🔒 How to Build an Australian Sports Odds Comparison App in Python

How to Build an Australian Sports Odds Comparison App in Python

What We're Building A Python script that: Fetches AFL head-to-head odds from multiple Australian bookmakers via one API call Finds the best available price per…

Sichere Programmierung dev.to 07.06.2026, 11:29 Uhr 3 Min. Lesezeit

What We're Building

A Python script that:

Fetches AFL head-to-head odds from multiple Australian bookmakers via one API call

Finds the best available price per team

Flags any arbitrage opportunities (implied prob sum < 1.0)

Setup

Get a free API key at puntersedge.online/developers/getting-started (500 credits/month free, no credit card).

pip install requests

Step 1: Fetch AFL Odds

import requests



API_KEY = "your_api_key_here"

headers = {"X-API-Key": API_KEY}



resp = requests.get(

    "https://puntersedge.online/api/odds",

    params={"sport": "afl", "market": "h2h"},

    headers=headers,

)

events = resp.json()["data"]

print(f"Found {len(events)} AFL events")

The response is a list of events. Each event has a bookmakers array — one entry per supported Australian bookmaker (Sportsbet, TAB, Neds, Ladbrokes, Betfair, Unibet, PointsBet, Betr).

Step 2: Find the Best Price Per Team

def best_prices(event):

    best = {}

    for bk in event["bookmakers"]:

        for market in bk["markets"]:

            if market["key"] != "h2h":

                continue

            for outcome in market["outcomes"]:

                team = outcome["name"]

                price = outcome["price"]

                if team not in best or price > best[team]["price"]:

                    best[team] = {"price": price, "bookmaker": bk["key"]}

    return best



for event in events:

    print(f"\n{event['home_team']} vs {event['away_team']}")

    for team, info in best_prices(event).items():

        print(f"  {team}: ${info['price']:.2f} @ {info['bookmaker']}")

Sample output:

Brisbane Lions vs Geelong Cats

  Brisbane Lions: $2.10 @ sportsbet

  Geelong Cats: $1.85 @ betfair

Step 3: Detect Arbitrage

Arb exists when the sum of implied probabilities (1/odds) across the best prices is less than 1.0. That means you could back both sides and guarantee a profit.

def check_arb(prices):

    implied_sum = sum(1 / info["price"] for info in prices.values())

    if implied_sum < 1.0:

        profit_pct = round((1 - implied_sum) * 100, 2)

        return True, profit_pct

    return False, 0



for event in events:

    prices = best_prices(event)

    is_arb, profit = check_arb(prices)

    if is_arb:

        print(f"ARB FOUND: {event['home_team']} vs {event['away_team']} — {profit}% profit")

Step 4: Load Into pandas for Modelling

import pandas as pd



rows = []

for event in events:

    for bk in event["bookmakers"]:

        for market in bk["markets"]:

            for outcome in market["outcomes"]:

                rows.append({

                    "event": f"{event['home_team']} vs {event['away_team']}",

                    "bookmaker": bk["key"],

                    "team": outcome["name"],

                    "price": outcome["price"],

                    "implied_prob": round(1 / outcome["price"], 4),

                })



df = pd.DataFrame(rows)

print(df.groupby(["event", "team"])["price"].max())

Supported Sports

AFL — H2H, line, totals

NRL — H2H, line, State of Origin

Horse Racing — Win, place, next-to-go

Greyhound & Harness Racing

Cricket — BBL, Test, ODI

Tennis — ATP/WTA including Australian Open

NBA, Soccer, Rugby Union, MMA/UFC

Price Alert in 20 Lines

import time



TARGET_TEAM = "Melbourne Demons"

TARGET_PRICE = 2.50



while True:

    events = requests.get(

        "https://puntersedge.online/api/odds",

        params={"sport": "afl", "market": "h2h"},

        headers=headers,

    ).json()["data"]



    for event in events:

        for bk in event["bookmakers"]:

            for market in bk["markets"]:

                for outcome in market["outcomes"]:

                    if outcome["name"] == TARGET_TEAM and outcome["price"] >= TARGET_PRICE:

                        print(f"ALERT: {TARGET_TEAM} @ ${outcome['price']:.2f} ({bk['key']})")



    time.sleep(60)

Full Code on GitHub

All examples (arb scanner, price alerts, horse racing NTG, pandas feed, JS widget) are on GitHub:

👉 github.com/Propertyscout001/puntersedge-api-examples