esports odds API guide<\/a>.<\/p>\nStep 1: Authentication<\/h3>\n
OddsPapi uses an apiKey<\/code> query parameter, not a header. Every request needs it.<\/p>\nimport requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\n# Test the connection\nr = requests.get(f\"{BASE_URL}\/sports\", params={\"apiKey\": API_KEY})\nprint(r.status_code) # 200 = you're in\n<\/code><\/pre>\nStep 2: Discover the Esports Sport IDs<\/h3>\n
Each esports title is its own sport with its own ID. Pull them dynamically rather than hardcoding \u2014 the catalog grows.<\/p>\n
sports = requests.get(f\"{BASE_URL}\/sports\", params={\"apiKey\": API_KEY}).json()\nesports = [s for s in sports if s[\"slug\"].startswith(\"esport-\")]\n\nfor e in esports:\n print(f\"{e['sportId']:>3} {e['slug']:<32} {e['sportName']}\")\n\n# Output:\n# 16 esport-dota ESport Dota\n# 17 esport-counter-strike ESport Counter-Strike\n# 18 esport-league-of-legends ESport League of Legends\n# 56 esport-call-of-duty ESport Call of Duty\n# 57 esport-overwatch ESport Overwatch\n# 59 esport-rocket-league ESport Rocket League\n# 61 esport-valorant ESport Valorant\n# ... (12 esports total)\n<\/code><\/pre>\nStep 3: Pull Upcoming LoL Fixtures<\/h3>\n
The \/fixtures<\/code> endpoint takes a sport ID and a date range (max 10 days). Each fixture has a hasOdds<\/code> flag \u2014 only those will return prices when you call \/odds<\/code> next.<\/p>\nfrom datetime import date, timedelta\n\ntoday = date.today().isoformat()\nend = (date.today() + timedelta(days=7)).isoformat()\n\nr = requests.get(f\"{BASE_URL}\/fixtures\", params={\n \"apiKey\": API_KEY,\n \"sportId\": 18, # League of Legends\n \"from\": today,\n \"to\": end,\n})\n\nfixtures = r.json()\nwith_odds = [f for f in fixtures if f.get(\"hasOdds\")]\n\nprint(f\"LoL fixtures: {len(fixtures)} total, {len(with_odds)} with odds\")\nfor f in with_odds[:5]:\n print(f\" {f['tournamentName']}: {f['participant1Name']} vs {f['participant2Name']}\")\n\n# Output:\n# LoL fixtures: 135 total, 85 with odds\n# LEC: G2 Esports vs Team Vitality\n# LCS: 100 Thieves vs Cloud9\n# LCK: T1 vs Gen.G\n# LPL: JD Gaming vs Bilibili Gaming\n# Esports World Cup: Fnatic vs Hanwha Life Esports\n<\/code><\/pre>\nStep 4: Pull Live Odds for One Fixture<\/h3>\n
The \/odds<\/code> endpoint returns the current price across every bookmaker that's pricing the fixture. The response is heavily nested \u2014 read it carefully.<\/p>\nfixture_id = with_odds[0][\"fixtureId\"]\n\nodds = requests.get(f\"{BASE_URL}\/odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id,\n \"bookmakers\": \"1xbet,stake,betway,draftkings,polymarket\",\n}).json()\n\n# bookmakerOdds[slug][markets][market_id_str][outcomes][outcome_id_str][players]['0']['price']\nfor slug, book in odds[\"bookmakerOdds\"].items():\n for mid, market in book[\"markets\"].items():\n for oid, outcome in market[\"outcomes\"].items():\n price = outcome[\"players\"][\"0\"][\"price\"]\n print(f\" {slug:<12} market {mid} outcome {oid} -> {price}\")\n<\/code><\/pre>\nThat's the live snapshot for one fixture across five books. Swap in any of OddsPapi's 350+ bookmakers \u2014 sharp books, soft books, crypto books, prediction markets \u2014 by passing them as a comma-separated list.<\/p>\n
Step 5: Multi-Game Scan<\/h3>\n
The whole reason to switch from a per-game API is being able to loop across every esport in one script without four separate billing relationships:<\/p>\n
import time\n\nESPORTS = {\n 18: \"League of Legends\",\n 17: \"Counter-Strike\",\n 16: \"Dota 2\",\n 61: \"Valorant\",\n 56: \"Call of Duty\",\n}\n\ntoday = date.today().isoformat()\nend = (date.today() + timedelta(days=7)).isoformat()\n\nfor sport_id, name in ESPORTS.items():\n r = requests.get(f\"{BASE_URL}\/fixtures\", params={\n \"apiKey\": API_KEY, \"sportId\": sport_id,\n \"from\": today, \"to\": end,\n })\n fixtures = r.json() if r.status_code == 200 else []\n with_odds = [f for f in fixtures if f.get(\"hasOdds\")]\n print(f\"{name:<20} {len(with_odds):>3} priced fixtures (next 7 days)\")\n time.sleep(0.3) # rate-limit politeness\n<\/code><\/pre>\nOutput during a busy esports week:<\/p>\n
League of Legends 85 priced fixtures (next 7 days)\nCounter-Strike 10 priced fixtures (next 7 days)\nDota 2 6 priced fixtures (next 7 days)\nValorant 35 priced fixtures (next 7 days)\nCall of Duty 18 priced fixtures (next 7 days)\n<\/code><\/pre>\nThat's 154 priced fixtures across five esports. Through PandaScore at the live tier, that same coverage costs \u20ac5,000\/month. Through OddsPapi: free.<\/p>\n
Step 6: Historical Odds for Backtesting<\/h3>\n
If you're building a model, you need historical price movements to train against. PandaScore charges \u20ac400\/month per game for historical access. OddsPapi gives it away on the free tier through \/historical-odds<\/code> (max 3 bookmakers per call):<\/p>\nhistory = requests.get(f\"{BASE_URL}\/historical-odds\", params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id,\n \"bookmakers\": \"1xbet,stake,betway\",\n}).json()\n\n# \/historical-odds returns players[\"0\"] as a LIST of snapshots, not a dict\nfor slug, book in history[\"bookmakers\"].items():\n for mid, market in book[\"markets\"].items():\n for oid, outcome in market[\"outcomes\"].items():\n snapshots = outcome[\"players\"][\"0\"]\n print(f\"{slug} market {mid} outcome {oid}: {len(snapshots)} price points\")\n for snap in snapshots[:3]:\n print(f\" {snap['createdAt']} -> {snap['price']}\")\n<\/code><\/pre>\nLoop that across every priced fixture from a past split and you have a full backtesting dataset \u2014 for free.<\/p>\n