How to get live sports odds in Python

What you need

Code status: The Python in this tutorial was run against the example responses published in the Odds-API.io docs (checked 3 November 2026), not against a live API key. Live responses can carry extra fields; the code only reads documented ones.

1. Install the packages and store your key

Keep the key in an environment variable, not in your code ^[1].

python -m pip install requests websockets
export ODDS_API_KEY="your-api-key"   # Windows PowerShell: $env:ODDS_API_KEY="your-api-key"

2. List sports and upcoming events

/sports needs no key. /events takes a sport plus optional league, status (e.g. pending, live), date range and limit ^[4].

import os
import requests

BASE = "https://api.odds-api.io/v3"
API_KEY = os.environ["ODDS_API_KEY"]   # never hard-code or commit your key
BOOKMAKERS = "Bet365,Unibet"           # use bookmakers selected on your plan

def get(path, **params):
    """GET an Odds-API.io v3 endpoint and return parsed JSON."""
    params["apiKey"] = API_KEY
    r = requests.get(BASE + path, params=params, timeout=20)
    if r.status_code == 429:
        raise RuntimeError("Rate limit reached - wait before retrying")
    r.raise_for_status()
    return r.json()

sports = requests.get(BASE + "/sports", timeout=20).json()   # no key needed
print([s["slug"] for s in sports][:10])

events = get("/events", sport="football", league="england-premier-league",
             status="pending", limit=10)
for e in events:
    print(e["id"], e["date"], e["home"], "v", e["away"])

3. Fetch odds and compute implied probability

/odds takes one eventId, up to 30 bookmakers and an optional markets filter; prices are decimal strings ^{[4] [1]}. The snippet converts them to implied probabilities, removes the Overround proportionally and finds the best price per outcome — the same maths as the Implied probability & no-vig calculator.

def ml_prices(event_odds):
    """{bookmaker: {"home": 2.1, "draw": 3.4, "away": 3.2}} from one /odds response."""
    out = {}
    for book, markets in event_odds.get("bookmakers", {}).items():
        for m in markets:
            if m["name"] == "ML" and m.get("odds"):
                row = m["odds"][0]
                out[book] = {k: float(row[k]) for k in ("home", "draw", "away") if k in row}
    return out

event_id = events[0]["id"]
prices = ml_prices(get("/odds", eventId=event_id, bookmakers=BOOKMAKERS, markets="ML"))

for book, p in prices.items():
    booksum = sum(1 / o for o in p.values())                 # implied probabilities
    fair = {side: (1 / o) / booksum for side, o in p.items()}  # proportional no-vig
    print(f"{book}: overround {booksum - 1:.2%}",
          {side: f"{q:.1%}" for side, q in fair.items()})

best = {}
for book, p in prices.items():
    for side, o in p.items():
        if o > best.get(side, (0, ""))[0]:
            best[side] = (o, book)
print("Best price per outcome:", best)

On the sample response in the docs (Bet365 2.10 / 3.40 / 3.20) this prints an overround of 8.28%.

4. Batch up to 10 events in one request

/odds/multi returns odds for up to 10 events and counts as one request, which matters with a 5,000 requests/hour budget ^{[4] [3]}.

ids = ",".join(str(e["id"]) for e in events[:10])   # up to 10 events
for ev in get("/odds/multi", eventIds=ids, bookmakers=BOOKMAKERS, markets="ML"):
    print(ev["home"], "v", ev["away"], ml_prices(ev))

5. Stream live prices over WebSocket

For prices that move in play, use the WebSocket feed at wss://api.odds-api.io/v3/ws. The markets filter is required, connections must support permessage-deflate (Python websockets does; websocket-client is refused), and each updated message carries the full market set, so replace what you store rather than merging ^[5]. WebSocket access is a paid add-on ^[3].

import asyncio, json, os
from urllib.parse import urlencode
import websockets   # needs permessage-deflate; "websocket-client" is refused

STATE = {}   # (event id, bookmaker) -> list of markets

async def stream():
    qs = urlencode({"apiKey": os.environ["ODDS_API_KEY"], "markets": "ML",
                    "sport": "football", "status": "live"})
    async with websockets.connect(f"wss://api.odds-api.io/v3/ws?{qs}",
                                  ping_interval=30, ping_timeout=10) as ws:
        async for raw in ws:
            msg = json.loads(raw)
            if msg["type"] in ("created", "updated"):
                STATE[(msg["id"], msg["bookie"])] = msg["markets"]   # replace, never merge
                for m in msg["markets"]:
                    if m["name"] == "ML" and m.get("odds"):
                        print(msg["id"], msg["bookie"], m["odds"][0])
            elif msg["type"] in ("deleted", "no_markets"):
                STATE.pop((msg["id"], msg["bookie"]), None)

asyncio.run(stream())

6. Where to go next

Feed the prices into the Arbitrage calculator or Expected value (+EV) calculator, or let the API do it: see Arbitrage and +EV finder with Odds-API.io. To compare with Polymarket and Kalshi, read Prediction-market prices vs bookmaker odds.

Related glossary terms and tools

• Odds API
• Implied probability
• Overround
• WebSocket feed
• Moneyline
• Odds converter
• Implied probability & no-vig calculator
• No-code odds alerts with n8n or Make
• Odds data in Claude and ChatGPT via MCP
• Arbitrage and +EV finder with Odds-API.io
• Prediction-market prices vs bookmaker odds
• Odds-API vs SportsGameOdds

Frequently asked questions