Odds data isn’t just another layer in your stack, it’s what drives real-time decisions, shapes user bets, and keeps your product competitive.
If you’re pulling in odds but just dumping them into your UI, you’re leaving a lot of value on the table. Done right, the odds endpoint can power everything from sharp prediction tools to dynamic in-play experiences that bettors actually trust.
In this guide, we’ll walk through how to make the most of your odds feed, step by step, covering real use cases that help you turn raw odds into useful features.
We’ll cover:
- Which odds markets you should focus on (and why)
- How to query and structure the data effectively
- What kind of analysis actually moves the needle
- How to display odds in ways that help users make decisions
- What to track and tweak once you’re live
We’ll be using examples that align with how structured APIs like SportDevs deliver odds, but the thinking here applies no matter which provider you’re working with.
Let’s kick off with a quick look at the key markets you should care about.
Step 1: Understand the Odds Markets
Before you start pulling data, you need to know what you’re actually asking for.
Every odds market serves a different purpose, some are built for casual bettors looking to back their favorite team, others are built for sharps sniffing out inefficiencies. As a developer, the better you understand these markets, the better you can build features that fit how people actually bet.
Here are five core markets you’ll encounter (and likely need to support):
-
Correct Score This one’s exactly what it sounds like, you’re betting on the exact final score (e.g., 2-1, 0-0, etc.).
- Use case: High-risk, high-reward. These odds tend to be long, so it’s a favorite among users who like to place low-stake, high-upside bets.
- Dev note: Useful for personalization modules (“Trending Correct Score Bets”) or niche promotions.
-
Next Goal Which team scores next? Simple concept, fast decisions.
- Use case: Perfect for in-play betting, odds shift rapidly based on momentum and possession.
- Dev note: Combine with live stats to give users confidence (e.g., Team A has had 8 shots in the last 10 mins).
-
Asian Handicaps This one trips up beginners, but pros love it. Instead of just win/lose, teams get a virtual advantage or disadvantage.
- Use case: Levels the playing field when there’s a clear favorite, bettors get more nuanced ways to play.
- Dev note: Make sure you handle 0.5, 1.0, and 1.5 lines correctly. You’ll also need to explain this clearly in your UI if you want casual users to engage.
-
Total Goals (Over/Under) This is about how many goals both teams score combined, not who wins.
- Use case: Very popular in predictions and model-driven betting. “Over 2.5” is basically a standard.
- Dev note: You can generate automated recommendations here by pulling historical match data and calculating averages.
-
First Team to Score Quick, binary, and easy to understand. Just bet on who gets the first goal.
- Use case: Great for beginner users. Works well as a featured market on mobile.
- Dev note: Often paired with UI nudges like “Team A’s scored first in 7 of their last 10 games.”
Understanding these markets helps you do more than just display data, it helps you surface the right data at the right time, depending on the user’s intent and experience level.
Step 2: Access the Odds Data
Once you understand the markets you want to use, the next step is pulling the odds data correctly. SportDevs provides a unified odds endpoint that allows you to query by event or fixture, with support for multiple markets and bookmakers.
Let’s walk through the correct structure using the following examples. ✅ Example: Get Odds by Fixture
GET https://api.sportdevs.com/v1/odds360/football/fixture/{fixture_id}
Replace {fixture_id}
with the ID of the match you want to query. This gives you all available markets, outcomes, and odds for that fixture.
🔑 Headers:
Authorization: Bearer YOUR_API_KEY
Make sure to include your API key in the Authorization header using the Bearer format.
📥 Sample Response (Simplified):
{
"fixture_id": 5415093,
"markets": [
{
"market_name": "1X2",
"bookmaker": "bet365",
"last_updated": "2024-12-04T19:26:01Z",
"outcomes": [
{ "label": "Home", "odds": 1.83 },
{ "label": "Draw", "odds": 3.60 },
{ "label": "Away", "odds": 4.20 }
]
},
...
]
}
You’ll get:
- All the available markets for a fixture (e.g. 1X2, Over/Under, Handicaps)
- Outcomes with labels and odds
- Bookmaker identifier
- Last update timestamp (great for syncing and cache validation)
Step 3: Analyze the Data
Pulling odds is easy. Knowing what to do with them is where the edge comes in.
Once you’ve got the data in place, it’s time to turn it into something useful for both your product and your users. Odds aren’t just static numbers; they’re a snapshot of expectations, risk, and momentum. Here’s how to read them:
Identify Trends
Start by analyzing which teams are consistently favored across different markets, and dig into the “why.” Maybe it's a run of clean sheets. Maybe it’s just public hype. Pairing odds data with match history, form, and contextual stats helps you surface patterns that can power smart recommendations, filters, or betting insights.
Compare Odds
If you’re pulling from multiple bookmakers, comparing them isn’t just about variety, it’s about value. Small differences in odds can mean significant changes in potential payout. Highlighting the best available line gives users a reason to engage, while also supporting features like value-bet detection or smart alerts.
Understand Market Movements
Odds aren’t static, they move as betting volumes shift, injuries are announced, or lineups drop. Tracking those changes lets you detect sentiment in real time. When odds shorten quickly, that’s usually a signal. When they drift, that’s another. Understanding these movements gives your platform a sharper edge, especially for in-play or time-sensitive betting scenarios.
This kind of analysis doesn’t just add depth, it gives users reasons to trust the data, explore further, and come back.
Step 4: Incorporate Calculations
Odds alone aren’t enough, users want to know what those odds mean for them. That’s where simple calculations make a big difference. Once you’ve fetched the raw data, start layering in logic to help users understand potential outcomes. This isn’t just about payouts, it’s about clarity, transparency, and improving decision-making.
Calculate Potential Payouts
At a basic level, users want to know: “If I bet X, what do I get back?” Integrating a real-time payout calculator in your interface (based on current odds and stake) turns passive data into an actionable tool.
Support Multiple Odds Formats
Depending on your audience, decimal, fractional, and American odds might all come into play. Allowing users to toggle between formats improves usability, and in regulated markets, it’s often a requirement.
Integrate with UX Flows
Odds-based calculations shouldn’t live in isolation. Use them inside bet slips, promotional modules (“Bet $50 to win $120”), or dynamic widgets that respond to market changes. This makes betting more intuitive and helps users make faster, smarter decisions.
Feed Predictive Models
If you’re building any sort of risk engine, arbitrage detection, or smart betting assistant, these odds and payout calculations form the foundation. You can go deeper, factoring in implied probability, margins, and expected value, but it all starts here.
Step 5: Design User-Friendly Presentations
You can have the best data and the sharpest models, but if your UI confuses users, none of it matters. Odds data is dense by nature. Your job is to make it digestible. That doesn’t mean oversimplifying, it means presenting information in a way that supports real decisions.
Visualize the Right Things
Odds movements over time? Use line charts. Bookmaker spreads for the same market? Horizontal bar charts work well. Team trends or form-based metrics? Tables, heatmaps, or compact widgets. Don’t throw data at the user, guide them to what matters. Highlight changes, show best available odds, and use micro-interactions (like hover explanations) to make the experience feel alive.
Choose the Right Tools
Whether you’re building a web dashboard or embedding betting features into an app, solid visualization libraries go a long way:
- Chart.js – lightweight and fast to integrate
- D3.js – flexible and powerful for custom visualizations
- Recharts or Plotly – good for interactive elements or dashboards
Tailor the tooling to your product’s stack and performance needs.
Make Calculators and Widgets Intuitive
Users don’t need a spreadsheet. They want clear input → output. If someone enters a bet amount, show the payout instantly. If odds change, reflect that dynamically in the UI. Even simple cues like green/red highlights for rising/falling odds can help users feel more confident.
Refresh Odds Regularly
Odds shift constantly. Make sure you’re fetching updated data at appropriate intervals, especially for in-play or fast-moving markets. Most platforms refresh odds every 30–60 seconds, depending on load and market type. Use timestamps like last_updated to avoid unnecessary requests.
Build Alerts for Anomalies
When odds change significantly in a short window, something’s happening. Flag these internally, or better yet, surface them in your UI. Odds dropping from 2.8 to 1.9 in 10 minutes? That’s not random. Use it. You can also use anomaly detection to monitor your own system, API failures, stale data, or gaps in bookmaker coverage.
Smart betting products evolve, based on how people actually interact with the data. Odds feeds aren’t static, and your UX shouldn’t be either.
Start building smarter betting features with SportDevs’ Odds360, structured, real-time data built for developers who want more than just numbers.