Cricket is the second-most bet sport on the planet. The IPL alone moves more money than most American leagues combined. But if you’re a developer trying to build anything \u2014 a model, a scanner, a dashboard \u2014 you’ll quickly discover the data ecosystem is a mess.<\/p>\n
The big bookmakers (Pinnacle, Bet365, Betfair) don’t offer public APIs. The cricket-specific data providers (CricketAPI, EntitySport) give you scores and stats, not odds. And the enterprise feeds from Sportradar and LSports will quote you five figures before you see a JSON response.<\/p>\n
OddsPapi sits in the middle: 20+ cricket bookmakers<\/strong> (including Pinnacle and the Betfair Exchange), 175+ live markets per fixture<\/strong>, and a free tier that doesn’t require a sales call. This guide walks you through pulling IPL, T20, and Test match odds with Python.<\/p>\n Every OddsPapi request uses a query parameter for auth \u2014 no headers, no OAuth.<\/p>\n Important terminology:<\/strong> OddsPapi uses “tournament” where you might say “league,” “fixture” instead of “match,” and “participant” instead of “team.” The cricket sport ID is 27<\/strong>.<\/p>\n The A typical 10-day window gives you 50\u201360 fixtures with odds<\/strong> across the IPL, Pakistan Super League, Big Bash, Caribbean Premier League, international T20s, and domestic competitions.<\/p>\n Tip:<\/strong> Only fixtures with The main cricket market is “Winner (incl. super over)”<\/strong> \u2014 market ID On a live IPL match, you’ll see 40\u201350 bookmakers<\/strong> pricing the match winner \u2014 including Pinnacle (the market benchmark), the Betfair Exchange, FanDuel, DraftKings, Dafabet, Betway, and dozens of regional books.<\/p>\n This is where it gets useful. Pinnacle sets the sharpest cricket line in the world. Comparing it against soft bookmakers is the foundation of every value betting and arbitrage strategy.<\/p>\n When a soft bookmaker is slow to move and Pinnacle has already shifted, the edge column lights up. That’s the signal arb scanners and value bettors are looking for.<\/p>\n Cricket isn’t just match-winner. OddsPapi covers 175+ markets per IPL fixture<\/strong>, including:<\/p>\n Don’t hardcode these \u2014 the catalog is too large and changes across formats. Query the market catalog and build a lookup:<\/p>\n If you’ve ever used the Betfair API, you know the pain: account verification, API keys, rate limits, session tokens. OddsPapi normalizes Betfair Exchange data into the same format as every other bookmaker \u2014 with the addition of back\/lay depth.<\/p>\n This gives you the full Betfair order book \u2014 back prices, lay prices, and available liquidity at each level \u2014 without touching the Betfair API directly.<\/p>\n Building a cricket prediction model? You need historical closing lines to measure accuracy. Most providers charge for this. OddsPapi includes it on the free tier.<\/p>\n Key difference:<\/strong> The live Here’s a complete script that scans all upcoming IPL fixtures and finds where soft bookmakers are offering better prices than Pinnacle’s fair line:<\/p>\n Coverage scales with liquidity. The IPL gets the deepest bookmaker coverage because it has the most betting volume. Niche domestic leagues still get odds, just from fewer books.<\/p>\n The Odds API<\/strong> covers cricket, but with ~40 bookmakers total across all sports. You won’t find Dafabet, TabTouch, Pamestoixima, or the other regional books that price cricket aggressively. And you won’t get Betfair Exchange depth.<\/p>\n CricketAPI (Roanuz)<\/strong> and EntitySport<\/strong> are excellent for scores, ball-by-ball data, and player stats. They don’t provide bookmaker odds. You’d need both CricketAPI and<\/em> OddsPapi to build a complete cricket data pipeline \u2014 stats from one, prices from the other.<\/p>\n Enterprise feeds<\/strong> (Sportradar, LSports, OddsMatrix) cover cricket with depth, but the free tier either doesn’t exist or is too restrictive for development work. OddsPapi gives you free historical data, WebSocket streaming, and 350+ bookmakers across all sports \u2014 cricket is one of 69 supported sports.<\/p>\nWhat You Get vs. What You’re Used To<\/h2>\n
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\n \n<\/th>\n Scraping \/ Manual<\/th>\n Enterprise Feed (Sportradar, LSports)<\/th>\n OddsPapi<\/th>\n<\/tr>\n<\/thead>\n \n Cricket Bookmakers<\/strong><\/td>\n 1 at a time<\/td>\n 10\u201340<\/td>\n 49 on IPL fixtures<\/strong><\/td>\n<\/tr>\n \n Sharp Lines (Pinnacle)<\/strong><\/td>\n No public API<\/td>\n Yes (enterprise only)<\/td>\n Yes (free tier)<\/strong><\/td>\n<\/tr>\n \n Betfair Exchange Depth<\/strong><\/td>\n Requires Betfair API key<\/td>\n Sometimes<\/td>\n Full back\/lay + liquidity<\/strong><\/td>\n<\/tr>\n \n Markets per Fixture<\/strong><\/td>\n Match winner only<\/td>\n 50\u2013100<\/td>\n 175+ (overs, powerplay, runs)<\/strong><\/td>\n<\/tr>\n \n Historical Odds<\/strong><\/td>\n Not available<\/td>\n $$$ add-on<\/td>\n Free (backtest your models)<\/strong><\/td>\n<\/tr>\n \n Price<\/strong><\/td>\n Free (until you get blocked)<\/td>\n $5K\u2013$50K\/year<\/td>\n Free tier available<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Step 1: Authentication & Sport Discovery<\/h2>\n
import requests\nimport time\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\n# Verify your key works\nresponse = requests.get(f\"{BASE_URL}\/sports\", params={\"apiKey\": API_KEY})\nsports = response.json()\n\n# Find cricket\ncricket = [s for s in sports if s[\"slug\"] == \"cricket\"][0]\nprint(f\"Sport: {cricket['name']} (ID: {cricket['sportId']})\")\n# Output: Sport: Cricket (ID: 27)<\/code><\/pre>\nStep 2: Fetch Cricket Fixtures (IPL, PSL, T20, Tests)<\/h2>\n
\/fixtures<\/code> endpoint returns upcoming and recent matches within a date range (max 10 days per call).<\/p>\nfrom datetime import datetime, timedelta\n\n# Get the next 10 days of cricket fixtures\ntoday = datetime.utcnow().strftime(\"%Y-%m-%d\")\nend = (datetime.utcnow() + timedelta(days=10)).strftime(\"%Y-%m-%d\")\n\nresponse = requests.get(f\"{BASE_URL}\/fixtures\", params={\n \"apiKey\": API_KEY,\n \"sportId\": 27,\n \"from\": today,\n \"to\": end\n})\nfixtures = response.json()\n\n# Filter to fixtures that actually have odds\nwith_odds = [f for f in fixtures if f.get(\"hasOdds\")]\nprint(f\"Total fixtures: {len(fixtures)}, With odds: {len(with_odds)}\")\n\n# Group by tournament\nfrom collections import Counter\ntournaments = Counter(f[\"tournamentName\"] for f in with_odds)\nfor name, count in tournaments.most_common(10):\n print(f\" {name}: {count} fixtures\")<\/code><\/pre>\nhasOdds: true<\/code> will return pricing from the \/odds<\/code> endpoint. The rest are metadata-only.<\/p>\nStep 3: Pull Live Odds \u2014 Match Winner<\/h2>\n
271<\/code>. This is your match-winner line.<\/p>\n# Pick an IPL fixture\nipl_fixtures = [f for f in with_odds if \"Indian Premier League\" in f.get(\"tournamentName\", \"\")]\n\nif not ipl_fixtures:\n print(\"No IPL fixtures in range \u2014 try a different date window\")\nelse:\n fixture = ipl_fixtures[0]\n fid = fixture[\"fixtureId\"]\n print(f\"{fixture['participant1Name']} vs {fixture['participant2Name']}\")\n print(f\"Fixture ID: {fid}\")\n\n time.sleep(0.2)\n\n # Get odds from all bookmakers\n odds_response = requests.get(f\"{BASE_URL}\/odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fid\n })\n odds_data = odds_response.json()\n\n # Parse match-winner prices (market 271)\n MATCH_WINNER = \"271\"\n\n for slug, bookie_data in odds_data[\"bookmakerOdds\"].items():\n markets = bookie_data.get(\"markets\", {})\n if MATCH_WINNER in markets:\n outcomes = markets[MATCH_WINNER][\"outcomes\"]\n home = outcomes.get(\"271\", {}).get(\"players\", {}).get(\"0\", {})\n away = outcomes.get(\"272\", {}).get(\"players\", {}).get(\"0\", {})\n print(f\" {slug:20s} {home.get('price', '-'):>6} {away.get('price', '-'):>6}\")<\/code><\/pre>\nStep 4: Compare Sharp vs. Soft Lines<\/h2>\n
# Build a comparison table: sharp vs soft\nSHARP_BOOKS = [\"pinnacle\"]\nSOFT_BOOKS = [\"fanduel\", \"draftkings\", \"betway\", \"dafabet\", \"paddypower\"]\n\ndef get_match_winner_prices(odds_data, market_id=\"271\"):\n \"\"\"Extract match-winner prices for all bookmakers.\"\"\"\n prices = {}\n for slug, bdata in odds_data.get(\"bookmakerOdds\", {}).items():\n markets = bdata.get(\"markets\", {})\n if market_id in markets:\n outcomes = markets[market_id][\"outcomes\"]\n home_price = outcomes.get(\"271\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\")\n away_price = outcomes.get(\"272\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\")\n if home_price and away_price:\n prices[slug] = {\"home\": home_price, \"away\": away_price}\n return prices\n\nprices = get_match_winner_prices(odds_data)\n\nprint(f\"\\n{'Bookmaker':20s} {'Home':>8} {'Away':>8} {'Edge vs Pinnacle':>16}\")\nprint(\"-\" * 60)\n\npinnacle = prices.get(\"pinnacle\")\nif pinnacle:\n # Pinnacle implied probabilities (no-vig midpoint)\n pin_home_prob = 1 \/ pinnacle[\"home\"]\n pin_away_prob = 1 \/ pinnacle[\"away\"]\n overround = pin_home_prob + pin_away_prob\n fair_home = pin_home_prob \/ overround\n fair_away = pin_away_prob \/ overround\n\n for slug in SHARP_BOOKS + SOFT_BOOKS:\n if slug in prices:\n p = prices[slug]\n # Edge = (price * fair_prob) - 1\n home_edge = (p[\"home\"] * fair_home - 1) * 100\n away_edge = (p[\"away\"] * fair_away - 1) * 100\n best_edge = max(home_edge, away_edge)\n print(f\" {slug:20s} {p['home']:>8.3f} {p['away']:>8.3f} {best_edge:>+14.1f}%\")<\/code><\/pre>\nStep 5: Cricket-Specific Markets (Overs, Runs, Powerplay)<\/h2>\n
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\n \nMarket<\/th>\n Example ID<\/th>\n Description<\/th>\n<\/tr>\n<\/thead>\n \n Winner (incl. super over)<\/td>\n 271<\/td>\n Match winner \u2014 the main line<\/td>\n<\/tr>\n \n 1X2<\/td>\n 273<\/td>\n Home \/ Draw \/ Away (Test matches)<\/td>\n<\/tr>\n \n Tied Match<\/td>\n 274<\/td>\n Will the match end in a tie?<\/td>\n<\/tr>\n \n To Win the Coin Toss<\/td>\n 278<\/td>\n Coin toss winner<\/td>\n<\/tr>\n \n Over\/Under Runs 1st Innings<\/td>\n 27188+<\/td>\n Total runs lines (multiple thresholds)<\/td>\n<\/tr>\n \n 3 Overs 1st Innings<\/td>\n 271832+<\/td>\n Powerplay segment runs<\/td>\n<\/tr>\n \n 6 Overs 1st Innings<\/td>\n 273620+<\/td>\n Full powerplay lines<\/td>\n<\/tr>\n \n 10 Overs 1st Innings<\/td>\n 278092+<\/td>\n Mid-innings lines<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n # Build a market name lookup for cricket\ntime.sleep(0.2)\nresponse = requests.get(f\"{BASE_URL}\/markets\", params={\n \"apiKey\": API_KEY,\n \"sportId\": 27\n})\ncatalog = response.json()\n\nmarket_names = {m[\"marketId\"]: m[\"marketName\"] for m in catalog}\noutcome_names = {\n (m[\"marketId\"], o[\"outcomeId\"]): o[\"outcomeName\"]\n for m in catalog for o in m.get(\"outcomes\", [])\n}\n\nprint(f\"Cricket market catalog: {len(catalog)} entries\")\nprint(f\"Unique market types: {len(set(market_names.values()))}\")\n\n# Show what markets a fixture actually has\nfor slug in [\"pinnacle\"]:\n if slug in odds_data.get(\"bookmakerOdds\", {}):\n for mid in odds_data[\"bookmakerOdds\"][slug][\"markets\"]:\n name = market_names.get(int(mid), mid)\n print(f\" {mid}: {name}\")<\/code><\/pre>\nStep 6: Betfair Exchange \u2014 Full Depth of Book<\/h2>\n
# Betfair Exchange data comes with full order book depth\nbetfair = odds_data.get(\"bookmakerOdds\", {}).get(\"betfair-ex\", {})\n\nif betfair:\n for mid, mdata in betfair.get(\"markets\", {}).items():\n name = market_names.get(int(mid), mid)\n print(f\"\\nMarket: {name} (ID: {mid})\")\n\n for oid, odata in mdata.get(\"outcomes\", {}).items():\n p0 = odata.get(\"players\", {}).get(\"0\", {})\n price = p0.get(\"price\")\n meta = p0.get(\"exchangeMeta\", {})\n oname = outcome_names.get((int(mid), int(oid)), oid)\n\n print(f\" {oname}:\")\n print(f\" Back: {price} (available: {p0.get('limit', 0):.2f})\")\n\n if meta and \"availableToBack\" in meta:\n for level in meta[\"availableToBack\"][:3]:\n print(f\" {level['price']} x \u00a3{level['size']:.2f}\")\n for level in meta.get(\"availableToLay\", [])[:3]:\n print(f\" Lay {level['price']} x \u00a3{level['size']:.2f}\")<\/code><\/pre>\nStep 7: Historical Odds for Backtesting<\/h2>\n
# Pull historical odds for a completed fixture\n# Note: historical endpoint uses \"bookmakers\" key (not \"bookmakerOdds\")\n# Max 3 bookmakers per call\n\ncompleted = [f for f in fixtures if not f.get(\"hasOdds\") or f[\"statusId\"] != 1]\n# Or use a known completed fixture ID\n\nHISTORICAL_FIXTURE = completed[0][\"fixtureId\"] if completed else fid\n\ntime.sleep(0.2)\nhist_response = requests.get(f\"{BASE_URL}\/historical-odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": HISTORICAL_FIXTURE,\n \"bookmakers\": \"pinnacle,betfair-ex,dafabet\"\n})\nhist_data = hist_response.json()\n\n# The structure is different from live odds:\n# hist_data[\"bookmakers\"][slug][\"markets\"][mid][\"outcomes\"][oid][\"players\"][\"0\"]\n# is a LIST of snapshots, not a single dict\n\nfor slug, bdata in hist_data.get(\"bookmakers\", {}).items():\n for mid, mdata in bdata.get(\"markets\", {}).items():\n for oid, odata in mdata.get(\"outcomes\", {}).items():\n snapshots = odata.get(\"players\", {}).get(\"0\", [])\n if snapshots:\n print(f\"\\n{slug} | Market {mid} | Outcome {oid}\")\n print(f\" {len(snapshots)} price snapshots\")\n print(f\" Opening: {snapshots[0]['price']} at {snapshots[0]['createdAt']}\")\n print(f\" Closing: {snapshots[-1]['price']} at {snapshots[-1]['createdAt']}\")<\/code><\/pre>\n\/odds<\/code> endpoint returns bookmakerOdds<\/code> with a single price per outcome. The \/historical-odds<\/code> endpoint returns bookmakers<\/code> with a list<\/strong> of timestamped price snapshots. Don’t mix up the response shapes.<\/p>\nStep 8: Full Pipeline \u2014 IPL Odds Scanner<\/h2>\n
import requests\nimport time\nfrom datetime import datetime, timedelta\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\ndef get_fixtures(sport_id, days=10):\n \"\"\"Fetch fixtures with odds for a date range.\"\"\"\n today = datetime.utcnow().strftime(\"%Y-%m-%d\")\n end = (datetime.utcnow() + timedelta(days=days)).strftime(\"%Y-%m-%d\")\n r = requests.get(f\"{BASE_URL}\/fixtures\", params={\n \"apiKey\": API_KEY, \"sportId\": sport_id, \"from\": today, \"to\": end\n })\n return [f for f in r.json() if f.get(\"hasOdds\")]\n\ndef get_odds(fixture_id):\n \"\"\"Fetch live odds for a fixture.\"\"\"\n r = requests.get(f\"{BASE_URL}\/odds\", params={\n \"apiKey\": API_KEY, \"fixtureId\": fixture_id\n })\n return r.json()\n\ndef find_value(odds_data, market_id=\"271\"):\n \"\"\"Compare all bookmakers against Pinnacle's fair line.\"\"\"\n pin = odds_data.get(\"bookmakerOdds\", {}).get(\"pinnacle\", {})\n pin_markets = pin.get(\"markets\", {})\n if market_id not in pin_markets:\n return []\n\n # Calculate Pinnacle fair probabilities\n outcomes = pin_markets[market_id][\"outcomes\"]\n pin_prices = {}\n for oid, odata in outcomes.items():\n p = odata.get(\"players\", {}).get(\"0\", {}).get(\"price\")\n if p:\n pin_prices[oid] = p\n\n if len(pin_prices) < 2:\n return []\n\n overround = sum(1\/p for p in pin_prices.values())\n fair_probs = {oid: (1\/p) \/ overround for oid, p in pin_prices.items()}\n\n # Scan all bookmakers\n edges = []\n for slug, bdata in odds_data.get(\"bookmakerOdds\", {}).items():\n if slug == \"pinnacle\":\n continue\n markets = bdata.get(\"markets\", {})\n if market_id not in markets:\n continue\n for oid, odata in markets[market_id].get(\"outcomes\", {}).items():\n price = odata.get(\"players\", {}).get(\"0\", {}).get(\"price\")\n if price and oid in fair_probs:\n edge = (price * fair_probs[oid] - 1) * 100\n if edge > 0:\n edges.append({\n \"bookmaker\": slug,\n \"outcome\": oid,\n \"price\": price,\n \"pinnacle_price\": pin_prices[oid],\n \"edge\": edge\n })\n\n return sorted(edges, key=lambda x: -x[\"edge\"])\n\n# Run the scanner\nfixtures = get_fixtures(sport_id=27)\nipl = [f for f in fixtures if \"Indian Premier League\" in f.get(\"tournamentName\", \"\")]\n\nprint(f\"Scanning {len(ipl)} IPL fixtures...\\n\")\n\nfor fixture in ipl:\n name = f\"{fixture['participant1Name']} vs {fixture['participant2Name']}\"\n time.sleep(0.2)\n odds = get_odds(fixture[\"fixtureId\"])\n values = find_value(odds)\n\n if values:\n print(f\"\\n{name}\")\n for v in values[:5]:\n side = \"Home\" if v[\"outcome\"] == \"271\" else \"Away\"\n print(f\" {v['bookmaker']:20s} {side} {v['price']:.3f} \"\n f\"(Pin: {v['pinnacle_price']:.3f}) Edge: {v['edge']:+.1f}%\")<\/code><\/pre>\nCoverage: What Tournaments Are Included?<\/h2>\n
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\n \nTournament<\/th>\n Format<\/th>\n Typical Bookmakers<\/th>\n<\/tr>\n<\/thead>\n \n Indian Premier League (IPL)<\/strong><\/td>\n T20<\/td>\n 49+<\/td>\n<\/tr>\n \n Pakistan Super League (PSL)<\/strong><\/td>\n T20<\/td>\n 30+<\/td>\n<\/tr>\n \n Big Bash League<\/strong><\/td>\n T20<\/td>\n 20+<\/td>\n<\/tr>\n \n Caribbean Premier League<\/strong><\/td>\n T20<\/td>\n 20+<\/td>\n<\/tr>\n \n International T20s<\/strong><\/td>\n T20<\/td>\n 20+<\/td>\n<\/tr>\n \n ODI Series<\/strong><\/td>\n ODI<\/td>\n 20+<\/td>\n<\/tr>\n \n West Indies Championship<\/strong><\/td>\n First-class<\/td>\n 10+<\/td>\n<\/tr>\n \n County Cricket \/ Domestic<\/strong><\/td>\n Various<\/td>\n 10+<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Why Not Just Use The Odds API or CricketAPI?<\/h2>\n
Quick Reference: Cricket API Endpoints<\/h2>\n