pip install requests<\/code><\/pre>\nStep 1: Authenticate & Discover Sports<\/h2>\n OddsPapi uses a query parameter for authentication — no headers, no OAuth, no token refresh.<\/p>\n
import requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE = \"https:\/\/api.oddspapi.io\/v4\"\n\n# Check which sports have Polymarket coverage\nresp = requests.get(f\"{BASE}\/sports\", params={\"apiKey\": API_KEY})\nsports = resp.json()\nfor s in sports:\n print(f\"{s['sportId']:3d} | {s['sportName']}\")<\/code><\/pre>\nPolymarket currently covers MLB (sportId 13)<\/strong> and NBA (sportId 11)<\/strong> moneylines through OddsPapi, with the same fixtures available from 80-120+ traditional sportsbooks.<\/p>\nStep 2: Find Fixtures with Polymarket Odds<\/h2>\n Not every fixture has Polymarket coverage. We need to fetch fixtures, then check which ones have the polymarket<\/code> slug in the odds response.<\/p>\nfrom datetime import datetime, timedelta\n\ndef get_polymarket_fixtures(sport_id, days_ahead=7):\n \"\"\"Find upcoming fixtures that have Polymarket odds.\"\"\"\n now = datetime.now()\n params = {\n \"apiKey\": API_KEY,\n \"sportId\": sport_id,\n \"from\": now.strftime(\"%Y-%m-%dT00:00:00Z\"),\n \"to\": (now + timedelta(days=days_ahead)).strftime(\"%Y-%m-%dT23:59:59Z\"),\n \"limit\": 100\n }\n resp = requests.get(f\"{BASE}\/fixtures\", params=params)\n fixtures = resp.json()\n\n if isinstance(fixtures, dict) and \"error\" in fixtures:\n print(f\"Error: {fixtures['error']}\")\n return []\n\n pm_fixtures = []\n for f in fixtures:\n fid = f[\"fixtureId\"]\n odds_resp = requests.get(\n f\"{BASE}\/odds\",\n params={\"apiKey\": API_KEY, \"fixtureId\": fid}\n )\n odds = odds_resp.json()\n bo = odds.get(\"bookmakerOdds\", {})\n\n if \"polymarket\" in bo:\n f[\"bookmakerOdds\"] = bo\n f[\"num_books\"] = len(bo)\n pm_fixtures.append(f)\n\n return pm_fixtures\n\n# Scan MLB\nfixtures = get_polymarket_fixtures(sport_id=13)\nprint(f\"Found {len(fixtures)} MLB fixtures with Polymarket odds\")\nfor f in fixtures:\n print(f\" {f['participant1Name']} vs {f['participant2Name']} \"\n f\"({f['num_books']} bookmakers)\")<\/code><\/pre>\nStep 3: Compare Polymarket vs Sportsbook Prices<\/h2>\n Now extract prices from both sides. OddsPapi normalizes everything to decimal odds, so Polymarket share prices and sportsbook odds are directly comparable.<\/p>\n
def extract_moneyline(bookmaker_odds, market_id=\"131\"):\n \"\"\"Extract moneyline prices for all bookmakers on a fixture.\n\n Market IDs: 131 = MLB Moneyline, 111 = NBA Moneyline\n Outcome IDs: 131\/111 = Home, 132\/112 = Away\n \"\"\"\n prices = {}\n outcome_ids = (\n [\"131\", \"132\"] if market_id == \"131\"\n else [\"111\", \"112\"]\n )\n\n for slug, book_data in bookmaker_odds.items():\n market = book_data.get(\"markets\", {}).get(market_id, {})\n outcomes = market.get(\"outcomes\", {})\n\n home = (outcomes.get(outcome_ids[0], {})\n .get(\"players\", {}).get(\"0\", {}).get(\"price\", 0))\n away = (outcomes.get(outcome_ids[1], {})\n .get(\"players\", {}).get(\"0\", {}).get(\"price\", 0))\n\n if home > 1 and away > 1:\n prices[slug] = {\"home\": home, \"away\": away}\n\n return prices\n\n# Example: first fixture\nfixture = fixtures[0]\nbo = fixture[\"bookmakerOdds\"]\nprices = extract_moneyline(bo, market_id=\"131\")\n\n# Show Polymarket vs top sportsbooks\nprint(f\"\\n{fixture['participant1Name']} vs {fixture['participant2Name']}\")\nprint(f\"{'Bookmaker':20s} | {'Home':>8s} | {'Away':>8s} | {'Implied':>8s}\")\nprint(\"-\" * 55)\nfor slug in [\"polymarket\", \"pinnacle\", \"bet365\",\n \"draftkings\", \"fanduel\", \"betmgm\"]:\n if slug in prices:\n p = prices[slug]\n implied = round((1\/p[\"home\"] + 1\/p[\"away\"]) * 100, 1)\n print(f\"{slug:20s} | {p['home']:8.3f} | \"\n f\"{p['away']:8.3f} | {implied:7.1f}%\")<\/code><\/pre>\nYou’ll see something like this (real data from the OddsPapi API):<\/p>\n\n\n\n\nBookmaker<\/th>\n Home<\/th>\n Away<\/th>\n Overround<\/th>\n<\/tr>\n<\/thead>\n \n\nPolymarket<\/td>\n 2.174<\/td>\n 1.852<\/td>\n 100.0%<\/td>\n<\/tr>\n \nPinnacle<\/td>\n 1.952<\/td>\n 1.900<\/td>\n 103.9%<\/td>\n<\/tr>\n \nBet365<\/td>\n 1.900<\/td>\n 1.900<\/td>\n 105.3%<\/td>\n<\/tr>\n \nDraftKings<\/td>\n 2.200<\/td>\n 1.700<\/td>\n 104.3%<\/td>\n<\/tr>\n \nBetMGM<\/td>\n 1.910<\/td>\n 1.910<\/td>\n 104.7%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\nNotice: Polymarket has 0% overround<\/strong> (it’s an exchange), while sportsbooks bake in 3-5% vig. This structural difference is what creates cross-market arbs.<\/p>\nStep 4: Detect Arbitrage Opportunities<\/h2>\n An arb exists when the combined implied probability of backing one side on Polymarket and the opposite side on a sportsbook is less than 100%.<\/p>\n
def detect_polymarket_arbs(prices, match_name=\"\"):\n \"\"\"Find arbs between Polymarket and any other bookmaker.\"\"\"\n if \"polymarket\" not in prices:\n return []\n\n pm = prices[\"polymarket\"]\n arbs = []\n\n for slug, book_prices in prices.items():\n if slug == \"polymarket\":\n continue\n\n # Direction 1: Back Home on PM, Back Away on sportsbook\n total_1 = (1 \/ pm[\"home\"]) + (1 \/ book_prices[\"away\"])\n if total_1 < 1.0:\n margin = round((1 - total_1) * 100, 2)\n arbs.append({\n \"match\": match_name,\n \"pm_side\": \"Home\", \"pm_price\": pm[\"home\"],\n \"book\": slug,\n \"book_side\": \"Away\",\n \"book_price\": book_prices[\"away\"],\n \"margin\": margin\n })\n\n # Direction 2: Back Away on PM, Back Home on sportsbook\n total_2 = (1 \/ pm[\"away\"]) + (1 \/ book_prices[\"home\"])\n if total_2 < 1.0:\n margin = round((1 - total_2) * 100, 2)\n arbs.append({\n \"match\": match_name,\n \"pm_side\": \"Away\", \"pm_price\": pm[\"away\"],\n \"book\": slug,\n \"book_side\": \"Home\",\n \"book_price\": book_prices[\"home\"],\n \"margin\": margin\n })\n\n arbs.sort(key=lambda x: x[\"margin\"], reverse=True)\n return arbs\n\n# Run on our fixture\nmatch = (f\"{fixture['participant1Name']} vs \"\n f\"{fixture['participant2Name']}\")\narbs = detect_polymarket_arbs(prices, match)\n\nprint(f\"\\nArbs found: {len(arbs)}\")\nfor a in arbs[:10]:\n print(f\" +{a['margin']}% | PM({a['pm_side']}) \"\n f\"@ {a['pm_price']:.3f} vs \"\n f\"{a['book']}({a['book_side']}) \"\n f\"@ {a['book_price']:.3f}\")<\/code><\/pre>\nOn the Phillies vs Tigers fixture above, this scanner found 51 arb opportunities<\/strong> across different bookmakers, with margins up to +3.24%<\/strong>. The highest-margin arbs were against regional European and Brazilian books that were slow to adjust their lines.<\/p>\nStep 5: Build the Full Scanner<\/h2>\n Now let's wrap everything into a scanner that checks multiple sports and fixtures automatically.<\/p>\n
def scan_polymarket_arbs(sport_configs, days_ahead=3):\n \"\"\"Scan multiple sports for Polymarket vs sportsbook arbs.\n\n sport_configs: list of (sport_id, market_id, sport_name)\n \"\"\"\n all_arbs = []\n\n for sport_id, market_id, sport_name in sport_configs:\n print(f\"\\nScanning {sport_name}...\")\n fixtures = get_polymarket_fixtures(sport_id, days_ahead)\n print(f\" {len(fixtures)} fixtures with Polymarket odds\")\n\n for f in fixtures:\n match = (f\"{f['participant1Name']} vs \"\n f\"{f['participant2Name']}\")\n prices = extract_moneyline(\n f[\"bookmakerOdds\"], market_id\n )\n arbs = detect_polymarket_arbs(prices, match)\n\n if arbs:\n best = arbs[0]\n print(f\" {match}: {len(arbs)} arbs \"\n f\"(best: +{best['margin']}% \"\n f\"via {best['book']})\")\n all_arbs.extend(arbs)\n\n all_arbs.sort(key=lambda x: x[\"margin\"], reverse=True)\n return all_arbs\n\n# Configure sports to scan\nSPORTS = [\n (13, \"131\", \"MLB\"), # Baseball moneyline\n (11, \"111\", \"Basketball\"), # Basketball moneyline\n]\n\n# Run the scanner\narbs = scan_polymarket_arbs(SPORTS, days_ahead=5)\n\n# Summary\nprint(f\"\\n{'='*60}\")\nprint(f\"TOTAL ARBS FOUND: {len(arbs)}\")\nprint(f\"{'='*60}\")\n\nif arbs:\n print(f\"\\nTop opportunities:\")\n for a in arbs[:10]:\n print(f\" +{a['margin']:5.2f}% | {a['match']}\")\n print(f\" PM({a['pm_side']}) @ {a['pm_price']:.3f}\")\n print(f\" {a['book']}({a['book_side']}) \"\n f\"@ {a['book_price']:.3f}\\n\")<\/code><\/pre>\nStep 6: Calculate Optimal Stakes<\/h2>\n Once you find an arb, you need to split your bankroll correctly to guarantee the same profit regardless of outcome.<\/p>\n
def calculate_stakes(pm_price, book_price, total_stake=100):\n \"\"\"Calculate optimal stake split for a 2-way arb.\"\"\"\n implied_total = (1 \/ pm_price) + (1 \/ book_price)\n if implied_total >= 1.0:\n return None # Not an arb\n\n # Stake proportional to implied probability\n stake_pm = (total_stake * (1 \/ pm_price)\n \/ implied_total)\n stake_book = (total_stake * (1 \/ book_price)\n \/ implied_total)\n\n # Profit is the same regardless of outcome\n profit = (stake_pm * pm_price) - total_stake\n\n return {\n \"stake_polymarket\": round(stake_pm, 2),\n \"stake_sportsbook\": round(stake_book, 2),\n \"total_stake\": total_stake,\n \"guaranteed_profit\": round(profit, 2),\n \"roi_pct\": round((profit \/ total_stake) * 100, 2)\n }\n\n# Example: PM Home @ 2.174 vs Unibet Away @ 1.970\nresult = calculate_stakes(2.174, 1.970, total_stake=1000)\nif result:\n print(f\"Stake on Polymarket: ${result['stake_polymarket']}\")\n print(f\"Stake on Sportsbook: ${result['stake_sportsbook']}\")\n print(f\"Total invested: ${result['total_stake']}\")\n print(f\"Guaranteed profit: ${result['guaranteed_profit']}\")\n print(f\"ROI: {result['roi_pct']}%\")<\/code><\/pre>\nFor a $1,000 total stake on the 3.24% arb above, you'd put ~$466 on Polymarket (Home @ 2.174) and ~$534 on Unibet (Away @ 1.970), locking in ~$32 guaranteed profit regardless of outcome.<\/p>\n
Step 7: Share Finds with Your Community<\/h2>\n The real power of this scanner is running it on a schedule and pushing alerts to a Telegram group or Discord channel. Here's a minimal alert formatter:<\/p>\n
def format_alert(arb, total_stake=1000):\n \"\"\"Format an arb opportunity as a shareable alert.\"\"\"\n stakes = calculate_stakes(\n arb[\"pm_price\"], arb[\"book_price\"], total_stake\n )\n if not stakes:\n return None\n\n return (\n f\"ARB FOUND: {arb['match']}\\n\"\n f\"Polymarket ({arb['pm_side']}) \"\n f\"@ {arb['pm_price']:.3f}\\n\"\n f\"{arb['book']} ({arb['book_side']}) \"\n f\"@ {arb['book_price']:.3f}\\n\"\n f\"Margin: +{arb['margin']}% | \"\n f\"ROI: +${stakes['guaranteed_profit']} \"\n f\"on ${total_stake}\\n\"\n f\"Stakes: PM ${stakes['stake_polymarket']} \"\n f\"\/ Book ${stakes['stake_sportsbook']}\"\n )\n\n# Example output\nif arbs:\n alert = format_alert(arbs[0])\n print(alert)<\/code><\/pre>\nRun the scanner on a cron job, pipe alerts to your group chat, and you've got yourself a cross-market arb community. This is how Polymarket Telegram alpha groups operate — someone runs a scanner, everyone benefits from the edge.<\/p>\n
Backtest Before Going Live<\/h2>\n Before risking real money, use OddsPapi's free historical data<\/strong> to backtest which markets diverge most. Historical odds are included on the free tier — no upgrade needed.<\/p>\n# Fetch historical odds for a completed fixture\nhistorical_resp = requests.get(\n f\"{BASE}\/odds\/history\",\n params={\n \"apiKey\": API_KEY,\n \"fixtureId\": \"COMPLETED_FIXTURE_ID\"\n }\n)\nhistorical = historical_resp.json()\n# Compare Polymarket prices vs closing lines<\/code><\/pre>\nKey things to backtest:<\/p>\n
\nWhich sports produce the most Polymarket arbs (MLB vs NBA vs specials)<\/li>\n What time of day arbs appear most frequently (line movements after news)<\/li>\n Which regional sportsbooks are slowest to adjust (your best counterparties)<\/li>\n How long arbs stay open before closing (execution window)<\/li>\n<\/ul>\nHow This Compares to Sportsbook-Only Arbs<\/h2>\n\n\n\n\nFactor<\/th>\n Polymarket Arbs<\/th>\n Sportsbook-Only Arbs<\/th>\n<\/tr>\n<\/thead>\n \n\nFrequency<\/td>\n Less frequent (fewer markets)<\/td>\n More frequent (thousands of markets)<\/td>\n<\/tr>\n \nMargin size<\/td>\n Larger (1-5% typical)<\/td>\n Smaller (0.5-2% typical)<\/td>\n<\/tr>\n \nExecution risk<\/td>\n Higher (blockchain settlement)<\/td>\n Lower (instant sportsbook bets)<\/td>\n<\/tr>\n \nAccount limits<\/td>\n None on Polymarket<\/td>\n Sportsbooks limit winners<\/td>\n<\/tr>\n \nCoverage via OddsPapi<\/td>\n 350+ books + Polymarket<\/td>\n 350+ books<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\nThe biggest advantage of Polymarket arbs: you can't get limited on Polymarket<\/strong>. Unlike sportsbooks that restrict sharp bettors, Polymarket is a decentralized exchange. Your account won't get flagged for winning too much.<\/p>\nFAQ<\/h2>\nCan you arbitrage Polymarket?<\/h3>\n Yes. Polymarket prices are set by an order book (crowd sentiment), while sportsbooks use professional odds compilers. These different pricing models create structural divergence that produces arbitrage opportunities, especially around news events when Polymarket is slower to react.<\/p>\n
Is Polymarket arbitrage legal?<\/h3>\n Arbitrage betting is legal in most jurisdictions. However, Polymarket has specific geographic restrictions (US residents face limitations under current CFTC regulations). Always check your local laws. This is not financial or legal advice.<\/p>\n
How often do Polymarket arbs appear?<\/h3>\n Polymarket arbs on major sports (MLB, NBA) appear multiple times daily, with margins typically between 1-5%. Niche markets with thinner liquidity diverge more frequently but have lower limits.<\/p>\n
Do I need a Polymarket wallet to use this scanner?<\/h3>\n No. The scanner uses OddsPapi to read Polymarket prices — no wallet needed. You only need a Polymarket wallet (with USDC) if you want to execute<\/em> trades on the Polymarket side.<\/p>\nWhich bookmakers produce the most arbs against Polymarket?<\/h3>\n Regional books (European, Brazilian, Asian) that are slower to adjust their lines produce the highest margins. In our scan, books like Unibet, Paf, and Stake.bet.br showed 3%+ margins against Polymarket on MLB moneylines.<\/p>\n