When we started building a real-time sports odds platform, we thought the biggest challenge would be scraping bookmaker websites.
We were wrong.
Scraping turned out to be just the first 10% of the problem.
The real challenge was building an infrastructure capable of collecting, processing, normalizing, and delivering thousands of live updates every second without falling behind.
Here are five things we learned along the way.
1. Scraping Is Easy. Keeping It Running Isn't.
The first scraper is always exciting.
A few HTTP requests.
A bit of parsing.
Some JSON.
Everything works.
Then a bookmaker changes its frontend.
One endpoint disappears.
Cloudflare gets updated.
A field gets renamed.
Suddenly you're debugging production at 2 AM because football matches have stopped updating.
The difficult part isn't writing a scraper.
It's maintaining dozens of them simultaneously while your users expect everything to work 24/7.
2. Every Bookmaker Has Its Own Definition of "The Same Match"
Imagine Liverpool vs Arsenal.
Sounds simple.
Now compare how different bookmakers describe that exact event.
One uses:
Liverpool
Arsenal
Another returns:
Liverpool FC
Arsenal FC
A third includes league prefixes.
Another changes market names completely.
Some identify events with numeric IDs.
Others don't expose IDs at all.
Before you can compare odds, calculate arbitrage, or build analytics, you first need to solve a surprisingly difficult data problem:
How do you know these are actually the same event?
Matching events reliably became a much bigger engineering challenge than we originally expected.
3. Real-Time Means More Than "Fast"
People often ask:
"How often do odds actually change?"
The answer surprised us.
During busy periods, thousands of odds can change within seconds.
That means your infrastructure has to:
detect changes quickly
process updates efficiently
avoid sending duplicate data
keep latency low
recover automatically when a source fails
Refreshing every 30 or 60 seconds simply isn't enough for many live applications.
Real-time isn't a feature.
It's an architecture.
4. Monitoring Is More Important Than Code
One lesson we learned very early:
If you don't know something is broken, it is broken.
A scraper can silently stop returning one market.
A bookmaker might remove an endpoint.
A WebSocket connection might stay open but stop delivering updates.
Without proper monitoring, everything appears healthy while your users are looking at stale data.
Today we spend almost as much time improving monitoring and alerting as writing new features.
Reliable systems aren't built by accident.
5. Developers Don't Want Raw Data. They Want Consistency.
One of the biggest pieces of feedback we received was surprisingly simple.
Developers don't mind consuming APIs.
They mind consuming different APIs.
If every bookmaker has a different structure, naming convention, and market format, integration becomes painful.
We realized the real value wasn't just collecting sports odds.
It was presenting them through one consistent interface.
Once the data is normalized, developers can focus on building products instead of writing conversion logic.
Looking Back
When we started this project, we thought we were building a collection of scrapers.
In reality, we ended up building a data platform.
The scrapers became just one small component.
Most of the engineering effort went into reliability, normalization, monitoring, and delivering low-latency updates at scale.
That journey eventually became PulseScore.
It's a real-time sports odds API that aggregates bookmakers including Bet365, DraftKings, FanDuel, Ladbrokes, Bwin, Paddy Power, PS3838, William Hill, BetOnline, Unibet AU and others into a single, developer-friendly REST API and WebSocket platform.
Current capabilities include:
Live odds updated every second
Pregame odds from multiple bookmakers
REST API + WebSocket streaming
Unified JSON responses
Football, Basketball, Tennis, Ice Hockey, Baseball, Horse Racing, Rugby, American Football, Volleyball, Table Tennis, eSports, Greyhounds and many more sports
We originally built it to solve our own infrastructure problems.
Eventually, we realized other developers were fighting exactly the same battles.
If you're building a sports analytics platform, an odds comparison tool, a trading application, or anything that relies on live bookmaker data, you can explore the API at https://pulsescore.net.
I'd genuinely love to hear from other developers working with real-time data.
What's been the most unexpected engineering challenge you've encountered while building systems that need to stay in sync with constantly changing information?
Top comments (0)