DEV Community: E. Mitev

5 Things We Didn't Expect While Building a Real-Time Sports Odds API

E. Mitev — Sat, 04 Jul 2026 14:14:40 +0000

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?

Why Most Sports Betting Projects Fail Before Launch (And It's Not the Algorithm)

E. Mitev — Sat, 13 Jun 2026 15:41:43 +0000

If you've ever tried building a sports betting application, odds tracker, arbitrage scanner, value betting tool, or sports analytics dashboard, you've probably experienced the same thing:

You start with the exciting part.

The idea.

The algorithm.

The UI.

The business logic.

And then reality hits.

The Hidden Problem Nobody Talks About

Most developers assume the hardest part of a betting-related project is the prediction model or arbitrage logic.

In practice, the real challenge is data infrastructure.

Before your project can calculate anything, you need:

Live events
Accurate odds
Multiple bookmakers
Consistent market structures
Historical updates
Reliable refresh rates

And suddenly your "weekend project" turns into a full-time data engineering job.

The Scraping Trap

Most developers begin by scraping bookmaker websites.

At first it seems simple:

Open DevTools
Find the API request
Parse the response
Save the data

Done, right?

Not quite.

Within a few weeks you'll likely encounter:

Changed endpoints
Rate limits
Cloudflare protection
Different JSON formats
Missing markets
Broken parsers
Increased maintenance costs

Instead of improving your product, you're fixing scrapers.

Again.

And again.

Every Bookmaker Speaks a Different Language

Let's say you want to compare odds from five sportsbooks.

You quickly discover that every provider structures data differently.

One bookmaker might return:

{
  "home": "Liverpool",
  "away": "Arsenal"
}

Another might return:

{
  "team1": "Liverpool",
  "team2": "Arsenal"
}

A third one could use:

{
  "participants": [
    "Liverpool",
    "Arsenal"
  ]
}

Now multiply that problem across:

dozens of bookmakers
hundreds of leagues
thousands of events

You end up spending more time normalizing data than building features.

Real-Time Data Changes Everything

Many projects work perfectly during testing.

Then live data arrives.

Odds can move multiple times within a minute.

If your system refreshes too slowly:

arbitrage opportunities disappear
alerts become useless
dashboards display outdated information

This is why refresh frequency matters more than most developers initially realize.

Real-time applications require a completely different approach than static datasets.

What Developers Should Focus On Instead

The most successful projects usually spend their development time on:

Opportunity detection
Analytics
User experience
Visualization
Trading logic
Notifications
Automation

Not on collecting raw bookmaker data.

The data layer should be the foundation, not the project itself.

Build Features, Not Infrastructure

A useful exercise is asking yourself:

"If all sports data magically appeared in my database today, what would I build?"

That's usually the actual product.

Maybe it's:

An arbitrage scanner
A line movement tracker
A betting dashboard
A prediction model
A value betting system
A Telegram alert bot

Those are the things users care about.

Not how many nights you spent maintaining scrapers.

Final Thoughts

Sports betting development is becoming increasingly competitive.

The difference between successful projects and abandoned repositories often isn't the algorithm.

It's whether the developer can spend time building value instead of maintaining data pipelines.

The sooner you separate data collection from product development, the faster you'll be able to ship something people actually want to use.

And that's where most projects win or lose.

Stop Scraping Betting Sites: How to Build a Real-Time Sports Tracker in Python

E. Mitev — Thu, 21 May 2026 13:06:54 +0000

If you have ever tried to build a sports dashboard, an arbitrage scanner, or an automated trading bot, you already know the painful truth: scraping bookmaker data is a nightmare.

Sites like Bet365, FanDuel, and DraftKings have aggressive anti-bot protection (hello, Cloudflare). Even if you bypass it, they constantly change their HTML structure, breaking your scrapers every few weeks. Plus, managing custom WebSocket connections for live in-play odds is an infrastructure money pit.

In this quick guide, we’ll build a real-time sports odds tracker in Python without writing a single line of scraper code.

To do this, we’ll use PulseScore, a real-time sports odds API that aggregates multiple bookmakers into a single, unified JSON shape. They have a completely free tier (no credit card required), which is perfect for this project.

🛠️ The Tech Stack

Python 3.x
Requests library (for polling pre-match data)
WebSockets library (for ultra-low latency live updates)

First, make sure you have the required packages installed:

pip install requests websockets

1️⃣ Fetching Pre-Match Odds (REST API)

One of the biggest headaches of multi-bookie apps is normalizing data. Every bookmaker names their markets and sports differently. PulseScore solves this by serving the same JSON structure whether you fetch from Bet365, DraftKings, or Bwin.

Here is how to fetch upcoming tennis or soccer matches using their clean REST endpoints:

import requests

# Get your free API key from [https://pulsescore.net](https://pulsescore.net)
API_KEY = "YOUR_FREE_SECRET_HEADER_KEY"
URL = "[https://api.pulsescore.net/v1/odds/bet365](https://api.pulsescore.net/v1/odds/bet365)" # You can easily swap /bet365 for /fanduel or /bwin

headers = {
    "X-Secret": API_KEY,
    "Accept": "application/json"
}

response = requests.get(URL, headers=headers)

if response.status_code == 200:
    data = response.json()
    # Let's print out the first few upcoming events
    for event in data.get("events", [])[:5]:
        print(f"Match: {event['home_team']} vs {event['away_team']}")
        print(f"Start Time: {event['start_time']}")
        print(f"1X2 Odds: {event['markets'].get('1x2')}\n")
else:
    print(f"Error: {response.status_code}, {response.text}")

2️⃣ Streaming Live In-Play Odds (WebSockets)

For live betting or real-time trading, a 10-second delay is unacceptable. WebSockets allow you to stream low-latency data straight into your application.

Here is how you can listen to live match updates with a 1-2 second refresh rate:

import asyncio
import websockets
import json

API_KEY = "YOUR_FREE_SECRET_HEADER_KEY"
WS_URL = f"wss://ws.pulsescore.net/live?secret={API_KEY}"

async def stream_live_sports():
    async with websockets.connect(WS_URL) as websocket:
        print("Successfully connected to PulseScore live stream...")

        while True:
            try:
                message = await websocket.recv()
                event_data = json.loads(message)

                # Print real-time shifts in lines/odds
                print(f"Live Update: {event_data['home_team']} vs {event_data['away_team']}")
                print(f"Current Score: {event_data['score']}")
                print(f"Live In-Play Odds: {event_data['live_markets']}\n")

            except websockets.ConnectionClosed:
                print("Connection closed, reconnecting...")
                break

asyncio.run(stream_live_sports())

💡 Why This Beats Custom Scrapers

Unified JSON Shape: You can swap /bet365 for /fanduel, /bwin, or /ps3838 in your code, and the payload format remains identical. No need to map data structures manually.
No IP Bans: You don't have to manage proxy rotation or deal with headless browsers.
Low Latency: Live events update every 1 second, keeping your data ahead of the curve.

Conclusion
Building sports tools should be about creating smart algorithms and beautiful dashboards, not fighting website layouts and Cloudflare blocks.eo

If you want to try this out yourself, head over to pulsescore.net, spin up a free plan (gives you 500 requests/mo right away), and test the payloads with the scripts above.

What are you currently building with sports data? Let me know in the comments below! ⚽🏀🎾