Best Sports Betting Odds APIs for Developers (2026)

Best Sports Betting Odds APIs for Developers (2026)

Building a sports betting application, odds comparison tool, or analytics platform requires reliable access to real-time odds data. The right API can make or break your project -- you need accurate data, broad sportsbook coverage, reasonable pricing, and good developer experience.

This guide compares the best sports betting odds APIs available in 2026, with code examples and practical guidance for choosing the right one.

Top Sports Betting Odds APIs Compared

API	Free Tier	Starting Price	Sports Covered	Sportsbooks	Best For
The Odds API	500 requests/mo	$20/mo	35+	70+	Indie developers
Sportradar	Trial available	Custom pricing	50+	100+	Enterprise apps
BetOdds API	100 requests/day	$29/mo	20+	50+	Comparison tools
OddsJam API	No free tier	$99/mo	25+	40+	Arbitrage/value betting
API-Football	100 requests/day	$19/mo	Football/soccer	30+	Soccer-focused apps
RapidAPI Sports	Varies	Varies	Varies	Varies	Aggregated access

1. The Odds API

The Odds API is the most popular choice for individual developers and small teams. It provides clean, well-documented endpoints with generous free tier access.

Key Features

• 70+ sportsbooks across US, UK, EU, and Australia
• 35+ sports including NFL, NBA, MLB, NHL, soccer, tennis, MMA
• Real-time odds, pre-match, and live in-play data
• Historical odds data available
• Simple REST API with JSON responses

Setup

# Sign up at https://the-odds-api.com to get your API key
# Free tier: 500 requests per month

Code Examples

Fetch available sports:

import requests

API_KEY = "your_api_key_here"
BASE_URL = "https://api.the-odds-api.com/v4"

# Get list of available sports
response = requests.get(
    f"{BASE_URL}/sports",
    params={"apiKey": API_KEY}
)

sports = response.json()
for sport in sports:
    print(f"{sport['key']}: {sport['title']} ({'In Season' if sport['active'] else 'Off Season'})")

Get odds for a specific sport:

# Get NFL odds from multiple sportsbooks
response = requests.get(
    f"{BASE_URL}/sports/americanfootball_nfl/odds",
    params={
        "apiKey": API_KEY,
        "regions": "us",              # us, uk, eu, au
        "markets": "h2h,spreads,totals",  # moneyline, spreads, over/under
        "oddsFormat": "american",     # american, decimal, fractional
        "bookmakers": "draftkings,fanduel,betmgm"  # optional filter
    }
)

games = response.json()

for game in games:
    print(f"\n{game['away_team']} @ {game['home_team']}")
    print(f"Start: {game['commence_time']}")

    for bookmaker in game.get("bookmakers", []):
        print(f"\n  {bookmaker['title']}:")
        for market in bookmaker.get("markets", []):
            if market["key"] == "h2h":
                for outcome in market["outcomes"]:
                    print(f"    {outcome['name']}: {outcome['price']}")

Get live in-play odds:

# Fetch live event odds
response = requests.get(
    f"{BASE_URL}/sports/basketball_nba/odds-live",
    params={
        "apiKey": API_KEY,
        "regions": "us",
        "markets": "h2h,spreads,totals"
    }
)

live_games = response.json()
for game in live_games:
    print(f"LIVE: {game['away_team']} @ {game['home_team']}")

JavaScript/TypeScript example:

const API_KEY = "your_api_key_here";
const BASE_URL = "https://api.the-odds-api.com/v4";

interface OddsResponse {
  id: string;
  sport_key: string;
  home_team: string;
  away_team: string;
  commence_time: string;
  bookmakers: Bookmaker[];
}

interface Bookmaker {
  key: string;
  title: string;
  markets: Market[];
}

interface Market {
  key: string;
  outcomes: Outcome[];
}

interface Outcome {
  name: string;
  price: number;
  point?: number;
}

async function getNFLOdds(): Promise<OddsResponse[]> {
  const params = new URLSearchParams({
    apiKey: API_KEY,
    regions: "us",
    markets: "h2h,spreads,totals",
    oddsFormat: "american",
  });

  const response = await fetch(
    `${BASE_URL}/sports/americanfootball_nfl/odds?${params}`
  );

  if (!response.ok) {
    throw new Error(`API error: ${response.status}`);
  }

  // Check remaining requests in headers
  const remaining = response.headers.get("x-requests-remaining");
  console.log(`Requests remaining: ${remaining}`);

  return response.json();
}

Pricing

Plan	Requests/Month	Price	Historical Data
Free	500	$0	No
Starter	10,000	$20/mo	No
Standard	50,000	$75/mo	Yes
Premium	250,000	$200/mo	Yes

2. Sportradar

Sportradar is the enterprise standard for sports data. It powers many major betting platforms, media companies, and sports leagues.

Key Features

• Most comprehensive data coverage (50+ sports)
• Official data partnerships with NFL, NBA, NHL, NASCAR
• Real-time play-by-play data
• Player props and advanced markets
• Streaming data feeds (push updates)

Code Example

import requests

API_KEY = "your_sportradar_key"
BASE_URL = "https://api.sportradar.us"

# Get NFL schedule and odds
response = requests.get(
    f"{BASE_URL}/oddscomparison-usp1/en/categories/sr:category:33/outrights.json",
    params={"api_key": API_KEY}
)

data = response.json()

# Get live odds for a specific match
match_id = "sr:match:12345678"
response = requests.get(
    f"{BASE_URL}/oddscomparison-usp1/en/matches/{match_id}/probabilities.json",
    params={"api_key": API_KEY}
)

match_odds = response.json()

Pricing

Sportradar uses custom enterprise pricing. Contact their sales team for quotes. Expect $500+/month for basic access and significantly more for real-time feeds.

3. Building an Odds Comparison Tool

Here is a practical example of building a simple odds comparison tool using The Odds API:

from dataclasses import dataclass

@dataclass
class OddsComparison:
    game: str
    market: str
    outcome: str
    best_book: str
    best_odds: float
    worst_book: str
    worst_odds: float
    edge: float

def find_best_odds(games: list[dict]) -> list[OddsComparison]:
    comparisons = []

    for game in games:
        game_name = f"{game['away_team']} @ {game['home_team']}"

        # Collect odds from all bookmakers for each outcome
        outcomes: dict[str, list[tuple[str, float]]] = {}

        for bookmaker in game.get("bookmakers", []):
            for market in bookmaker.get("markets", []):
                if market["key"] != "h2h":
                    continue
                for outcome in market["outcomes"]:
                    key = outcome["name"]
                    if key not in outcomes:
                        outcomes[key] = []
                    outcomes[key].append((bookmaker["title"], outcome["price"]))

        # Find best and worst for each outcome
        for outcome_name, odds_list in outcomes.items():
            if len(odds_list) < 2:
                continue

            best = max(odds_list, key=lambda x: x[1])
            worst = min(odds_list, key=lambda x: x[1])

            comparisons.append(OddsComparison(
                game=game_name,
                market="Moneyline",
                outcome=outcome_name,
                best_book=best[0],
                best_odds=best[1],
                worst_book=worst[0],
                worst_odds=worst[1],
                edge=round(best[1] - worst[1], 2)
            ))

    return sorted(comparisons, key=lambda x: x.edge, reverse=True)

# Usage
games = requests.get(f"{BASE_URL}/sports/basketball_nba/odds", params={
    "apiKey": API_KEY, "regions": "us", "markets": "h2h", "oddsFormat": "decimal"
}).json()

comparisons = find_best_odds(games)

print(f"{'Game':<40} {'Outcome':<20} {'Best Book':<15} {'Best':<8} {'Worst':<8} {'Edge':<6}")
print("-" * 100)
for c in comparisons[:10]:
    print(f"{c.game:<40} {c.outcome:<20} {c.best_book:<15} {c.best_odds:<8} {c.worst_odds:<8} {c.edge:<6}")

4. Detecting Arbitrage Opportunities

Arbitrage betting involves betting on all outcomes of an event across different sportsbooks to guarantee a profit when odds are misaligned.

def find_arbitrage(games: list[dict]) -> list[dict]:
    opportunities = []

    for game in games:
        game_name = f"{game['away_team']} @ {game['home_team']}"

        # Get best odds for each outcome across all bookmakers
        best_odds: dict[str, tuple[str, float]] = {}

        for bookmaker in game.get("bookmakers", []):
            for market in bookmaker.get("markets", []):
                if market["key"] != "h2h":
                    continue
                for outcome in market["outcomes"]:
                    name = outcome["name"]
                    decimal_odds = outcome["price"]

                    if name not in best_odds or decimal_odds > best_odds[name][1]:
                        best_odds[name] = (bookmaker["title"], decimal_odds)

        if len(best_odds) < 2:
            continue

        # Calculate arbitrage percentage
        # If sum of (1/odds) < 1.0, arbitrage exists
        arb_sum = sum(1 / odds for _, odds in best_odds.values())

        if arb_sum < 1.0:
            profit_pct = round((1 - arb_sum) * 100, 2)
            opportunities.append({
                "game": game_name,
                "profit_pct": profit_pct,
                "bets": {
                    name: {"book": book, "odds": odds, "stake_pct": round((1/odds) / arb_sum * 100, 2)}
                    for name, (book, odds) in best_odds.items()
                }
            })

    return sorted(opportunities, key=lambda x: x["profit_pct"], reverse=True)

# Usage
arb_opps = find_arbitrage(games)

for opp in arb_opps:
    print(f"\nARBITRAGE: {opp['game']} ({opp['profit_pct']}% profit)")
    for outcome, details in opp["bets"].items():
        print(f"  Bet {outcome} on {details['book']} @ {details['odds']} ({details['stake_pct']}% of bankroll)")

5. Expected Value (EV) Calculator

Calculate whether a bet has positive expected value:

def american_to_decimal(american: int) -> float:
    """Convert American odds to decimal odds."""
    if american > 0:
        return (american / 100) + 1
    else:
        return (100 / abs(american)) + 1

def implied_probability(decimal_odds: float) -> float:
    """Convert decimal odds to implied probability."""
    return 1 / decimal_odds

def expected_value(
    your_odds: int,
    fair_probability: float,
    stake: float = 100
) -> dict:
    """
    Calculate EV of a bet.

    Args:
        your_odds: American odds you're getting
        fair_probability: Your estimated true probability (0-1)
        stake: Bet amount
    """
    decimal = american_to_decimal(your_odds)
    profit = stake * (decimal - 1)
    loss = stake

    ev = (fair_probability * profit) - ((1 - fair_probability) * loss)
    ev_pct = ev / stake * 100

    return {
        "odds": your_odds,
        "decimal_odds": round(decimal, 3),
        "implied_prob": round(implied_probability(decimal) * 100, 1),
        "fair_prob": round(fair_probability * 100, 1),
        "expected_value": round(ev, 2),
        "ev_percentage": round(ev_pct, 2),
        "positive_ev": ev > 0
    }

# Example: You think Team A has a 55% chance of winning
# Sportsbook offers +110 (American odds)
result = expected_value(your_odds=110, fair_probability=0.55)

print(f"Odds: {result['odds']} (Decimal: {result['decimal_odds']})")
print(f"Implied probability: {result['implied_prob']}%")
print(f"Your estimated probability: {result['fair_prob']}%")
print(f"Expected value per $100: ${result['expected_value']}")
print(f"EV%: {result['ev_percentage']}%")
print(f"Positive EV bet: {'Yes' if result['positive_ev'] else 'No'}")

6. Building a Real-Time Odds Dashboard

Here is a Next.js API route that serves odds data to a frontend dashboard:

// app/api/odds/route.ts
import { NextResponse } from "next/server";

const API_KEY = process.env.ODDS_API_KEY!;
const BASE_URL = "https://api.the-odds-api.com/v4";

// Cache odds for 60 seconds to respect rate limits
let cache: { data: any; timestamp: number } | null = null;
const CACHE_TTL = 60 * 1000;

export async function GET(request: Request) {
  const { searchParams } = new URL(request.url);
  const sport = searchParams.get("sport") || "americanfootball_nfl";

  if (cache && Date.now() - cache.timestamp < CACHE_TTL) {
    return NextResponse.json(cache.data);
  }

  const params = new URLSearchParams({
    apiKey: API_KEY,
    regions: "us",
    markets: "h2h,spreads,totals",
    oddsFormat: "american",
  });

  const response = await fetch(`${BASE_URL}/sports/${sport}/odds?${params}`);

  if (!response.ok) {
    return NextResponse.json(
      { error: "Failed to fetch odds" },
      { status: response.status }
    );
  }

  const data = await response.json();

  cache = { data, timestamp: Date.now() };

  return NextResponse.json(data, {
    headers: {
      "Cache-Control": "public, s-maxage=60, stale-while-revalidate=30",
    },
  });
}

API Selection Guide

Choose your API based on these factors:

Factor	The Odds API	Sportradar	BetOdds	OddsJam
Free tier	500 req/mo	Trial only	100 req/day	None
Setup time	5 minutes	Days (sales)	10 minutes	15 minutes
Documentation	Excellent	Good	Good	Good
Live odds	Yes	Yes	Yes	Yes
Historical data	Paid plans	Yes	Limited	Yes
Player props	Limited	Extensive	No	Yes
Best for	Side projects	Enterprise	Comparison	Arb hunting

Decision Flowchart

Starting a side project or MVP?
  -> The Odds API (free tier, quick setup)

Building a production betting platform?
  -> Sportradar (enterprise data, official partnerships)

Building an odds comparison or arbitrage tool?
  -> OddsJam or BetOdds (specialized features)

Need only soccer/football data?
  -> API-Football (cheaper, soccer-specialized)

Rate Limiting Best Practices

Regardless of which API you choose, implement proper rate limiting:

import time
from functools import wraps

class RateLimiter:
    def __init__(self, max_requests: int, window_seconds: int):
        self.max_requests = max_requests
        self.window = window_seconds
        self.requests: list[float] = []

    def wait_if_needed(self):
        now = time.time()
        self.requests = [r for r in self.requests if now - r < self.window]

        if len(self.requests) >= self.max_requests:
            sleep_time = self.window - (now - self.requests[0])
            print(f"Rate limit reached. Sleeping {sleep_time:.1f}s...")
            time.sleep(sleep_time)

        self.requests.append(time.time())

# Usage: 10 requests per minute
limiter = RateLimiter(max_requests=10, window_seconds=60)

def fetch_odds(sport: str):
    limiter.wait_if_needed()
    return requests.get(f"{BASE_URL}/sports/{sport}/odds", params={
        "apiKey": API_KEY, "regions": "us", "markets": "h2h"
    }).json()

Conclusion

For most developers starting out, The Odds API is the best choice -- it has a generous free tier, excellent documentation, and covers all major sports. As your application scales, Sportradar provides the most comprehensive data for enterprise-grade platforms.

Whichever API you choose, implement proper caching and rate limiting from day one. Odds data changes frequently, but you rarely need sub-second updates for most consumer applications.

If your sports platform needs AI-generated visual content -- like automated video highlights, player avatar images, or dynamic graphics -- Hypereal AI provides APIs for AI image and video generation that can complement your odds data with engaging visual content.