Scraping Ticketmaster Event Data (easy and quick method)

Ticketmaster is one of the largest online ticket marketplaces, listing thousands of events worldwide.

But if you’ve tried scraping Ticketmaster, you’ve probably hit roadblocks.

The platform actively prevents automated access through IP tracking and JavaScript-rendered pages, making traditional scraping methods unreliable.

But as always, there’s a way around it.

In this guide, we’ll break down why Ticketmaster is difficult to scrape, how its defenses work, and how to bypass them to extract event data like dates, venues, and locations.

Why Scraping Ticketmaster Is Challenging

Compared to heavily protected sites like Amazon or LinkedIn, Ticketmaster isn’t impossible to scrape; but it does have some defenses in place.

The biggest challenges come from IP tracking and JavaScript-rendered content, which can block or disrupt scrapers that don’t handle requests properly.

IP-based blocking

Ticketmaster doesn’t impose aggressive rate limits, but it does monitor incoming traffic.

Datacenter IPs are easily flagged; requests coming from cheap datacenter proxies will be easily blocked.
Too many rapid requests from the same IP can lead to CAPTCHAs or temporary access restrictions.

This means a simple script running from a fixed IP might work for a while, but won’t hold up for larger-scale scraping.

JavaScript-loaded data

Most event details aren’t included in the raw HTML. Instead, Ticketmaster loads event information dynamically using JavaScript.

A standard requests.get() call won’t retrieve event data directly.
Instead, you need to parse JSON data from embedded scripts or use a browser automation tool for full page rendering.

Fortunately, there’s a way to work around these challenges without overcomplicating your setup.

How Scrape.do Makes It Easy

Instead of dealing with IP bans, session tracking, and JavaScript-rendered content manually, you can use Scrape.do to handle it for you.

✅ Bypass IP Blocks: Scrape.do automatically routes requests through a rotating proxy pool, including residential IPs, making it look like a normal user.
✅ Access JavaScript-Rendered Data: Need fully loaded pages? Scrape.do can render JavaScript when needed, ensuring you don’t miss critical details.
✅ No More CAPTCHAs: Ticketmaster sometimes triggers CAPTCHAs on repeated requests, but Scrape.do’s system automatically retries and bypasses them.

With this setup, you can scrape Ticketmaster efficiently without worrying about blocks; even at scale.

Extracting Event Data from Ticketmaster

Before extracting event details, we need to ensure our request reaches the page successfully and returns a 200 OK response.

Since Ticketmaster loads event data dynamically, we’ll inspect the page and extract the JSON data directly instead of scraping the rendered HTML.

Prerequisites

Install the required dependencies if you haven’t already:

pip install requests beautifulsoup4

You’ll also need an API key from Scrape.do, which you can get by signing up for free.

For this guide, we’ll scrape events from my favorite singer recently, Post Malone.

Click to view full-size image

You can also scrape search results for events in certain locations, venues, genres, etc.

Sending a request and verifying access

We’ll include a few optional Scrape.do API parameters in our request to ensure we get access:

super=true enables Scrape.do’s super proxy mod, allowing you to access Ticketmaster through residential and mobile IPs.
geoCode=us ensures we receive data localized for the US market, avoiding redirects or missing events.
render=false on my attempts, I was able to extract even data because of how Ticketmaster is built, which I’ll talk about in a bit. Turning this off can also save you credits.

import requests
import urllib.parse

# Our token provided by Scrape.do
token = "<your_token>"

# Target Ticketmaster URL
target_url = urllib.parse.quote_plus("https://www.ticketmaster.com/post-malone-tickets/artist/2119390")

# Optional parameters
render = "false"
geo_code = "us"
super_mode = "true"

# Scrape.do API endpoint
url = f"https://api.scrape.do/?token={token}&url={target_url}&render={render}&geoCode={geo_code}&super={super_mode}"

# Send the request
response = requests.get(url)

# Print response status
print(response)

If everything is working correctly, you should see:

<Response [200]>

This confirms that the request was successful and we can now proceed with extracting event details.

💡 When web scraping, it’s always a great idea to see if you can first get a 200 OK response before starting parsing.

Extracting All Events from Ticketmaster’s JSON

Even though Ticketmaster renders pages dynamically with JavaScript, all event data is embedded as JSON in the page source under the application/ld+json script tag.

This makes scraping simpler since we don’t need to render JavaScript; we can extract and parse the JSON directly.

Why does Ticketmaster include JSON data?

Ticketmaster uses event listing schema markup (schema.org/MusicEvent) to provide structured data for search engines like Google. This helps their events get indexed, appear in search results, and drive more traffic.

For scrapers, this is a huge advantage because:

We don’t need to execute JavaScript or interact with the page dynamically.
The JSON already contains structured event data, reducing the need for complex parsing.
We can extract everything in a single request, making the scraper faster and more efficient.

Examining the JSON return, you can find the details we need are listed as:

Click to view full-size image

So we need to parse them using the json library.

This is the final script that you need to scrape all event details from a page on Ticketmaster:

import requests
import re
import json

# Our token provided by Scrape.do
token = "<your_token>"

# Target Ticketmaster URL
target_url = "https://www.ticketmaster.com/post-malone-tickets/artist/2119390"

# Optional parameters
render = "false"
geo_code = "us"
super_mode = "true"

# Scrape.do API endpoint
url = f"https://api.scrape.do/?token={token}&url={target_url}&render={render}&geoCode={geo_code}&super={super_mode}"

# Send the request
response = requests.get(api_url)

# Extract JSON data using regex
match = re.search(r'<script type="application/ld\+json">(.*?)</script>', response.text, re.DOTALL)
json_data = json.loads(match.group(1) if match else "[]")

# Loop through all events and extract details
for event in json_data:
    print(f"Event: {event['name']}")
    print(f"Date: {event['startDate']}")
    print(f"Venue: {event['location']['name']}")
    print(f"Location: {event['location']['address']['addressLocality']}, {event['location']['address']['addressRegion']}\n")

And you should be getting an output formatted as such:

Event: Houston Rodeo w/ Post Malone
Date: 2025-03-18T18:45:00
Venue: NRG Stadium
Location: Houston, TX

Event: Post Malone Presents: The BIG ASS Stadium Tour
Date: 2025-04-29T19:30:00
Venue: Rice-Eccles Stadium
Location: Salt Lake City, UT

Event: Post Malone Presents: The BIG ASS Stadium Tour
Date: 2025-05-03T19:30:00
Venue: Allegiant Stadium
Location: Las Vegas, NV

...

And voila! You’ve extracted all event details from Ticketmaster! 🎉

Conclusion

Scraping Ticketmaster is easier than many other platforms because it includes structured event data in JSON format.

Instead of dealing with JavaScript rendering or complex HTML parsing, we can extract all event details directly from the page source in a single request.

With Scrape.do handling IP rotation, anti-bot bypassing, and geo-restricted access, you don’t have to worry about getting blocked or missing data.

If you need to scrape Ticketmaster at scale, Scrape.do makes the process effortless:

✅ Bypasses IP blocks and CAPTCHAs automatically
✅ Extracts structured event data without rendering JavaScript
✅ Works at scale without rate limits

Get 1000 free API calls and start scraping today.