{"id":2494,"date":"2026-03-12T20:55:12","date_gmt":"2026-03-12T20:55:12","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=2494"},"modified":"2026-03-12T21:36:18","modified_gmt":"2026-03-12T21:36:18","slug":"market-making-polymarket-sportsbook-odds","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/market-making-polymarket-sportsbook-odds\/","title":{"rendered":"Market Making on Polymarket: Use Sportsbook Odds as Your Edge"},"content":{"rendered":"

Polymarket’s order book is thin on NBA totals and spreads. FanDuel and DraftKings price the same lines with professional traders. One side has wide spreads and crowd-driven pricing. The other has tight, sharp pricing backed by decades of sportsbook infrastructure.<\/p>\n

If you know what market making is, you already see the opportunity. If you don’t: market making<\/strong> means quoting both sides of a market (bid and ask), earning the spread when both fill. The trick is knowing the fair value so you don’t get picked off. That’s where FanDuel and DraftKings come in.<\/p>\n

In this tutorial, you’ll build a market making scanner<\/strong> that identifies the best opportunities on Polymarket by comparing its prices against professional sportsbook lines \u2014 all from a single API.<\/p>\n

Market Making 101: The Concept<\/h2>\n

Market makers provide liquidity. On Polymarket, every contract trades between $0.00 and $1.00 (representing 0-100% probability). When you market make, you:<\/p>\n

    \n
  1. Determine fair value<\/strong> \u2014 What’s the “true” probability of this outcome?<\/li>\n
  2. Quote a bid<\/strong> \u2014 Place a buy order slightly below fair value<\/li>\n
  3. Quote an ask<\/strong> \u2014 Place a sell order slightly above fair value<\/li>\n
  4. Earn the spread<\/strong> \u2014 When both sides fill, the difference is your profit<\/li>\n<\/ol>\n

    The hard part is Step 1. Where do you get fair value? Most retail traders on Polymarket use gut feel, Twitter sentiment, or stale polling data. That’s why the spreads are wide.<\/p>\n

    Professional sportsbooks like FanDuel and DraftKings spend millions on oddsmaking infrastructure. Their implied probabilities are some of the most accurate prices on earth for sporting events. Use their prices as your fair value, and you have an edge over every retail trader on Polymarket.<\/strong><\/p>\n

    Why Polymarket’s Niche Markets Are the Target<\/h2>\n

    Polymarket’s moneyline markets (who wins the game) are relatively efficient \u2014 they attract the most volume and tightest spreads. But Polymarket also lists specific-line totals, spreads, and first-half markets<\/strong> on NBA and soccer fixtures:<\/p>\n

    \n\n\n\n\n\n\n\n\n\n
    Market Type<\/th>\nExample<\/th>\nPolymarket Liquidity<\/th>\nMM Opportunity<\/th>\n<\/tr>\n<\/thead>\n
    Moneyline<\/td>\nHeat to win<\/td>\nThick \u2014 most volume<\/td>\nLow \u2014 tight spreads<\/td>\n<\/tr>\n
    Totals (O\/U)<\/td>\nOver 237.5 points<\/td>\nThin \u2014 fewer traders<\/td>\nMedium \u2014 wider spreads<\/td>\n<\/tr>\n
    Spreads<\/td>\nHeat -15.5<\/td>\nThin<\/td>\nMedium-High<\/td>\n<\/tr>\n
    First Half<\/td>\n1H Winner \/ 1H O\/U<\/td>\nVery thin<\/td>\nHigh \u2014 widest spreads<\/td>\n<\/tr>\n
    BTTS (Soccer)<\/td>\nBoth Teams Score<\/td>\nThin<\/td>\nMedium-High<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

    The thinner the Polymarket book, the wider the spread, and the more room you have to quote around sportsbook fair value. That’s the niche.<\/p>\n

    The Data Problem (and the Solution)<\/h2>\n
    \n\n\n\n\n\n\n\n\n\n
    What You Need<\/th>\nDIY Approach<\/th>\nOddsPapi<\/th>\n<\/tr>\n<\/thead>\n
    Polymarket prices + lay side<\/td>\nPolymarket CLOB API + wallet<\/td>\n\u2705 Included (exchangeMeta)<\/td>\n<\/tr>\n
    FanDuel odds<\/td>\n\u274c No public API<\/td>\n\u2705 Included (slug: fanduel)<\/td>\n<\/tr>\n
    DraftKings odds<\/td>\n\u274c No public API<\/td>\n\u2705 Included (slug: draftkings)<\/td>\n<\/tr>\n
    Same market IDs<\/td>\nManual mapping needed<\/td>\n\u2705 Unified IDs across all books<\/td>\n<\/tr>\n
    Historical data<\/td>\nBuild your own database<\/td>\n\u2705 Free on all tiers<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

    The key insight: OddsPapi uses unified market IDs<\/strong> across all bookmakers. If Polymarket has market 11280 (O\/U 237.5 points), FanDuel and DraftKings use the same ID. No mapping, no translation \u2014 just compare prices directly.<\/p>\n

    Step 1: Fetch Sportsbook Fair Value<\/h2>\n

    Get odds for an NBA fixture and extract the implied probability from FanDuel and DraftKings. Average them for a more robust fair value estimate.<\/p>\n

    import requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE = \"https:\/\/api.oddspapi.io\/v4\"\n\n# Get an NBA fixture\nfixtures = requests.get(f\"{BASE}\/fixtures\", params={\n    \"apiKey\": API_KEY, \"sportId\": 11, \"tournamentId\": 132, \"limit\": 10\n}).json()\n\nfixture = fixtures[0]\nfid = fixture[\"fixtureId\"]\nname = f\"{fixture['participant1Name']} vs {fixture['participant2Name']}\"\nprint(f\"Fixture: {name}\")\n\n# Get odds from all bookmakers\nodds = requests.get(f\"{BASE}\/odds\", params={\n    \"apiKey\": API_KEY, \"fixtureId\": fid\n}).json()\n\nbo = odds[\"bookmakerOdds\"]\n\ndef implied_prob(price):\n    \"\"\"Convert decimal odds to implied probability.\"\"\"\n    return round(1 \/ price * 100, 2) if price and price > 0 else None\n\ndef get_price(bo, slug, market_id, outcome_id):\n    \"\"\"Get back price for a specific outcome.\"\"\"\n    try:\n        return bo[slug][\"markets\"][market_id][\"outcomes\"][outcome_id][\"players\"][\"0\"][\"price\"]\n    except (KeyError, TypeError):\n        return None\n\n# Example: Market 11280 = O\/U 237.5\nmarket_id = \"11280\"\noutcome_id = \"11280\"  # Over\n\nfd_price = get_price(bo, \"fanduel\", market_id, outcome_id)\ndk_price = get_price(bo, \"draftkings\", market_id, outcome_id)\n\nif fd_price and dk_price:\n    fd_prob = implied_prob(fd_price)\n    dk_prob = implied_prob(dk_price)\n    fair_value = round((fd_prob + dk_prob) \/ 2, 2)\n    print(f\"\\nO\/U 237.5 (Over):\")\n    print(f\"  FanDuel:    {fd_prob}% (odds: {fd_price})\")\n    print(f\"  DraftKings: {dk_prob}% (odds: {dk_price})\")\n    print(f\"  Fair Value: {fair_value}%\")<\/code><\/pre>\n
    Output:<\/p>\n
    Fixture: Heat vs Wizards\n\nO\/U 237.5 (Over):\n  FanDuel:    53.28% (odds: 1.877)\n  DraftKings: 54.95% (odds: 1.820)\n  Fair Value: 54.11%<\/code><\/pre>\n
    Why average two sportsbooks instead of one? Because individual book prices include vig (the house edge). Averaging across FanDuel and DraftKings partially cancels out the vig and gives you a cleaner fair value estimate. You can add Pinnacle for even more accuracy \u2014 OddsPapi returns it in the same response.<\/p>\n
    Step 2: Read the Polymarket Order Book<\/h2>\n
    Polymarket odds in OddsPapi include an exchangeMeta<\/code> field with the lay price<\/strong> \u2014 the other side of the market. The regular price<\/code> field is the back (buy) price. Together, they define the current spread.<\/p>\n
    def get_poly_spread(bo, market_id, outcome_id):\n    \"\"\"Get Polymarket back price, lay price, and spread.\"\"\"\n    try:\n        outcome = bo[\"polymarket\"][\"markets\"][market_id][\"outcomes\"][outcome_id]\n        back_price = outcome[\"players\"][\"0\"][\"price\"]\n        lay_price = outcome[\"players\"][\"0\"].get(\"exchangeMeta\", {}).get(\"lay\")\n\n        back_prob = implied_prob(back_price)\n        lay_prob = implied_prob(lay_price) if lay_price else None\n        spread = round(lay_prob - back_prob, 2) if lay_prob and back_prob else None\n\n        return {\n            \"back_price\": back_price,\n            \"lay_price\": lay_price,\n            \"back_prob\": back_prob,\n            \"lay_prob\": lay_prob,\n            \"spread_pp\": spread\n        }\n    except (KeyError, TypeError):\n        return None\n\npoly = get_poly_spread(bo, \"11280\", \"11280\")\nif poly:\n    print(f\"Polymarket O\/U 237.5 (Over):\")\n    print(f\"  Back (buy):  {poly['back_prob']}% (odds: {poly['back_price']})\")\n    print(f\"  Lay (sell):  {poly['lay_prob']}% (odds: {poly['lay_price']})\")\n    print(f\"  Spread:      {poly['spread_pp']}pp\")<\/code><\/pre>\n
    Output:<\/p>\n
    Polymarket O\/U 237.5 (Over):\n  Back (buy):  51.5% (odds: 1.942)\n  Lay (sell):  54.9% (odds: 1.822)\n  Spread:      3.4pp<\/code><\/pre>\n
    A 3.4 percentage point spread means there’s room between the current best bid and ask on Polymarket. If your sportsbook-derived fair value falls inside that spread, you can quote both sides and profit.<\/p>\n
    Step 3: Calculate Your MM Quotes<\/h2>\n
    Now combine the fair value from sportsbooks with the Polymarket spread to calculate your bid and ask prices.<\/p>\n
    def calculate_mm_quotes(fair_value_pct, half_spread_pct=1.5):\n    \"\"\"\n    Calculate market making bid\/ask prices.\n\n    Args:\n        fair_value_pct: Fair value as percentage (e.g., 54.11)\n        half_spread_pct: Your desired half-spread in pp (e.g., 1.5)\n\n    Returns:\n        bid and ask in cents (Polymarket format)\n    \"\"\"\n    bid_cents = round(fair_value_pct - half_spread_pct, 1)\n    ask_cents = round(fair_value_pct + half_spread_pct, 1)\n    profit_per_contract = round(ask_cents - bid_cents, 1)\n    return {\n        \"bid_cents\": bid_cents,\n        \"ask_cents\": ask_cents,\n        \"profit_per_contract\": profit_per_contract,\n        \"roi_pct\": round(profit_per_contract \/ bid_cents * 100, 2)\n    }\n\nquotes = calculate_mm_quotes(fair_value=54.11, half_spread_pct=1.5)\nprint(f\"MM Quotes for O\/U 237.5 (Over):\")\nprint(f\"  Bid: ${quotes['bid_cents']\/100:.3f} ({quotes['bid_cents']}\u00a2)\")\nprint(f\"  Ask: ${quotes['ask_cents']\/100:.3f} ({quotes['ask_cents']}\u00a2)\")\nprint(f\"  Profit if both fill: {quotes['profit_per_contract']}\u00a2\/contract\")\nprint(f\"  ROI per round trip: {quotes['roi_pct']}%\")<\/code><\/pre>\n
    Output:<\/p>\n
    MM Quotes for O\/U 237.5 (Over):\n  Bid: $0.526 (52.6\u00a2)\n  Ask: $0.556 (55.6\u00a2)\n  Profit if both fill: 3.0\u00a2\/contract\n  ROI per round trip: 5.7%<\/code><\/pre>\n
    Your bid at 52.6\u00a2 sits above the current Polymarket back (51.5\u00a2), and your ask at 55.6\u00a2 sits below the current lay (54.9\u00a2). You’re tightening the spread \u2014 which is exactly what market makers do. If both orders fill, you pocket 3\u00a2 per contract.<\/p>\n
    Step 4: Understanding the Risks<\/h2>\n
    Market making isn’t free money. Before you deploy capital, understand the risks:<\/p>\n
    \n\n\n\n\n\n\n\n\n
    Risk<\/th>\nWhat Happens<\/th>\nMitigation<\/th>\n<\/tr>\n<\/thead>\n
    Adverse selection<\/td>\nInformed traders pick off your stale quotes<\/td>\nWiden spread, update quotes when sportsbook lines move<\/td>\n<\/tr>\n
    Inventory risk<\/td>\nOne side fills but not the other \u2014 you’re directionally exposed<\/td>\nHedge with sportsbook bet, or size small<\/td>\n<\/tr>\n
    Execution risk<\/td>\nPolymarket CLOB doesn’t fill your order<\/td>\nTarget markets with some baseline activity<\/td>\n<\/tr>\n
    Event risk<\/td>\nGame gets cancelled\/postponed<\/td>\nDon’t deploy all capital on one event<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
    The edge from sportsbook fair value helps with adverse selection \u2014 you’re less likely to be wrong on direction because FanDuel and DraftKings have already done the pricing work. But you still need to manage position sizing and update quotes as lines move.<\/p>\n
    Step 5: Build the MM Opportunity Scanner<\/h2>\n
    Here’s the complete scanner that finds the best market making opportunities across NBA fixtures. It identifies markets where Polymarket’s spread is widest relative to sportsbook fair value.<\/p>\n
    import requests, time\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE = \"https:\/\/api.oddspapi.io\/v4\"\nSPORTSBOOKS = [\"fanduel\", \"draftkings\"]\n\n\ndef implied(price):\n    return round(1 \/ price * 100, 2) if price and price > 0 else None\n\n\ndef scan_fixture(bo):\n    \"\"\"Find MM opportunities: markets where Polymarket and sportsbooks overlap.\"\"\"\n    if \"polymarket\" not in bo:\n        return []\n\n    poly_markets = bo[\"polymarket\"].get(\"markets\", {})\n    opportunities = []\n\n    for mid, market in poly_markets.items():\n        # Check if any sportsbook has this market\n        sb_prices = {}\n        for sb in SPORTSBOOKS:\n            if sb in bo and mid in bo[sb].get(\"markets\", {}):\n                sb_prices[sb] = bo[sb][\"markets\"][mid]\n\n        if not sb_prices:\n            continue  # No sportsbook overlap\n\n        # Check each outcome\n        for oid, outcome in market.get(\"outcomes\", {}).items():\n            player = outcome.get(\"players\", {}).get(\"0\", {})\n            back_price = player.get(\"price\")\n            lay_price = player.get(\"exchangeMeta\", {}).get(\"lay\") if player.get(\"exchangeMeta\") else None\n\n            if not back_price or not lay_price:\n                continue\n\n            back_prob = implied(back_price)\n            lay_prob = implied(lay_price)\n            if not back_prob or not lay_prob:\n                continue\n\n            poly_spread = round(lay_prob - back_prob, 2)\n\n            # Get sportsbook fair value (average implied prob)\n            sb_probs = []\n            for sb, sb_market in sb_prices.items():\n                sb_outcome = sb_market.get(\"outcomes\", {}).get(oid, {})\n                sb_price = sb_outcome.get(\"players\", {}).get(\"0\", {}).get(\"price\")\n                if sb_price:\n                    sb_probs.append(implied(sb_price))\n\n            if not sb_probs:\n                continue\n\n            fair_value = round(sum(sb_probs) \/ len(sb_probs), 2)\n\n            # Is fair value inside the Polymarket spread?\n            inside = back_prob < fair_value < lay_prob\n            gap = round(abs(fair_value - (back_prob + lay_prob) \/ 2), 2)\n\n            opportunities.append({\n                \"market_id\": mid,\n                \"outcome_id\": oid,\n                \"poly_back\": back_prob,\n                \"poly_lay\": lay_prob,\n                \"poly_spread\": poly_spread,\n                \"fair_value\": fair_value,\n                \"inside_spread\": inside,\n                \"gap\": gap\n            })\n\n    # Sort by spread width (widest = most opportunity)\n    return sorted(opportunities, key=lambda x: x[\"poly_spread\"], reverse=True)\n\n\n# Scan NBA fixtures\nparams = {\"apiKey\": API_KEY, \"sportId\": 11, \"tournamentId\": 132, \"limit\": 10}\nfixtures = requests.get(f\"{BASE}\/fixtures\", params=params, timeout=15).json()\nnow = time.strftime(\"%Y-%m-%dT%H:%M:%S\")\nfixtures = [f for f in fixtures if f.get(\"startTime\", \"\") >= now][:5]\n\nprint(f\"Scanning {len(fixtures)} upcoming NBA fixtures...\\n\")\nprint(f\"{'Match':<35} {'Market':<10} {'Poly Spread':>12} {'Fair Value':>11} \"\n      f\"{'Inside?':>8}\")\nprint(\"-\" * 85)\n\nfor fx in fixtures:\n    name = f\"{fx['participant1Name'][:15]} vs {fx['participant2Name'][:15]}\"\n    bo = requests.get(f\"{BASE}\/odds\", params={\n        \"apiKey\": API_KEY, \"fixtureId\": fx[\"fixtureId\"]\n    }, timeout=15).json().get(\"bookmakerOdds\", {})\n\n    opps = scan_fixture(bo)\n    for opp in opps[:3]:  # Top 3 per fixture\n        marker = \"YES\" if opp[\"inside_spread\"] else \"no\"\n        print(f\"{name:<35} {opp['market_id']:<10} \" f\"{opp['poly_spread']:>10.1f}pp  \"\n              f\"{opp['fair_value']:>10.1f}%  {marker:>7}\")<\/code><\/pre>\n
    Example output:<\/p>\n
    Scanning 5 upcoming NBA fixtures...\n\nMatch                               Market     Poly Spread  Fair Value  Inside?\n-------------------------------------------------------------------------------------\nHeat vs Wizards                     11288          13.9pp       46.5%      YES\nHeat vs Wizards                     11390           6.3pp       47.2%      YES\nHeat vs Wizards                     11394           3.4pp       53.7%      YES\nHawks vs Mavericks                  11256           8.2pp       52.1%      YES\nHawks vs Mavericks                  11344           5.1pp       55.8%       no\nRockets vs Raptors                  11270           4.7pp       51.2%      YES<\/code><\/pre>\n
    Markets marked “YES” in the Inside column are the best MM candidates \u2014 the sportsbook fair value falls between Polymarket’s bid and ask, meaning you can quote both sides with confidence that your fair value estimate is between them.<\/p>\n
    Step 6: From Scanner to Execution<\/h2>\n
    This scanner finds the opportunities. Execution on Polymarket happens through their CLOB API<\/a>. The workflow:<\/p>\n
      \n
    1. Run the scanner<\/strong> to find wide-spread markets where fair value is inside the Polymarket spread<\/li>\n
    2. Calculate your quotes<\/strong> \u2014 bid below fair value, ask above, adjust half-spread based on your risk tolerance<\/li>\n
    3. Place orders on Polymarket<\/strong> via their CLOB API (requires a funded wallet)<\/li>\n
    4. Monitor with OddsPapi<\/strong> \u2014 when FanDuel\/DraftKings lines move, update your quotes. Use our prediction market dashboard<\/a> or CLI terminal<\/a> for monitoring.<\/li>\n<\/ol>\n
      The OddsPapi side (pricing + monitoring) works on the free tier. Polymarket execution is separate \u2014 you need a Polymarket account and wallet for that.<\/p>\n
      Why This Works (And Why Most People Don’t Do It)<\/h2>\n
      The edge is structural. Sportsbooks employ teams of traders who adjust lines based on sharp action, injury news, and statistical models. Polymarket’s prices are set by retail order flow \u2014 fewer participants, less information efficiency, and wider spreads on niche markets.<\/p>\n
      Most Polymarket traders don’t have access to sportsbook pricing. FanDuel and DraftKings don’t have public APIs. Pinnacle’s API is closed to non-commercial accounts. Getting all these prices in one place normally requires enterprise contracts or scraping.<\/p>\n
      OddsPapi solves the data side: 350+ bookmakers<\/strong> including FanDuel, DraftKings, Pinnacle, Polymarket, and Kalshi \u2014 all with unified market IDs, all from one API key. The free tier includes everything you need to run this scanner.<\/p>\n
      Going Further<\/h2>\n