Brazil just became the world’s hottest regulated sports betting market. On January 1, 2025, federal regulation kicked in, turning a gray market into a $10+ billion opportunity. Analysts project Brazil will hit $10B in Gross Gaming Revenue by 2029\u2014putting it on track to rival the US market.<\/p>\n
The local players are massive: Betano<\/strong> holds roughly 23% market share. EstrelaBet<\/strong>, Blaze<\/strong>, Stake BR<\/strong>, and Pixbet<\/strong> are all fighting for the rest. These aren’t small-time operators\u2014they’re billion-dollar businesses with odds data that arbitrageurs and quants desperately want.<\/p>\n The problem?<\/strong> None of them offer a public API.<\/p>\n If you’re a developer trying to pull odds from EstrelaBet or Betano, your options are grim: scrape their sites (and risk getting banned), pay enterprise rates ($500+\/month) for partial coverage, or give up entirely.<\/p>\n There’s a third option.<\/strong> OddsPapi aggregates odds from 300+ bookmakers\u2014including all major Brazilian operators\u2014into a single API. Free tier included. Historical data for backtesting. No scraping required.<\/p>\n These are the Brazilian-licensed operators you can access with a single API call:<\/p>\n Let’s build a Python script that compares odds across EstrelaBet, Stake BR, and Sportingbet to identify arbitrage opportunities on Brazilian football fixtures.<\/p>\n OddsPapi uses query parameter authentication\u2014not headers. This is important.<\/p>\n First, pull upcoming soccer fixtures and filter for Brazil. Note: date range must be under 10 days.<\/p>\n The odds response is nested JSON. Here’s how to parse it correctly:<\/p>\n An arbitrage exists when the sum of implied probabilities across the best odds is less than 100%.<\/p>\n Here's the full working script that scans Brazilian fixtures for arbitrage opportunities:<\/p>\n Arbitrage is rare. Value betting is more realistic. The idea: compare soft book odds (EstrelaBet, Stake BR) against Pinnacle (the sharp benchmark). When soft odds exceed sharp by a meaningful margin, you've found value.<\/p>\n Three reasons this matters for Brazilian market developers:<\/p>\nOld Way vs. OddsPapi<\/h2>\n
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\n \nOld Way<\/th>\n OddsPapi<\/th>\n<\/tr>\n<\/thead>\n \n Scrape EstrelaBet (risk ban, legal gray area)<\/td>\n One API call with estrelabet<\/code> slug<\/td>\n<\/tr>\n\n No Betano API exists publicly<\/td>\n betano.bet.br<\/code> available in responses<\/td>\n<\/tr>\n\n Pay $500+\/month for enterprise data<\/td>\n Free tier + historical data for backtesting<\/td>\n<\/tr>\n \n Most APIs cover ~40 bookmakers max<\/td>\n 300+ bookmakers including all Brazilian locals<\/td>\n<\/tr>\n \n No sharp book benchmarks<\/td>\n Pinnacle, Singbet included for value detection<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Brazilian Bookmakers Available via OddsPapi<\/h2>\n
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\n \nBookmaker<\/th>\n API Slug<\/th>\n Coverage<\/th>\n<\/tr>\n<\/thead>\n \n EstrelaBet<\/td>\n estrelabet<\/code><\/td>\nFull odds<\/td>\n<\/tr>\n \n Stake BR<\/td>\n stake.bet.br<\/code><\/td>\nFull odds<\/td>\n<\/tr>\n \n Sportingbet BR<\/td>\n sportingbet.bet.br<\/code><\/td>\nFull odds<\/td>\n<\/tr>\n \n Superbet BR<\/td>\n superbet.bet.br<\/code><\/td>\nFull odds<\/td>\n<\/tr>\n \n Blaze<\/td>\n blaze.bet.br<\/code><\/td>\nFull odds<\/td>\n<\/tr>\n \n KTO<\/td>\n kto<\/code><\/td>\nFull odds<\/td>\n<\/tr>\n \n Pixbet<\/td>\n pixbet<\/code><\/td>\nFull odds<\/td>\n<\/tr>\n \n Brazino777<\/td>\n brazino777.bet.br<\/code><\/td>\nFull odds<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Tutorial: Build a Brazilian Bookmaker Arbitrage Scanner<\/h2>\n
Step 1: Authentication<\/h3>\n
import requests\n\nAPI_KEY = \"YOUR_API_KEY\" # Get free key at oddspapi.io\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\n# Always pass apiKey as query parameter\nparams = {\"apiKey\": API_KEY}\nresponse = requests.get(f\"{BASE_URL}\/sports\", params=params)\nprint(response.status_code) # Should be 200<\/code><\/pre>\nStep 2: Get Brazilian Football Fixtures<\/h3>\n
from datetime import datetime, timedelta\n\n# Date range (max 10 days apart)\ntoday = datetime.now().strftime('%Y-%m-%d')\nnext_week = (datetime.now() + timedelta(days=7)).strftime('%Y-%m-%d')\n\n# Fetch soccer fixtures (sportId=10)\nresponse = requests.get(f\"{BASE_URL}\/fixtures\", params={\n \"apiKey\": API_KEY,\n \"sportId\": 10,\n \"from\": today,\n \"to\": next_week\n})\n\nfixtures = response.json()\n\n# Filter for Brazilian fixtures with odds\nbrazil_fixtures = [\n f for f in fixtures\n if \"brazil\" in f.get(\"categorySlug\", \"\").lower()\n and f.get(\"hasOdds\", False)\n]\n\nprint(f\"Found {len(brazil_fixtures)} Brazilian fixtures with odds\")\n\n# Sample output\nfor f in brazil_fixtures[:5]:\n print(f\"{f['participant1Name']} vs {f['participant2Name']}\")<\/code><\/pre>\nStep 3: Fetch Odds from Multiple Brazilian Bookmakers<\/h3>\n
# Get odds for a specific fixture\nfixture_id = brazil_fixtures[0][\"fixtureId\"]\n\nresponse = requests.get(f\"{BASE_URL}\/odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id\n})\n\ndata = response.json()\nbookmaker_odds = data.get(\"bookmakerOdds\", {})\n\n# Target Brazilian bookmakers\ntarget_slugs = [\"estrelabet\", \"stake.bet.br\", \"sportingbet.bet.br\", \"superbet.bet.br\"]\n\n# Market 101 = Full Time Result (1X2)\n# Outcomes: 101 = Home, 102 = Draw, 103 = Away\n\nfor slug in target_slugs:\n if slug not in bookmaker_odds:\n continue\n\n markets = bookmaker_odds[slug].get(\"markets\", {})\n\n if \"101\" not in markets:\n continue\n\n outcomes = markets[\"101\"][\"outcomes\"]\n\n # Extract prices (nested path: outcomes -> outcomeId -> players -> \"0\" -> price)\n home = outcomes.get(\"101\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", \"-\")\n draw = outcomes.get(\"102\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", \"-\")\n away = outcomes.get(\"103\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", \"-\")\n\n print(f\"{slug}: Home {home} | Draw {draw} | Away {away}\")<\/code><\/pre>\nStep 4: Arbitrage Detection Logic<\/h3>\n
def calculate_arb(home_odds, draw_odds, away_odds):\n \"\"\"\n Calculate if there's an arbitrage opportunity.\n\n Returns:\n tuple: (total_implied_probability, profit_percentage)\n - If total_implied < 1.0, there's an arb opportunity\n - profit_percentage shows potential gain\n \"\"\"\n implied_home = 1 \/ home_odds\n implied_draw = 1 \/ draw_odds\n implied_away = 1 \/ away_odds\n\n total_implied = implied_home + implied_draw + implied_away\n\n if total_implied < 1.0:\n profit = (1 - total_implied) * 100\n return total_implied, profit\n\n return total_implied, 0\n\n# Example\ntotal, profit = calculate_arb(2.90, 3.15, 2.85)\nprint(f\"Total implied: {total:.4f}\")\nprint(f\"Profit margin: {profit:.2f}%\")<\/code><\/pre>\nStep 5: Complete Brazilian Arb Scanner<\/h3>\n
import requests\nfrom datetime import datetime, timedelta\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\ndef get_brazil_fixtures():\n \"\"\"Fetch upcoming Brazilian football fixtures.\"\"\"\n today = datetime.now().strftime('%Y-%m-%d')\n next_week = (datetime.now() + timedelta(days=7)).strftime('%Y-%m-%d')\n\n response = requests.get(f\"{BASE_URL}\/fixtures\", params={\n \"apiKey\": API_KEY,\n \"sportId\": 10,\n \"from\": today,\n \"to\": next_week\n })\n\n fixtures = response.json()\n return [f for f in fixtures\n if \"brazil\" in f.get(\"categorySlug\", \"\").lower()\n and f.get(\"hasOdds\", False)]\n\ndef get_best_odds(fixture_id, target_bookies):\n \"\"\"Get best odds across specified bookmakers for 1X2 market.\"\"\"\n response = requests.get(f\"{BASE_URL}\/odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id\n })\n\n if response.status_code != 200:\n return None\n\n bookmaker_odds = response.json().get(\"bookmakerOdds\", {})\n\n best = {\n \"home\": 0, \"draw\": 0, \"away\": 0,\n \"home_bookie\": \"\", \"draw_bookie\": \"\", \"away_bookie\": \"\"\n }\n\n for slug in bookmaker_odds:\n if target_bookies and slug not in target_bookies:\n continue\n\n markets = bookmaker_odds[slug].get(\"markets\", {})\n if \"101\" not in markets:\n continue\n\n outcomes = markets[\"101\"][\"outcomes\"]\n\n home = outcomes.get(\"101\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", 0)\n draw = outcomes.get(\"102\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", 0)\n away = outcomes.get(\"103\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", 0)\n\n if home and home > best[\"home\"]:\n best[\"home\"] = home\n best[\"home_bookie\"] = slug\n if draw and draw > best[\"draw\"]:\n best[\"draw\"] = draw\n best[\"draw_bookie\"] = slug\n if away and away > best[\"away\"]:\n best[\"away\"] = away\n best[\"away_bookie\"] = slug\n\n return best\n\ndef calculate_arb(home, draw, away):\n \"\"\"Calculate arbitrage opportunity.\"\"\"\n total = (1\/home) + (1\/draw) + (1\/away)\n profit = (1 - total) * 100 if total < 1 else 0\n return total, profit\n\n# Brazilian bookmakers to compare\nBRAZILIAN_BOOKIES = [\n \"estrelabet\", \"stake.bet.br\", \"sportingbet.bet.br\",\n \"superbet.bet.br\", \"kto\", \"pinnacle\"\n]\n\n# Run the scanner\nprint(\"=== Brazilian Bookmaker Arbitrage Scanner ===\\n\")\n\nfixtures = get_brazil_fixtures()\nprint(f\"Scanning {len(fixtures)} Brazilian fixtures...\\n\")\n\narb_count = 0\nfor f in fixtures:\n best = get_best_odds(f[\"fixtureId\"], BRAZILIAN_BOOKIES)\n\n if not best or best[\"home\"] == 0:\n continue\n\n total, profit = calculate_arb(best[\"home\"], best[\"draw\"], best[\"away\"])\n\n if profit > 0:\n arb_count += 1\n print(f\"\ud83d\udea8 ARB FOUND: {f['participant1Name']} vs {f['participant2Name']}\")\n print(f\" Home: {best['home']:.2f} @ {best['home_bookie']}\")\n print(f\" Draw: {best['draw']:.2f} @ {best['draw_bookie']}\")\n print(f\" Away: {best['away']:.2f} @ {best['away_bookie']}\")\n print(f\" Profit: {profit:.2f}%\\n\")\n\nprint(f\"Scan complete. Found {arb_count} arbitrage opportunities.\")<\/code><\/pre>\nBonus: Value Bet Detection (Soft vs. Sharp)<\/h2>\n
def find_value_bets(fixture_id, soft_bookies, threshold=2.0):\n \"\"\"\n Find value bets by comparing soft books vs Pinnacle.\n\n Args:\n fixture_id: The fixture to analyze\n soft_bookies: List of soft bookmaker slugs\n threshold: Minimum edge % to flag as value (default 2%)\n\n Returns:\n List of value bet opportunities\n \"\"\"\n response = requests.get(f\"{BASE_URL}\/odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id\n })\n\n if response.status_code != 200:\n return []\n\n bookmaker_odds = response.json().get(\"bookmakerOdds\", {})\n\n # Get Pinnacle (sharp) prices\n if \"pinnacle\" not in bookmaker_odds:\n return []\n\n sharp_markets = bookmaker_odds[\"pinnacle\"].get(\"markets\", {})\n if \"101\" not in sharp_markets:\n return []\n\n sharp = sharp_markets[\"101\"][\"outcomes\"]\n sharp_prices = {\n \"home\": sharp.get(\"101\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", 0),\n \"draw\": sharp.get(\"102\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", 0),\n \"away\": sharp.get(\"103\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", 0)\n }\n\n value_bets = []\n\n for soft_slug in soft_bookies:\n if soft_slug not in bookmaker_odds:\n continue\n\n soft_markets = bookmaker_odds[soft_slug].get(\"markets\", {})\n if \"101\" not in soft_markets:\n continue\n\n soft = soft_markets[\"101\"][\"outcomes\"]\n\n for outcome_id, outcome_name in [(\"101\", \"home\"), (\"102\", \"draw\"), (\"103\", \"away\")]:\n soft_price = soft.get(outcome_id, {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", 0)\n sharp_price = sharp_prices[outcome_name]\n\n if soft_price > 0 and sharp_price > 0:\n edge = ((soft_price \/ sharp_price) - 1) * 100\n\n if edge >= threshold:\n value_bets.append({\n \"outcome\": outcome_name,\n \"soft_bookie\": soft_slug,\n \"soft_price\": soft_price,\n \"sharp_price\": sharp_price,\n \"edge\": edge\n })\n\n return value_bets\n\n# Find value bets\nSOFT_BOOKIES = [\"estrelabet\", \"stake.bet.br\", \"sportingbet.bet.br\", \"superbet.bet.br\"]\n\nprint(\"=== Value Bet Scanner: Soft Books vs Pinnacle ===\\n\")\n\nfor f in fixtures[:20]:\n value_bets = find_value_bets(f[\"fixtureId\"], SOFT_BOOKIES, threshold=3.0)\n\n if value_bets:\n print(f\"{f['participant1Name']} vs {f['participant2Name']}\")\n for vb in value_bets:\n print(f\" {vb['outcome'].upper()}: {vb['soft_bookie']} @ {vb['soft_price']:.2f}\")\n print(f\" vs Pinnacle @ {vb['sharp_price']:.2f} (+{vb['edge']:.1f}% edge)\")\n print()<\/code><\/pre>\nWhy OddsPapi for Brazilian Markets?<\/h2>\n