how_to

Building a SaaS with Pre-Match Football Odds Data

By Daniel

Building a SaaS with Pre-Match Football Odds Data

Building a SaaS product that relies on sports odds data can be a complex undertaking. The core challenge often isn't the application logic itself, but reliably sourcing up-to-date, structured pre-match football odds from multiple UK bookmakers. Trying to scrape this data yourself is a fast track to rate limits, CAPTCHAs, and endless maintenance.

Instead of fighting against constantly changing website layouts, a dedicated odds API offers a stable, normalised data feed. This guide will walk you through building a SaaS with odds data using a robust UK bookmaker odds API, focusing on efficient integration and avoiding common pitfalls like manual scraping. We'll cover how to fetch scheduled fixtures, retrieve detailed pre-match odds, and integrate the best prices into your application.

abstract data flow into a cloud, representing a SaaS platform processing sports odds

Prerequisites for Odds Data Integration

Before you start integrating pre-match football odds into your SaaS, ensure you have the following:

  • API Key: You'll need an API key from ukoddsapi.com to authenticate your requests. You can sign up for a free account to get started.
  • Programming Language: This guide uses Python, but the concepts apply to any language capable of making HTTP requests (e.g., Node.js, PHP, Ruby).
  • HTTP Client Library: For Python, the requests library is standard. You can find its documentation here. For Node.js, fetch or axios work well.
  • Basic API Knowledge: Familiarity with REST APIs, JSON data structures, and HTTP methods (GET) will be helpful. For detailed endpoint information, refer to our API documentation.
  • Environment Variables: Store your API key as an environment variable (UKODDSAPI_KEY) for security.

Step 1: Discovering Upcoming Football Fixtures

The first step in building a SaaS with odds data is to identify the football matches for which you want to retrieve odds. The /v1/football/events endpoint provides a list of scheduled fixtures for a given date. This allows your application to dynamically fetch events rather than hardcoding them.

Here's how to fetch today's football events with available pre-match odds using Python:

python import os import requests from datetime import date

API_KEY = os.environ.get("UKODDSAPI_KEY", "YOUR_API_KEY") BASE = "https://api.ukoddsapi.com" headers = {"X-Api-Key": API_KEY}

today = date.today().isoformat() try: events_response = requests.get( f"{BASE}/v1/football/events", headers=headers, params={"schedule_date": today, "has_odds": "true", "per_page": "10"}, timeout=30, ) events_response.raise_for_status() # Raise an exception for HTTP errors events_data = events_response.json()

print("Upcoming Football Events Today:")
for event in events_data.get("events", []):
    print(f"  ID: {event['event_id']}, Match: {event['home_team']} vs {event['away_team']}, League: {event['league_name']}")

# Store the first event_id for later use in subsequent steps
first_event_id = None
if events_data.get("events"):
    first_event_id = events_data["events"][0]["event_id"]
    print(f"\nFirst event ID for detailed odds: {first_event_id}")
else:
    print("\nNo events found with odds for today.")

except requests.exceptions.RequestException as e: print(f"Error fetching events: {e}") first_event_id = None


The code above makes a GET request to the `/v1/football/events` endpoint, filtering for events on the current date that `has_odds=true`. The `per_page` parameter limits the number of results. It then prints a summary of each event and stores the `event_id` of the first match found, which is crucial for fetching specific odds in the next step.

Here's a truncated example of the **pre-match football odds JSON** response you might receive:

{
  "schema_version": "1.0",
  "count": 2,
  "events": [
    {
      "event_id": "EV01234567890A",
      "league_name": "Premier League",
      "home_team": "Manchester City",
      "away_team": "Liverpool",
      "kickoff_utc": "2026-04-25T15:00:00Z",
      "markets_with_odds": ["match_betting", "total_goals"],
      "unique_bookmaker_codes": ["UO001", "UO002", "UO003"]
    },
    {
      "event_id": "EV01234567890B",
      "league_name": "Championship",
      "home_team": "Everton",
      "away_team": "Fulham",
      "kickoff_utc": "2026-04-25T17:30:00Z",
      "markets_with_odds": ["match_betting"],
      "unique_bookmaker_codes": ["UO001", "UO004"]
    }
  ],
  "note": "Response truncated for brevity."
}


This response provides essential details like `event_id`, team names, league, and kickoff time, allowing your SaaS to display a list of upcoming fixtures.

Step 2: Fetching Detailed Pre-Match Odds for an Event

Once you have an event_id from Step 1, you can retrieve the full set of pre-match odds for that specific football fixture. The /v1/football/events/{event_id}/odds endpoint is designed for this. It allows you to specify the package (e.g., core for standard markets or full for advanced markets on higher tiers) and odds_format (e.g., decimal).

This is a core part of any building a SaaS with odds data integration, as it provides the granular pricing information.

import os
import requests

# Assuming first_event_id was obtained from Step 1
# For demonstration, let's use a placeholder if running independently
event_id_to_fetch = os.environ.get("TEST_EVENT_ID", "EV01234567890A")

if event_id_to_fetch:
    API_KEY = os.environ.get("UKODDSAPI_KEY", "YOUR_API_KEY")
    BASE = "https://api.ukoddsapi.com"
    headers = {"X-Api-Key": API_KEY}

    try:
        odds_response = requests.get(
            f"{BASE}/v1/football/events/{event_id_to_fetch}/odds",
            headers=headers,
            params={"package": "core", "odds_format": "decimal"},
            timeout=60,
        )
        odds_response.raise_for_status()
        odds_data = odds_response.json()

        print(f"\nPre-Match Odds for {odds_data.get('event_title')}:")
        for market in odds_data.get("markets", []):
            print(f"  Market: {market['market_name']} ({market['market_group']})")
            for selection in market.get("selections", []):
                bookmaker_codes = ", ".join(s['bookmaker_code'] for s in selection['odds'])
                print(f"    Selection: {selection['selection_name']}, Odds from: {bookmaker_codes}")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching detailed odds for {event_id_to_fetch}: {e}")
else:
    print("No event ID available to fetch detailed odds.")

This Python snippet fetches the detailed pre-match football odds JSON for a specific event_id. It requests core markets in decimal format. The response includes an array of markets, each containing selections with odds from various bookmakers. This structure is ideal for parsing and presenting comprehensive odds data within your SaaS.

A simplified JSON response for detailed odds might look like this:



{
  "schema_version": "1.0",
  "event_id": "EV01234567890A",
  "event_title": "Manchester City vs Liverpool",
  "kickoff_utc": "2026-04-25T15:00:00Z",
  "markets": [
    {
      "market_id": "MKT001",
      "market_name": "Match Betting",
      "market_group": "main",
      "selections": [
        {
          "selection_name": "Manchester City",
          "odds": [
            { "bookmaker_code": "UO001", "odds": 1.80, "status": "active" },
            { "bookmaker_code": "UO002", "odds": 1.75, "status": "active" }
          ]
        },
        {
          "selection_name": "Draw",
          "odds": [
            { "bookmaker_code": "UO001", "odds": 3.50, "status": "active" },
            { "bookmaker_code": "UO002", "odds": 3.40, "status": "active" }
          ]
        }
      ]
    }
  ],
  "note": "Response truncated for brevity."
}

This data lets you display all available odds for each selection across different bookmakers, a fundamental feature for any odds comparison or betting analysis SaaS.

Step 3: Integrating Best Odds into Your SaaS

For many SaaS applications, especially odds comparison sites, displaying the best available price for each selection is critical. The /v1/football/events/{event_id}/odds/best endpoint streamlines this by returning only the top price from all covered bookmakers for each selection. This saves you the work of iterating through all bookmakers and finding the maximum value yourself.

This approach is highly efficient for building a SaaS with odds data where performance and user experience are key.

import os
import requests

# Assuming event_id_to_fetch is available from previous steps
event_id_to_fetch = os.environ.get("TEST_EVENT_ID", "EV01234567890A")

if event_id_to_fetch:
    API_KEY = os.environ.get("UKODDSAPI_KEY", "YOUR_API_KEY")
    BASE = "https://api.ukoddsapi.com"
    headers = {"X-Api-Key": API_KEY}

    try:
        best_odds_response = requests.get(
            f"{BASE}/v1/football/events/{event_id_to_fetch}/odds/best",
            headers=headers,
            params={"odds_format": "decimal"},
            timeout=60,
        )
        best_odds_response.raise_for_status()
        best_odds_data = best_odds_response.json()

        print(f"\nBest Pre-Match Odds for {best_odds_data.get('event_title')}:")
        for market in best_odds_data.get("markets", []):
            print(f"  Market: {market['market_name']}")
            for selection in market.get("selections", []):
                best_price = selection['best_odds'][0] # Best odds are usually sorted
                print(f"    Selection: {selection['selection_name']}, Best Odds: {best_price['odds']} ({best_price['bookmaker_name']})")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching best odds for {event_id_to_fetch}: {e}")
else:
    print("No event ID available to fetch best odds.")

This code snippet retrieves the best odds for each selection in a given football match. The best_odds array within each selection will contain the highest price and the corresponding bookmaker. This is ideal for quickly populating comparison tables or highlighting value bets in your SaaS.

Here's an example of the pre-match football odds JSON response for best odds:



{
  "schema_version": "1.0",
  "event_id": "EV01234567890A",
  "event_title": "Manchester City vs Liverpool",
  "markets": [
    {
      "market_id": "MKT001",
      "market_name": "Match Betting",
      "selections": [
        {
          "selection_name": "Manchester City",
          "best_odds": [
            { "odds": 1.80, "bookmaker_code": "UO001", "bookmaker_name": "10Bet" }
          ]
        },
        {
          "selection_name": "Draw",
          "best_odds": [
            { "odds": 3.50, "bookmaker_code": "UO001", "bookmaker_name": "10Bet" }
          ]
        },
        {
          "selection_name": "Liverpool",
          "best_odds": [
            { "odds": 4.20, "bookmaker_code": "UO003", "bookmaker_name": "Betfair Sportsbook" }
          ]
        }
      ]
    }
  ],
  "note": "Response truncated for brevity."
}

This structured data makes it straightforward to build dynamic front-ends for your SaaS, ensuring users always see the most competitive pre-match prices. Remember to refresh this data periodically to keep your odds current, but be mindful of your API's rate limits.

Common Mistakes When Building a SaaS with Odds Data

Integrating external data feeds always comes with potential pitfalls. When building a SaaS with odds data, developers often encounter specific challenges. Avoiding these can save significant development time and ensure your application remains reliable.

  • Ignoring Rate Limits: Hitting the API too frequently will result in 429 Too Many Requests errors. Implement exponential backoff and intelligent caching strategies to reduce the number of API calls.
  • Confusing Pre-Match with In-Play: UK Odds API provides pre-match odds. Do not design your SaaS to expect real-time, sub-second in-play updates, as this is not the product's scope.
  • Poor Error Handling: Network issues, invalid event_ids, or API key problems can disrupt your data flow. Always wrap API calls in try-except blocks and log errors for debugging.
  • Not Caching Data: Fetching the same odds repeatedly is inefficient and wastes API requests. Cache pre-match odds locally for a reasonable period (e.g., 5-10 minutes) before re-fetching.
  • Hardcoding Bookmaker Names: Bookmaker names can change. Use the stable bookmaker_code (e.g., UO001) provided by the API for internal logic and map to display names in your UI.
  • Building Your Own Scraper: Attempting to build an in-house odds API without scraping is a common mistake. Bookmakers actively block scrapers, leading to constant maintenance and unreliable data.

Options and Alternatives for Sourcing Odds Data

When considering building a SaaS with odds data, you have a few options for sourcing that data. Each comes with its own set of trade-offs in terms of reliability, cost, and development effort.

abstract comparison chart, showing different data sourcing methods with pros and cons

Here's a comparison of common approaches:

Method Pros Cons Best For
Web Scraping (DIY) Full control, no direct API cost Unreliable, high maintenance, IP blocks, legal risks Niche, very low-volume, personal projects
Generic Sports Data APIs Broad sports coverage, some odds Limited bookmaker depth, less UK-focused, higher latency General sports news, non-betting applications
Specialised Odds APIs (e.g., UK Odds API) Reliable, normalised data, UK-focused, stable Subscription cost, specific sport coverage (football) Odds comparison, betting tools, arbitrage finders

FAQ

How often can I update pre-match odds for my SaaS?

The update frequency depends on your API plan and rate limits. For most pre-match data, polling every 5-10 minutes is sufficient to keep your odds fresh without hitting limits. Implement caching to avoid unnecessary requests.

What if an event ID doesn't return any odds data?

If an event_id returns no odds, it could mean the event has been cancelled, postponed, or no bookmakers are currently offering odds for it. Implement error handling to gracefully manage these cases, perhaps by hiding the event or displaying a "No Odds Available" message.

Can I filter events or odds by specific criteria like league or team?

The /v1/football/events endpoint allows filtering by schedule_date and has_odds. For more granular filtering by league or team, you would typically fetch a broader list of events and then filter the results within your application logic.

How should I handle different odds formats (e.g., decimal vs. fractional)?

The UK Odds API allows you to request odds in decimal format, which is generally easier to work with programmatically. If your SaaS needs to display fractional odds, you can convert them from decimal within your application.

What's the best way to secure my API key within a SaaS application?

Always store your API key as an environment variable and access it server-side. Never embed your API key directly in client-side code (e.g., JavaScript in a browser), as this exposes it to users and risks compromise.

Conclusion

Building a SaaS with odds data doesn't have to be a constant battle against web scraping and unreliable data. By leveraging a dedicated UK bookmaker odds API, developers can integrate high-quality, normalised pre-match football odds JSON efficiently. This approach ensures your application remains stable, scalable, and provides accurate data to your users.

Ready to integrate reliable football odds into your application? Explore the possibilities and get started with UK Odds API today.