%	Ending seat number (e.g., "6", "13")
quantity	float	100%	Number of tickets available in this listing (1-25, typically 2-8)
availableQuantities	array	100%	Purchasable quantities (e.g., [1,2,3,4] means you can buy 1, 2, 3, or 4 tickets)
ticketClass	float	100%	Ticket class ID (e.g., 594=Lower, 407=Upper, 954=Upper Level)
ticketClassName	string	100%	Ticket class name (Lower, Upper, Mezzanine, Club Level, Plaza Level, etc.)
ticketTypeId	float	100%	Ticket type ID (10=Mobile Transfer, 11=Mobile, 9=Mobile Entry, 1=Print-at-Home)
ticketTypeName	string	100%	Ticket delivery type (Mobile Transfer ticket, Mobile ticket, Print-at-Home ticket)
listingTypeId	float	100%	Listing type ID (1=standard ~95%, 14=other ~5%)
starRating 🔒	float	98%	Deal star rating 1-5 (5=best deal, null ~0.5-11% of listings)
dealScore 🔒	float	98%	Deal quality score 0-10 (e.g., 9.676, higher=better value)
discount 🔒	float	93%	Discount factor vs avg price (e.g., 0.798=~80% off avg, negative=above avg)
seatQualityScore 🔒	float	98%	Seat quality score (e.g., 4.533, higher=better seat position)
isSeatedTogether	bool	100%	Whether tickets are seated together (true ~94-96%, false ~4-6%)
isSpeculativeRow	bool	100%	Whether row is speculative/unconfirmed (true ~1-4%, false ~96-99%)
listingNotes	array	100%	Listing notes/disclosures (Clear view, Front Row of Section, Limited view, Aisle seat, etc.)
createdAt	datetime	100%	Listing creation timestamp

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

Field Distributions

Delivery Type Distribution (ticketTypeName)

Value	Count	Share
Mobile Transfer ticket	71,032,025	███████████████░░░░░ 75.2%
Mobile ticket	22,086,749	█████░░░░░░░░░░░░░░░ 23.4%
Print-at-Home ticket	1,212,750	░░░░░░░░░░░░░░░░░░░░ 1.3%
Physical ticket	126,992	░░░░░░░░░░░░░░░░░░░░ 0.1%

Top Ticket Classes (ticketClassName)

Value	Count	Share
Upper	21,823,150	████████░░░░░░░░░░░░ 42.1%
Lower	13,434,664	█████░░░░░░░░░░░░░░░ 25.9%
Balcony	4,033,147	██░░░░░░░░░░░░░░░░░░ 7.8%
Upper Level	2,449,050	█░░░░░░░░░░░░░░░░░░░ 4.7%
Middle	2,120,551	█░░░░░░░░░░░░░░░░░░░ 4.1%
Mezzanine	1,732,706	█░░░░░░░░░░░░░░░░░░░ 3.3%
Floor	1,713,651	█░░░░░░░░░░░░░░░░░░░ 3.3%
Upper Tier	1,599,325	█░░░░░░░░░░░░░░░░░░░ 3.1%
Orchestra	1,505,770	█░░░░░░░░░░░░░░░░░░░ 2.9%
200 Level	1,415,919	█░░░░░░░░░░░░░░░░░░░ 2.7%

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Events

Daily snapshot of StubHub events with start time, venue ID, availability state, and event type flags for market-level tracking.

6,783 total records from 2025-10-05 to 2026-03-08, up to 6,783 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 StubHub event ID (e.g., 159257629)
name	string	100%	Full event name (e.g., Arizona Diamondbacks at Los Angeles Dodgers)
url	string	100%	Full StubHub URL for the event
eventStartDatetime	datetime	100%	Event start datetime (UTC)
isTbd	bool	100%	Event datetime is TBD (to be determined)
isDateConfirmed	bool	100%	Event date is confirmed
isTimeConfirmed	bool	100%	Event time is confirmed
eventState	float	100%	Event state code (1=active, 4=postponed, 5=cancelled, 6=unknown, 11=TBD)
eventAvailabilityState	float	100%	Event availability state (0=available, 1=limited, 2=soldout)
venueId	float	100%	StubHub venue ID (join with stubhub_venues)
minPrice 🔒	float	97%	Minimum ticket price in dollars
medianPriceBucket	float	77%	Median price bucket (0-3 scale)
isUnderHundred	bool	100%	Event has tickets under $100
hasActiveListings	bool	100%	Event has active ticket listings
ticketsRemaining 🔒	float	2%	Number of tickets remaining on StubHub
isFastSelling 🔒	bool	30%	Event is fast selling (top 10% of daily sales)
onSaleDateTime	datetime	77%	When tickets go on sale (UTC)
rescheduledFromDate	string	0%	Original date if event was rescheduled
isParkingEvent	bool	100%	Event is a parking pass
isMultidayEvent	bool	100%	Event spans multiple days

🔒 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 State Distribution (eventState)

Value	Count	Share
1	5,588	████████████████░░░░ 82.4%
11	1,036	███░░░░░░░░░░░░░░░░░ 15.3%
6	89	░░░░░░░░░░░░░░░░░░░░ 1.3%
4	69	░░░░░░░░░░░░░░░░░░░░ 1.0%
5	1	░░░░░░░░░░░░░░░░░░░░ 0.0%

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Venues

StubHub venue directory with name, city, country, and timezone offset for geographic and venue-level event analysis.

180 total records from 2025-10-05 to 2026-03-08, 180 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 StubHub venue ID (e.g., 1817)
name	string	100%	Venue name (e.g., Dodger Stadium)
addressCity	string	100%	Venue city (e.g., Los Angeles)
addressFull	string	100%	Full venue location (e.g., Los Angeles, CA, USA)
addressCountryCode	string	100%	Country code (US, CA, GB, etc.)
addressCountry	string	100%	Full country name (USA, Canada, etc.)
timezoneOffset	float	99%	Timezone offset in milliseconds from UTC

Field Distributions

Venues by Country (addressCountryCode)

Value	Count	Share
US	164	██████████████████░░ 91.1%
CA	8	█░░░░░░░░░░░░░░░░░░░ 4.4%
GB	3	░░░░░░░░░░░░░░░░░░░░ 1.7%
DE	2	░░░░░░░░░░░░░░░░░░░░ 1.1%
MX	1	░░░░░░░░░░░░░░░░░░░░ 0.6%
ES	1	░░░░░░░░░░░░░░░░░░░░ 0.6%
SE	1	░░░░░░░░░░░░░░░░░░░░ 0.6%

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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:

Event Listings

High Deal Score Listings (8+) — 17,291,405 records

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

Listings with Face Value Data — 76,667,802 records

↳ [{"field":"faceValue","op":"isNotEmpty"},{"sort":"price ASC"}]

Mobile Transfer Ticket Listings — 61,707,626 records

↳ [{"field":"ticketTypeName","op":"is","value":"Mobile Transfer ticket"},{"sort":"price ASC"}]

Lower Level Ticket Listings — 12,032,205 records

↳ [{"field":"ticketClassName","op":"is","value":"Lower"},{"sort":"price ASC"}]

Multi-Ticket Listings (4+ tickets) — 40,966,584 records

↳ [{"field":"quantity","op":"gte","value":4},{"sort":"quantity DESC"}]

See all 25 views →

Events

Events with Active Listings — 2,400 records

↳ [{"field":"hasActiveListings","op":"isTrue"},{"sort":"eventStartDatetime ASC"}]

Active Events (Not Postponed/Cancelled) — 2,268 records

↳ [{"field":"eventState","op":"eq","value":1},{"sort":"eventStartDatetime ASC"}]

Fast Selling Events — 1,921 records

↳ [{"field":"isFastSelling","op":"isTrue"},{"sort":"minPrice ASC"}]

Upcoming Events (Next 30 Days) — 2,442 records

↳ [{"field":"eventStartDatetime","op":"gte","value":"now"},{"field":"eventStartDatetime","op":"lte","value":"now+30d"},{"sort":"eventStartDatetime ASC"}]

Events with Tickets Under $50 — 1,560 records

↳ [{"field":"minPrice","op":"lt","value":50},{"sort":"minPrice ASC"}]

See all 19 views →

Venues

United States Venues — 29 records

↳ [{"field":"addressCountryCode","op":"is","value":"US"},{"sort":"name ASC"}]

Canada Venues — 8 records

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

International Venues (Non-US) — 16 records

↳ [{"field":"addressCountryCode","op":"isNot","value":"US"},{"sort":"addressCountry ASC"}]

North America Venues — 37 records

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

Venues by City — 45 records

↳ [{"sort":"addressCity ASC"}]

See all 17 views →

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Code Examples

import pandas as pd
from pathlib import Path

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

# Top 10 cities by number of venues
print(venues['addressCity'].value_counts().head(10).to_string())

# Venues by country
print(venues.groupby('addressCountry').size().sort_values(ascending=False).to_string())

# All venues in a specific city
nyc = venues[venues['addressCity'] == 'New York']
print(nyc[['name', 'addressFull']].to_string(index=False))

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

# Events by availability state (0=available, 1=limited, 2=soldout)
print(events['eventAvailabilityState'].value_counts().to_string())

# Active events with confirmed dates
confirmed = events[(events['eventState'] == 1) & (events['isTbd'] == False)]
print(f"Confirmed active events: {len(confirmed)}")

# Events with tickets under $100
print(f"Budget-friendly events: {events['isUnderHundred'].sum()}")

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

# Listings by ticket delivery type
print(listings['ticketTypeName'].value_counts().to_string())

# Average quantity per listing by ticket class
print(listings.groupby('ticketClassName')['quantity'].mean()
      .sort_values(ascending=False).head(10).to_string())

# Seated-together percentage
pct = listings['isSeatedTogether'].mean() * 100
print(f"Seated together: {pct:.1f}%")

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Use Cases

Resale Inventory Analysis

Study ticket listing patterns across event types and venues. Analyze how section, row, and delivery method affect inventory distribution in the secondary market.

Event Supply Tracking

Monitor listing velocity for upcoming events. Identify which events have the most active resale inventory and how supply changes as event dates approach.

Venue Seating Research

Map seating section distribution across venues. Compare ticket class breakdowns (Lower, Upper, Floor, Mezzanine) to understand venue layout patterns and listing density.

Delivery Method Trends

Track the shift from physical to mobile ticket delivery across event categories. Analyze which delivery types dominate by event type and venue.

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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/stubhub

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 filters on any field and sort by any column. Preview results before purchasing. You only pay for records that match your criteria.
• 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_stubhub,
  author       = {Rebrowser},
  title        = {StubHub Ticket Marketplace Dataset},
  year         = {2026},
  howpublished = {\url{https://rebrowser.net/products/datasets/stubhub}},
  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 StubHub. Any trademarks are the property of their respective owners. This dataset is compiled from publicly available information; we do not request or collect StubHub user credentials. By using this dataset, you agree to comply with StubHub'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.