Step 2: Get Tonight’s NBA Games<\/h3>\ndef get_nba_fixtures():\n \"\"\"Get today's NBA 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 nba = [f for f in fixtures if \"NBA\" in f.get(\"tournamentName\", \"\") and f.get(\"hasOdds\")]\n print(f\"{len(nba)} NBA games tonight\")\n return nba\n\ngames = get_nba_fixtures()\nfor g in games:\n print(f\" {g['participant1Name']} vs {g['participant2Name']}\")<\/code><\/pre>\nThe fixtures endpoint returns a flat list. Filter by tournamentName<\/code> to isolate NBA games. The hasOdds<\/code> flag tells you which fixtures have pricing data available. OddsPapi covers 462 basketball tournaments globally \u2014 NBA, NCAA, EuroLeague, WNBA, CBA, and more.<\/p>\nStep 3: Fetch Odds from 117+ Bookmakers<\/h3>\ndef get_odds(fixture_id):\n \"\"\"Get odds from all bookmakers for a game.\"\"\"\n response = requests.get(f\"{BASE_URL}\/odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id\n })\n return response.json()\n\ngame = games[0]\nodds = get_odds(game[\"fixtureId\"])\nbookmakers = list(odds.get(\"bookmakerOdds\", {}).keys())\nprint(f\"{len(bookmakers)} bookmakers pricing {game['participant1Name']} vs {game['participant2Name']}\")<\/code><\/pre>\nOne call to \/odds<\/code> returns every bookmaker, every market, every outcome. The response is a flat dict with bookmakerOdds<\/code> at the top level \u2014 no pagination, no nested wrappers.<\/p>\nStep 4: Compare Moneylines<\/h3>\ndef compare_moneylines(odds_data):\n \"\"\"Compare moneyline (market 111) across all bookmakers.\"\"\"\n results = []\n\n for slug, bookie in odds_data.get(\"bookmakerOdds\", {}).items():\n market = bookie.get(\"markets\", {}).get(\"111\")\n if not market:\n continue\n\n row = {\"bookmaker\": slug}\n for outcome_id, outcome in market.get(\"outcomes\", {}).items():\n for player_id, player in outcome.get(\"players\", {}).items():\n boid = player.get(\"bookmakerOutcomeId\", \"\")\n price = player.get(\"price\")\n if \"home\" in str(boid).lower() or outcome_id == list(market[\"outcomes\"].keys())[0]:\n row[\"home\"] = price\n else:\n row[\"away\"] = price\n\n if \"home\" in row and \"away\" in row:\n results.append(row)\n\n results.sort(key=lambda x: x.get(\"home\", 0), reverse=True)\n\n print(f\"\\nMoneylines from {len(results)} bookmakers:\")\n print(f\"{'Bookmaker':<20} {'Home':>8} {'Away':>8}\")\n print(\"-\" * 38)\n for r in results[:10]:\n print(f\"{r['bookmaker']:<20} {r.get('home', 'N\/A'):>8} {r.get('away', 'N\/A'):>8}\")\n\n best_home = max(results, key=lambda x: x.get(\"home\", 0))\n best_away = max(results, key=lambda x: x.get(\"away\", 0))\n print(f\"\\nBest Home: {best_home['home']} @ {best_home['bookmaker']}\")\n print(f\"Best Away: {best_away['away']} @ {best_away['bookmaker']}\")\n\ncompare_moneylines(odds)<\/code><\/pre>\nMarket 111 is the moneyline \u2014 the only stable, universal market ID for NBA. Every bookmaker prices it. This function finds the best home and away odds across 111+ books in one pass.<\/p>\n
Step 5: Parse Spreads & Totals<\/h3>\ndef find_spreads(odds_data, target_line=None):\n \"\"\"Find spread markets and compare across bookmakers.\n\n NBA spreads use dynamic market IDs. The bookmakerOutcomeId\n contains the line (e.g., '-7.5\/home').\n \"\"\"\n spreads = {} # {line: [{bookmaker, home_price, away_price}]}\n\n for slug, bookie in odds_data.get(\"bookmakerOdds\", {}).items():\n for mid, market in bookie.get(\"markets\", {}).items():\n for oid, outcome in market.get(\"outcomes\", {}).items():\n for pid, player in outcome.get(\"players\", {}).items():\n boid = str(player.get(\"bookmakerOutcomeId\", \"\"))\n price = player.get(\"price\")\n\n # Spread boids look like \"-7.5\/home\" or \"-7.5\/away\"\n if \"\/\" in boid and (\"home\" in boid or \"away\" in boid):\n parts = boid.split(\"\/\")\n try:\n line = float(parts[0])\n side = parts[1]\n except (ValueError, IndexError):\n continue\n\n # Filter to spread-like lines (not totals which are 200+)\n if abs(line) > 50:\n continue\n\n if line not in spreads:\n spreads[line] = {}\n if slug not in spreads[line]:\n spreads[line][slug] = {}\n spreads[line][slug][side] = price\n\n # Show available lines\n print(f\"Available spread lines: {sorted(spreads.keys())}\")\n\n # Compare a specific line\n if target_line and target_line in spreads:\n line_data = spreads[target_line]\n print(f\"\\nSpread {target_line} from {len(line_data)} bookmakers:\")\n print(f\"{'Bookmaker':<20} {'Home':>8} {'Away':>8}\")\n print(\"-\" * 38)\n for slug, prices in sorted(line_data.items(), key=lambda x: x[1].get('home', 0), reverse=True)[:10]:\n print(f\"{slug:<20} {prices.get('home', 'N\/A'):>8} {prices.get('away', 'N\/A'):>8}\")\n\n return spreads\n\nspreads = find_spreads(odds)<\/code><\/pre>\nThe key insight: NBA spreads don’t have a single market ID. Each line (-7.5, -3.5, +1.5, etc.) gets its own unique market ID per fixture. The bookmakerOutcomeId<\/code> field is your decoder \u2014 it contains both the line and the side in a format like -7.5\/home<\/code>.<\/p>\nAccessing NBA Player Props<\/h2>\n
Player props are where the edge lives in NBA betting. Soft bookmakers are slow to adjust lines after injury news or lineup changes. With 117 bookmakers in one API call, you can spot stale lines in seconds.<\/p>\n
Player props use high market IDs (111xxx range). Each player\/prop combination \u2014 LeBron points over 25.5, Curry assists over 6.5 \u2014 gets its own market ID. The playerName<\/code> field identifies the player.<\/p>\ndef find_player_props(odds_data):\n \"\"\"Find player prop markets. Props have playerName set.\"\"\"\n props = []\n\n for slug, bookie in odds_data.get(\"bookmakerOdds\", {}).items():\n for mid, market in bookie.get(\"markets\", {}).items():\n for oid, outcome in market.get(\"outcomes\", {}).items():\n for pid, player in outcome.get(\"players\", {}).items():\n name = player.get(\"playerName\")\n if name: # Player props have playerName set\n props.append({\n \"market_id\": mid,\n \"player\": name,\n \"price\": player.get(\"price\"),\n \"bookmaker\": slug,\n \"line\": player.get(\"bookmakerOutcomeId\", \"\")\n })\n\n # Group by player\n from collections import defaultdict\n by_player = defaultdict(list)\n for p in props:\n by_player[p[\"player\"]].append(p)\n\n print(f\"Player props found: {len(props)} across {len(by_player)} players\")\n for player, entries in sorted(by_player.items()):\n books = len(set(e[\"bookmaker\"] for e in entries))\n markets = len(set(e[\"market_id\"] for e in entries))\n print(f\" {player}: {markets} prop markets from {books} bookmakers\")\n\nfind_player_props(odds)<\/code><\/pre>\nThis is the data that sharp bettors use to find value. When DraftKings has Jokic over 25.5 points at -110 and Pinnacle has it at -105, someone is wrong. OddsPapi lets you see both in one response.<\/p>\n
DraftKings vs Pinnacle: Where the Lines Diverge<\/h2>\n
DraftKings and FanDuel set the lines that 90% of the US market sees. Pinnacle sets the line that sharps respect. When these two diverge, there’s opportunity.<\/p>\n
def compare_books(odds_data, book1=\"pinnacle\", book2=\"draftkings\"):\n \"\"\"Compare two bookmakers across all shared markets.\"\"\"\n b1 = odds_data.get(\"bookmakerOdds\", {}).get(book1, {}).get(\"markets\", {})\n b2 = odds_data.get(\"bookmakerOdds\", {}).get(book2, {}).get(\"markets\", {})\n\n shared = set(b1.keys()) & set(b2.keys())\n print(f\"{book1} markets: {len(b1)}, {book2} markets: {len(b2)}, shared: {len(shared)}\")\n\n # Compare moneyline\n if \"111\" in shared:\n print(f\"\\nMoneyline (market 111):\")\n for slug, markets in [(book1, b1), (book2, b2)]:\n m = markets[\"111\"]\n for oid, outcome in m.get(\"outcomes\", {}).items():\n for pid, player in outcome.get(\"players\", {}).items():\n boid = player.get(\"bookmakerOutcomeId\", oid)\n print(f\" {slug}: {boid} = {player.get('price')}\")\n\ncompare_books(odds)<\/code><\/pre>\nPinnacle’s margin on NBA moneylines is typically 2-3%. DraftKings runs 4-5%. That difference is where value bettors and arbitrageurs operate. With OddsPapi, you can compare both \u2014 plus 115 other books \u2014 in a single API call.<\/p>\n
FAQ: NBA Odds API<\/h2>\nWhat sport ID is basketball\/NBA?<\/h3>\n
Sport ID 11 covers all basketball. The NBA specifically is tournament ID 132. Use sportId=11<\/code> in the fixtures endpoint, then filter by tournamentName<\/code> to isolate NBA games.<\/p>\nHow many bookmakers cover NBA games?<\/h3>\n
117+ bookmakers per game, including sharp books like Pinnacle, SBOBet, and Singbet, US books like DraftKings and FanDuel, the Betfair Exchange, and crypto books like 1xBet.<\/p>\n
Do you have NBA player props?<\/h3>\n
Yes. Each player\/prop combination has its own market ID in the 111xxx range. The playerName<\/code> field identifies the player, and the bookmakerOutcomeId<\/code> contains the line and direction.<\/p>\nHow do NBA spread market IDs work?<\/h3>\n
Each spread line gets a unique market ID per fixture. A game might have 50+ spread markets (one for each line like -7.5, -3.5, +1.5, etc.). Parse the bookmakerOutcomeId<\/code> field to get the actual line \u2014 it’ll look like -7.5\/home<\/code> or +7.5\/away<\/code>.<\/p>\nIs the API free for NBA data?<\/h3>\n
The free tier gives you 250 requests per month. That’s enough to check lines for every NBA game, every night. Historical odds data is also included free \u2014 backtest your models without upgrading.<\/p>\n
Do you cover college basketball (NCAA)?<\/h3>\n
Yes. OddsPapi covers 462 basketball tournaments globally, including NCAA, EuroLeague, WNBA, CBA (China), NBB (Brazil), and more. Same market depth, same bookmaker coverage.<\/p>\n