Tennis is the sharpest live betting sport on the planet. Every point shifts the odds. Every break of serve creates a new market. A single tiebreak can flip a match \u2014 and the prices move in real time.<\/p>\n
But most odds APIs treat tennis as an afterthought. 20 bookmakers, no sharp lines, no ITF coverage, and maybe a match winner market if you’re lucky. If you’re building a live tennis model, a scanner, or tracking line movements across books \u2014 you need more than that.<\/p>\n
OddsPapi covers 110+ bookmakers per match<\/strong>, including Pinnacle, Singbet, and Betfair Exchange. That’s match winner, set betting, game totals (71 lines), game handicaps, tiebreak markets, and 159+ markets total \u2014 across 5,605 tournaments from ATP and WTA down to ITF Futures. All on a free tier.<\/p>\n Team sports move in quarters and halves. Tennis moves point by point. Here’s why that matters for your model:<\/p>\n Break of serve = massive line swing.<\/strong> A single break can shift match winner odds by 30-50%. If you’re tracking this across 110 bookmakers, you’ll see which books react first and which lag behind. That’s where the edge is.<\/p>\n Momentum is measurable.<\/strong> Tennis momentum is real and quantifiable \u2014 a player winning 5 straight games creates predictable pricing patterns across bookmakers. Sharp books like Pinnacle adjust instantly. Soft books like Bet365 lag. That gap is your signal.<\/p>\n Year-round fixtures.<\/strong> Unlike NFL (September-February) or NBA (October-June), tennis runs 11 months a year. ATP, WTA, Challengers, ITF Futures \u2014 there are fixtures every single day. Your models never go cold.<\/p>\n 279+ daily fixtures.<\/strong> On any given day, OddsPapi has 200-300+ tennis fixtures across all levels. That’s 200+ opportunities to test your live betting logic.<\/p>\n OddsPapi covers 5,605 tennis tournaments<\/strong> \u2014 if a bookmaker prices it, we have it:<\/p>\n That’s not 20 “top leagues” \u2014 it’s every tournament that bookmakers actively price, from Wimbledon Centre Court to ITF Futures in Monastir. Singles and doubles.<\/p>\n Each tennis fixture on OddsPapi can have 159+ unique markets<\/strong> across 22 market types. Here are the key ones with market IDs \u2014 you’ll need these for API calls:<\/p>\n This is where most APIs fall short. OddsPapi gives you 71 different total games lines<\/strong> \u2014 from 15.5 through 35.5 and beyond. Each line is a separate market ID with its own outcome IDs. That means you can compare Pinnacle’s Over 22.5 line against Bet365’s Over 22.5 line directly, across 64+ bookmakers per line.<\/p>\n Same for game handicaps: 41 separate lines, each independently priced by 59+ bookmakers.<\/p>\n This is where tennis gets interesting. Because odds move point-by-point, you can build a simple poller that detects line movements and steam moves across bookmakers in real time.<\/p>\n This is a starting point. In production, you’d store the history in a database, set alerts for specific thresholds, and compare movements between sharp and soft books to identify late-mover value.<\/p>\n The real edge in live tennis isn’t just having the data \u2014 it’s knowing which book moves first<\/strong>. Pinnacle and Singbet are the benchmarks. When Pinnacle’s price shifts mid-match, the soft books follow \u2014 but not immediately.<\/p>\n When Pinnacle shows 1.89 and Bet365 still shows 1.61 on the same outcome \u2014 that’s a 17% discrepancy on the same match. During live play, these gaps open and close every few minutes. That’s why tennis is the best sport for live odds arbitrage and CLV tracking.<\/p>\n Men’s Grand Slams are best-of-5 sets. Everything else is best-of-3. This changes the total games markets completely:<\/p>\n OddsPapi handles this automatically \u2014 the total games lines adjust to the format. You’ll see 71 lines for a Grand Slam match (wider range) and fewer for a best-of-3. The market IDs are the same; the bookmaker handicap values change.<\/p>\n OddsPapi includes historical odds data on the free tier. That means you can backtest your tennis models against real closing lines from Pinnacle and Betfair Exchange \u2014 without paying for a separate data subscription.<\/p>\n Use cases for historical tennis data:<\/p>\n Yes. OddsPapi’s free tier gives you 250 requests\/month with access to all 5,605 tennis tournaments, 110+ bookmakers, and historical data. No credit card required.<\/p>\nTennis Odds Coverage: OddsPapi vs The Competition<\/h2>\n
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\n \nFeature<\/th>\n The Odds API<\/th>\n SportsGameOdds<\/th>\n OddsPapi<\/th>\n<\/tr>\n<\/thead>\n \n Bookmakers per match<\/td>\n ~15-20<\/td>\n ~30<\/td>\n 110+<\/strong><\/td>\n<\/tr>\n \n Sharp books (Pinnacle, Singbet)<\/td>\n No<\/td>\n No<\/td>\n Yes<\/strong><\/td>\n<\/tr>\n \n Betfair Exchange (order book)<\/td>\n No<\/td>\n No<\/td>\n Yes<\/strong><\/td>\n<\/tr>\n \n Game totals lines<\/td>\n 1-2<\/td>\n Some<\/td>\n 71 lines<\/strong><\/td>\n<\/tr>\n \n Game handicap lines<\/td>\n Limited<\/td>\n Limited<\/td>\n 41 lines<\/strong><\/td>\n<\/tr>\n \n Set betting markets<\/td>\n Limited<\/td>\n Some<\/td>\n Full (exact sets, set winner, handicap)<\/strong><\/td>\n<\/tr>\n \n Tournaments<\/td>\n ~20 leagues<\/td>\n ~40 leagues<\/td>\n 5,605 tournaments<\/strong><\/td>\n<\/tr>\n \n Live\/in-play odds<\/td>\n Delayed<\/td>\n Limited<\/td>\n Real-time (REST + WebSocket)<\/strong><\/td>\n<\/tr>\n \n Historical data<\/td>\n Paid add-on<\/td>\n No<\/td>\n Free tier<\/strong><\/td>\n<\/tr>\n \n Free tier<\/td>\n 500 req\/mo<\/td>\n Limited<\/td>\n 250 req\/mo<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Why Tennis Is the Sharpest Live Betting Market<\/h2>\n
5,605 Tournaments: Every Level That Matters<\/h2>\n
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\n \nLevel<\/th>\n Examples<\/th>\n Coverage<\/th>\n<\/tr>\n<\/thead>\n \n Grand Slams<\/strong><\/td>\n Wimbledon, US Open, Australian Open, Roland Garros<\/td>\n 110+ bookmakers<\/td>\n<\/tr>\n \n ATP Masters 1000<\/strong><\/td>\n Indian Wells, Miami, Paris, Madrid, Rome<\/td>\n 110+ bookmakers<\/td>\n<\/tr>\n \n ATP 500\/250<\/strong><\/td>\n Dubai, Rotterdam, Basel, Houston, Acapulco<\/td>\n 100+ bookmakers<\/td>\n<\/tr>\n \n WTA 1000\/500\/250<\/strong><\/td>\n Charleston, Bogota, Monterrey<\/td>\n 100+ bookmakers<\/td>\n<\/tr>\n \n ATP Challengers<\/strong><\/td>\n San Luis Potosi, Sao Leopoldo, Miyazaki<\/td>\n 50+ bookmakers<\/td>\n<\/tr>\n \n ITF \/ UTR<\/strong><\/td>\n Monastir, Heraklion, Jackson, Nantes<\/td>\n 20+ bookmakers<\/td>\n<\/tr>\n \n Doubles<\/strong><\/td>\n All levels (Grand Slam through Challengers)<\/td>\n 50+ bookmakers<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Tennis Markets: 22 Market Types, 159+ Lines Per Match<\/h2>\n
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\n \nMarket<\/th>\n Market ID<\/th>\n Outcomes<\/th>\n Books<\/th>\n<\/tr>\n<\/thead>\n \n Match Winner<\/td>\n 121<\/td>\n Player 1 (121), Player 2 (122)<\/td>\n 102+<\/td>\n<\/tr>\n \n First Set Winner<\/td>\n 123<\/td>\n Player 1 (123), Player 2 (124)<\/td>\n 69+<\/td>\n<\/tr>\n \n Second Set Winner<\/td>\n 125<\/td>\n Player 1 (125), Player 2 (126)<\/td>\n 59+<\/td>\n<\/tr>\n \n Total Games Over\/Under<\/td>\n 1229+<\/td>\n Over (odd), Under (even)<\/td>\n 64+ per line<\/td>\n<\/tr>\n \n Game Handicap<\/td>\n 12179+<\/td>\n Player 1 (odd), Player 2 (even)<\/td>\n 59+ per line<\/td>\n<\/tr>\n \n Set Handicap<\/td>\n 12239+<\/td>\n Player 1 (odd), Player 2 (even)<\/td>\n 55+ per line<\/td>\n<\/tr>\n \n Total Sets Over\/Under<\/td>\n 12231+<\/td>\n Over, Under<\/td>\n 51+<\/td>\n<\/tr>\n \n Total Games First Set<\/td>\n 12267+<\/td>\n Over, Under<\/td>\n 54+ per line<\/td>\n<\/tr>\n \n Total Tiebreaks<\/td>\n 12251+<\/td>\n Over, Under<\/td>\n 40+<\/td>\n<\/tr>\n \n Exact Sets<\/td>\n 12877<\/td>\n 2-0, 2-1<\/td>\n 45+<\/td>\n<\/tr>\n \n Correct Score<\/td>\n 12883<\/td>\n Set-by-set scores<\/td>\n 30+<\/td>\n<\/tr>\n \n Odd\/Even Games<\/td>\n 12875<\/td>\n Odd, Even<\/td>\n 35+<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n 71 Total Games Lines \u2014 Not Just “Over\/Under 20.5”<\/h3>\n
Python Tutorial: Tennis Odds in 5 Steps<\/h2>\n
Step 1: Setup<\/h3>\n
import requests\nfrom datetime import datetime, timedelta\n\nAPI_KEY = \"YOUR_API_KEY\" # Free at oddspapi.io\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\nSPORT_ID = 12 # Tennis<\/code><\/pre>\nStep 2: Browse Tournaments<\/h3>\n
def get_tournaments():\n \"\"\"Get all tennis tournaments.\"\"\"\n response = requests.get(f\"{BASE_URL}\/tournaments\", params={\n \"apiKey\": API_KEY,\n \"sportId\": SPORT_ID\n })\n tournaments = response.json()\n print(f\"{len(tournaments)} tournaments available\")\n return tournaments\n\ntournaments = get_tournaments()\n\n# Filter to Grand Slams and ATP\/WTA\nfor t in tournaments:\n name = t.get(\"tournamentName\", \"\")\n if any(s in name for s in [\"Wimbledon\", \"US Open\", \"Australian Open\", \"ATP\", \"WTA\"]):\n live = t.get(\"liveFixtures\", 0)\n upcoming = t.get(\"upcomingFixtures\", 0)\n if live + upcoming > 0:\n print(f\" {name} (ID: {t['tournamentId']}) \u2014 {live} live, {upcoming} upcoming\")<\/code><\/pre>\nStep 3: Get Today’s Fixtures (Including Live)<\/h3>\n
def get_fixtures():\n \"\"\"Get today's tennis fixtures with odds.\"\"\"\n today = datetime.now().strftime(\"%Y-%m-%d\")\n tomorrow = (datetime.now() + timedelta(days=1)).strftime(\"%Y-%m-%d\")\n\n response = requests.get(f\"{BASE_URL}\/fixtures\", params={\n \"apiKey\": API_KEY,\n \"sportId\": SPORT_ID,\n \"from\": today,\n \"to\": tomorrow\n })\n\n fixtures = response.json()\n with_odds = [f for f in fixtures if f.get(\"hasOdds\")]\n live = [f for f in fixtures if f.get(\"statusId\") == 3]\n\n print(f\"{len(fixtures)} fixtures today | {len(with_odds)} with odds | {len(live)} live\")\n return fixtures\n\nfixtures = get_fixtures()\nfor f in fixtures[:10]:\n status = \"LIVE\" if f.get(\"statusId\") == 3 else \"Pre-match\"\n print(f\" [{status}] {f['participant1Name']} vs {f['participant2Name']} ({f['tournamentName']})\")<\/code><\/pre>\nStep 4: Fetch Odds from 110+ Bookmakers<\/h3>\n
def get_odds(fixture_id):\n \"\"\"Get odds from all bookmakers for a fixture.\"\"\"\n response = requests.get(f\"{BASE_URL}\/odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id\n })\n return response.json()\n\n# Pick a fixture with odds\nfixture = next(f for f in fixtures if f.get(\"hasOdds\"))\nodds = get_odds(fixture[\"fixtureId\"])\nbookmakers = list(odds.get(\"bookmakerOdds\", {}).keys())\nprint(f\"{len(bookmakers)} bookmakers pricing {fixture['participant1Name']} vs {fixture['participant2Name']}\")<\/code><\/pre>\nStep 5: Compare Match Winner Odds Across Books<\/h3>\n
def compare_match_winner(odds_data):\n \"\"\"Compare match winner odds (market 121) across all bookmakers.\n\n Outcomes: 121 = Player 1, 122 = Player 2\n \"\"\"\n results = []\n\n for slug, bookie in odds_data.get(\"bookmakerOdds\", {}).items():\n market = bookie.get(\"markets\", {}).get(\"121\")\n if not market:\n continue\n\n p1 = market[\"outcomes\"].get(\"121\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\")\n p2 = market[\"outcomes\"].get(\"122\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\")\n\n if p1 and p2:\n results.append({\"bookmaker\": slug, \"player_1\": p1, \"player_2\": p2})\n\n results.sort(key=lambda x: x[\"player_1\"], reverse=True)\n\n print(f\"\\nMatch Winner from {len(results)} bookmakers:\")\n print(f\"{'Bookmaker':<20} {'Player 1':>10} {'Player 2':>10}\")\n print(\"-\" * 42)\n for r in results[:15]:\n print(f\"{r['bookmaker']:<20} {r['player_1']:>10.3f} {r['player_2']:>10.3f}\")\n\n # Best prices\n best_p1 = max(results, key=lambda x: x[\"player_1\"])\n best_p2 = max(results, key=lambda x: x[\"player_2\"])\n print(f\"\\nBest Player 1: {best_p1['player_1']} @ {best_p1['bookmaker']}\")\n print(f\"Best Player 2: {best_p2['player_2']} @ {best_p2['bookmaker']}\")\n\n return results\n\ncompare_match_winner(odds)<\/code><\/pre>\nLive Odds Movement Tracker<\/h2>\n
import time\n\ndef track_live_odds(fixture_id, market_id=\"121\", interval=30, duration=300):\n \"\"\"Track odds movement on a live tennis match.\n\n Polls every `interval` seconds for `duration` seconds.\n Detects when any bookmaker moves more than 5% between polls.\n \"\"\"\n history = {}\n start = time.time()\n\n print(f\"Tracking market {market_id} every {interval}s for {duration}s...\")\n\n while time.time() - start < duration:\n odds = get_odds(fixture_id)\n timestamp = datetime.now().strftime(\"%H:%M:%S\")\n movements = []\n\n for slug, bookie in odds.get(\"bookmakerOdds\", {}).items():\n market = bookie.get(\"markets\", {}).get(market_id)\n if not market:\n continue\n\n for outcome_id, outcome in market.get(\"outcomes\", {}).items():\n price = outcome.get(\"players\", {}).get(\"0\", {}).get(\"price\")\n if not price:\n continue\n\n key = f\"{slug}_{outcome_id}\"\n if key in history:\n old_price = history[key]\n change_pct = abs(price - old_price) \/ old_price * 100\n if change_pct > 5:\n direction = \"UP\" if price > old_price else \"DOWN\"\n movements.append({\n \"bookmaker\": slug,\n \"outcome\": outcome_id,\n \"old\": old_price,\n \"new\": price,\n \"change\": f\"{direction} {change_pct:.1f}%\"\n })\n\n history[key] = price\n\n if movements:\n print(f\"\\n[{timestamp}] {len(movements)} movement(s) detected:\")\n for m in movements:\n print(f\" {m['bookmaker']} outcome {m['outcome']}: \"\n f\"{m['old']:.3f} -> {m['new']:.3f} ({m['change']})\")\n else:\n print(f\"[{timestamp}] No significant movements\")\n\n time.sleep(interval)\n\n# Usage: track a live fixture\nlive_fixtures = [f for f in fixtures if f.get(\"statusId\") == 3 and f.get(\"hasOdds\")]\nif live_fixtures:\n track_live_odds(live_fixtures[0][\"fixtureId\"])<\/code><\/pre>\nSharp vs Soft: Track Where the Line Originates<\/h2>\n
def sharp_vs_soft_snapshot(odds_data, market_id=\"121\"):\n \"\"\"Compare sharp book prices vs soft book prices.\n\n Sharps: Pinnacle, Singbet, SBOBet\n Softs: Bet365, DraftKings, FanDuel, BetMGM\n \"\"\"\n sharps = [\"pinnacle\", \"singbet\", \"sbobet\"]\n softs = [\"bet365\", \"draftkings\", \"fanduel\", \"betmgm\", \"caesars\"]\n\n print(f\"\\n{'Book':<15} {'Type':<8} {'P1':>8} {'P2':>8}\")\n print(\"-\" * 41)\n\n for slug in sharps + softs:\n bookie = odds_data.get(\"bookmakerOdds\", {}).get(slug)\n if not bookie:\n continue\n\n market = bookie.get(\"markets\", {}).get(market_id)\n if not market:\n continue\n\n p1 = market[\"outcomes\"].get(\"121\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", \"N\/A\")\n p2 = market[\"outcomes\"].get(\"122\", {}).get(\"players\", {}).get(\"0\", {}).get(\"price\", \"N\/A\")\n book_type = \"SHARP\" if slug in sharps else \"SOFT\"\n\n print(f\"{slug:<15} {book_type:<8} {p1:>8} {p2:>8}\")\n\nsharp_vs_soft_snapshot(odds)<\/code><\/pre>\nGrand Slams vs Regular Tour: Why It Matters for Totals<\/h2>\n
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\n \nFormat<\/th>\n Events<\/th>\n Typical Total Games<\/th>\n Over\/Under Lines<\/th>\n<\/tr>\n<\/thead>\n \n Best-of-3<\/strong><\/td>\n ATP 250\/500\/1000, WTA all, Challengers<\/td>\n 18-26 games<\/td>\n Lines from 17.5 to 28.5<\/td>\n<\/tr>\n \n Best-of-5<\/strong><\/td>\n Men’s Grand Slams only<\/td>\n 30-45 games<\/td>\n Lines from 28.5 to 48.5<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n Free Historical Tennis Odds<\/h2>\n
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Frequently Asked Questions<\/h2>\n
Is there a free tennis odds API?<\/h3>\n
Can I get live tennis odds via API?<\/h3>\n