{"id":2970,"date":"2026-06-15T10:00:00","date_gmt":"2026-06-15T10:00:00","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=2970"},"modified":"2026-06-05T14:41:03","modified_gmt":"2026-06-05T14:41:03","slug":"free-sports-data-api","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/free-sports-data-api\/","title":{"rendered":"Free Sports Data API: 69 Sports of Schedules & Odds (Python)"},"content":{"rendered":"\n

Free Sports Data API: Schedules & Live Odds for 69 Sports<\/h2>\n\n

Search “free sports data API” and you get a wall of providers that either gate the good data behind a sales call or hand you a 20-event free tier that runs dry by lunchtime. If what you actually need is the betting-market data layer<\/strong> — fixtures, schedules, and live odds across every sport and bookmaker — this guide shows you how to pull it in Python from a genuinely free API.<\/p>\n\n

One honest note up front, because it saves you 20 minutes: OddsPapi is an odds-first<\/strong> sports data API. You get the schedule (who plays whom, when, and whether the game is live or finished) plus prices from 370 bookmakers<\/strong>. You do not<\/em> get play-by-play scores, box scores, or player stat lines. If you need final scores, every fixture ships a map of third-party IDs (Sofascore, Betradar, Pinnacle) so you can join results from a stats feed yourself. More on that below — we’ll be specific about what’s in the JSON and what isn’t.<\/p>\n\n

What “Sports Data” Actually Means (and What You’re Probably Searching For)<\/h2>\n\n

“Sports data API” is a catch-all. In practice, devs searching it want one of three different things:<\/p>\n\n

\n\n \n
You want…<\/th>Example<\/th>Is OddsPapi the right tool?<\/th><\/tr><\/thead>\n
Schedules & fixtures<\/td>“What MLB games are on tonight?”<\/td>\u2705 Yes — 69 sports, full boards<\/td><\/tr>\n
Betting odds & lines<\/td>“What’s the moneyline on Yankees vs Red Sox?”<\/td>\u2705 Yes — 370 books, live<\/td><\/tr>\n
Live & historical odds<\/td>“How did the line move before kickoff?”<\/td>\u2705 Yes — free historical endpoint<\/td><\/tr>\n
Final scores & results<\/td>“Who won, and what was the score?”<\/td>\u26a0\ufe0f Partial — status only, no score<\/td><\/tr>\n
Player\/team stats<\/td>“How many strikeouts did the pitcher throw?”<\/td>\u274c No — use a dedicated stats feed<\/td><\/tr>\n <\/tbody>\n<\/table>\n<\/figure>\n\n

If your project lives in the top three rows — an odds comparison tool, an arb scanner, a value-betting model, a line-movement tracker — the rest of this post is your fast path. If you only need the bottom two rows, you want a stats provider, not us, and we’d rather tell you that now than waste your free credits.<\/p>\n\n

The Old Way vs. The Free API<\/h2>\n\n
\n\n \n
The Old Way<\/th>OddsPapi<\/th><\/tr><\/thead>\n
Scrape each sportsbook site, fight Cloudflare, re-write your parser every time the HTML changes<\/td>One JSON endpoint, 370 books normalized to the same shape<\/td><\/tr>\n
“Free tier” capped at ~500 credits where one odds call burns 6+ credits<\/td>250 free requests\/month, each returning a full board of books<\/td><\/tr>\n
Historical odds locked behind a paid add-on<\/td>Free \/historical-odds<\/code> endpoint for backtesting<\/td><\/tr>\n
~40 bookmakers, no sharps<\/td>370 books including Pinnacle, Polymarket, Kalshi, crypto<\/td><\/tr>\n
Polling on a timer, always a few seconds stale<\/td>Real-time WebSocket push available<\/td><\/tr>\n <\/tbody>\n<\/table>\n<\/figure>\n\n

Step 1: Authentication<\/h2>\n\n

Auth is a query parameter, not a header. Grab a free key and pass it on every call.<\/p>\n\n

import requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\n# apiKey is a QUERY PARAMETER, never a header\nr = requests.get(f\"{BASE_URL}\/sports\", params={\"apiKey\": API_KEY})\nprint(r.status_code)          # 200\nprint(len(r.json()), \"sports\")  # 69\n<\/code><\/pre>\n\n
Step 2: Discover the Sports Catalog<\/h2>\n\n
The \/sports<\/code> endpoint returns all 69 sports with their IDs and slugs. You need the sportId<\/code> for every downstream call.<\/p>\n\n
sports = requests.get(f\"{BASE_URL}\/sports\", params={\"apiKey\": API_KEY}).json()\n\nfor s in sports[:8]:\n    print(s[\"sportId\"], s[\"slug\"], s[\"name\"])\n\n# 10 soccer Soccer\n# 11 basketball Basketball\n# 12 tennis Tennis\n# 13 baseball Baseball\n# 14 american-football American Football\n# 15 ice-hockey Ice Hockey\n# ...\n<\/code><\/pre>\n\n
Step 3: Pull the Schedule (Fixtures)<\/h2>\n\n
The \/fixtures<\/code> endpoint is your schedule feed. Give it a sportId<\/code> and a date range (max 10 days apart) and it returns every fixture — teams, start time, tournament, status, and whether odds are available.<\/p>\n\n
params = {\n    \"apiKey\": API_KEY,\n    \"sportId\": 13,            # Baseball\n    \"from\": \"2026-06-05\",\n    \"to\": \"2026-06-08\",\n}\nfixtures = requests.get(f\"{BASE_URL}\/fixtures\", params=params).json()\n\nfor f in fixtures:\n    if f[\"hasOdds\"] and f[\"statusName\"] == \"Pre-Game\":\n        print(f[\"fixtureId\"], f[\"participant1Name\"], \"vs\", f[\"participant2Name\"])\n\n# id1300010963301407 New York Yankees vs Boston Red Sox\n# id1300010963302659 Chicago Cubs vs San Francisco Giants\n# ...\n<\/code><\/pre>\n\n
How busy is the schedule? Here’s the real fixture count for a single 3-day window (June 5–8, 2026), and how many already carry live odds:<\/p>\n\n
\n\n  \n    
Sport<\/th>Fixtures (3-day window)<\/th>With live odds<\/th><\/tr><\/thead>\n  
Soccer<\/td>1,553<\/td>734<\/td><\/tr>\n    
Baseball<\/td>603<\/td>81<\/td><\/tr>\n    
Tennis<\/td>357<\/td>177<\/td><\/tr>\n    
Basketball<\/td>192<\/td>97<\/td><\/tr>\n    
Cricket<\/td>128<\/td>69<\/td><\/tr>\n    
Ice Hockey<\/td>21<\/td>12<\/td><\/tr>\n  <\/tbody>\n<\/table>\n<\/figure>\n\n
What’s in a Fixture Object (and What Isn’t)<\/h3>\n\n
This is the honesty section. Here’s a real fixture, trimmed:<\/p>\n\n
{\n  \"fixtureId\": \"id1300010963301407\",\n  \"sportId\": 13,\n  \"tournamentName\": \"MLB\",\n  \"startTime\": \"2026-06-05T23:05:00.000Z\",\n  \"statusName\": \"Pre-Game\",          # Pre-Game | Live | Finished | Cancelled\n  \"hasOdds\": true,\n  \"participant1Name\": \"New York Yankees\",\n  \"participant2Name\": \"Boston Red Sox\",\n  \"externalProviders\": {\n    \"sofascoreId\": 16217421,\n    \"betradarId\": 67360970,\n    \"pinnacleId\": 1631660206,\n    \"opticoddsId\": \"2026060539CEEF6E\"\n  }\n}\n<\/code><\/pre>\n\n
You get status<\/strong> (so you know if a game is live or final) but no score and no stats<\/strong>. The trade-off: every fixture carries externalProviders<\/code> — a map of IDs into Sofascore, Betradar, Pinnacle, and others. If you need final scores, use the sofascoreId<\/code> to look the result up in a stats feed and join it back to the odds on fixtureId<\/code>. That gives you a clean two-source pipeline: OddsPapi for the market data, a stats provider for the box score.<\/p>\n\n
Step 4: Fetch the Odds<\/h2>\n\n
Pass a fixtureId<\/code> to \/odds<\/code> and you get every bookmaker pricing that game. The response is nested — bookmakerOdds → slug → markets → marketId → outcomes → outcomeId → players[\"0\"] → price<\/code>.<\/p>\n\n
fid = \"id1300010963301407\"   # Yankees vs Red Sox\nodds = requests.get(f\"{BASE_URL}\/odds\",\n                    params={\"apiKey\": API_KEY, \"fixtureId\": fid}).json()\n\nbooks = odds[\"bookmakerOdds\"]\nprint(len(books), \"books on this fixture\")   # 14\n\ndef moneyline(slug, outcome):\n    # market 131 = moneyline; outcome 131 = home, 132 = away\n    try:\n        node = books[slug][\"markets\"][\"131\"][\"outcomes\"][outcome][\"players\"][\"0\"]\n        return node[\"price\"] if node.get(\"active\") else None\n    except KeyError:\n        return None\n\nfor slug in sorted(books):\n    home, away = moneyline(slug, \"131\"), moneyline(slug, \"132\")\n    if home and away:\n        print(f\"{slug:18} {home}  {away}\")\n\n# betmgm             1.67   2.25\n# caesars            1.69   2.22\n# draftkings         1.67   2.23\n# fanduel            1.69   2.22\n# kalshi             1.724  2.273\n# pinnacle           1.719  2.28\n# polymarket         1.724  2.326\n# williamhill        1.69   2.22\n<\/code><\/pre>\n\n
Ten of the 14 books priced the moneyline, including the sharp anchor (Pinnacle) and two prediction markets (Kalshi, Polymarket). That mix — sharps, US retail books, and prediction markets in one call — is the thing the ~40-book “free” APIs can’t give you.<\/p>\n\n
Step 5: Turn Raw Prices Into Fair Odds<\/h2>\n\n
Pinnacle is the sharpest book in the field, so de-vigging its line gives you a fair-probability benchmark to grade every other price against. Strip the margin with the simple two-way method:<\/p>\n\n
def devig_two_way(home, away):\n    ih, ia = 1\/home, 1\/away\n    total = ih + ia                 # > 1; the overround\n    return ih\/total, ia\/total, (total - 1) * 100\n\np_home, p_away, vig = devig_two_way(1.719, 2.28)   # Pinnacle\nprint(f\"Vig: {vig:.2f}%\")                           # 2.03%\nprint(f\"Fair: NYY {p_home*100:.1f}%  BOS {p_away*100:.1f}%\")\n# Fair: NYY 57.0%  BOS 43.0%   ->  fair odds 1.754 \/ 2.326\n<\/code><\/pre>\n\n
Pinnacle’s margin on this game was just 2.03%<\/strong>. The no-vig fair line was Yankees 1.754 \/ Red Sox 2.326. Now line-shop: the best available Red Sox price across all 10 books was Polymarket at 2.326<\/strong> — exactly the sharp fair number, and better than every retail book (FanDuel\/Caesars\/William Hill all sat at 2.22). The best Yankees price was Kalshi at 1.724. Finding those outliers is one loop:<\/p>\n\n
def best_price(books, market, outcome):\n    best, who = 0, None\n    for slug, data in books.items():\n        try:\n            node = data[\"markets\"][market][\"outcomes\"][outcome][\"players\"][\"0\"]\n            if node.get(\"active\") and node[\"price\"] > best:\n                best, who = node[\"price\"], slug\n        except KeyError:\n            continue\n    return who, best\n\nprint(best_price(books, \"131\", \"132\"))   # ('polymarket', 2.326)\n<\/code><\/pre>\n\n
Free Historical Odds (For Backtesting)<\/h2>\n\n
The same fixture has a price history. \/historical-odds<\/code> returns every recorded snapshot per book — free, where competitors charge for it. The shape differs from live: the top key is bookmakers<\/code> (not bookmakerOdds<\/code>), and players[\"0\"]<\/code> is a list<\/em> of snapshots, not a single price. Max 3 books per call.<\/p>\n\n
params = {\"apiKey\": API_KEY, \"fixtureId\": fid, \"bookmakers\": \"pinnacle\"}\nhist = requests.get(f\"{BASE_URL}\/historical-odds\", params=params).json()\n\nsnaps = hist[\"bookmakers\"][\"pinnacle\"][\"markets\"][\"131\"][\"outcomes\"][\"131\"][\"players\"][\"0\"]\nfor snap in snaps[:5]:\n    print(snap[\"createdAt\"], snap[\"price\"])\n<\/code><\/pre>\n\n
Feed that into a closing-line-value model and you can grade your bets against where the sharp line actually closed — the only honest measure of whether you’re beating the market. Our historical odds CSV\/Excel guide<\/a> walks through flattening this into a DataFrame.<\/p>\n\n
FAQ<\/h2>\n\n
Is there a truly free sports data API?<\/h3>\n
Yes. OddsPapi’s free tier gives you 250 requests\/month across 69 sports and 370 bookmakers, including free historical odds. There’s no credit card required and no sales call to unlock it.<\/p>\n\n
Does the API include live scores and player stats?<\/h3>\n
No. OddsPapi is odds-first: you get schedules, fixture status (Pre-Game\/Live\/Finished), and bookmaker odds, but not scores or stat lines. Each fixture carries externalProviders<\/code> IDs (Sofascore, Betradar) so you can join results from a dedicated stats feed.<\/p>\n\n
How many sports and bookmakers are covered?<\/h3>\n
69 sports and 370 bookmakers as of June 2026, spanning sharps (Pinnacle, SBOBet), US books (DraftKings, FanDuel, BetMGM), prediction markets (Polymarket, Kalshi), and crypto books (Stake, 1xBet).<\/p>\n\n
What’s the difference between this and an odds API?<\/h3>\n
None, functionally — “sports data API” and “odds API” describe the same product here. The data is fixtures plus odds. The only thing a generic “sports data” feed adds that we don’t is scores and stats.<\/p>\n\n
How do I get real-time updates instead of polling?<\/h3>\n
OddsPapi pushes price changes over a WebSocket feed<\/a> so you don’t have to poll on a timer. For most projects, polling \/odds<\/code> every few seconds on the free tier is plenty.<\/p>\n\n
Start Pulling Sports Data<\/h2>\n\n
You now have the full path: discover sports, pull the schedule, fetch odds from 370 books, de-vig against Pinnacle, and line-shop for the best price — all on a free key. Stop scraping sportsbook HTML. Get your free API key<\/a> and make your first call in the next five minutes.<\/p>\n\n
More tutorials: Free Odds API guide<\/a> · Sportsbook API<\/a> · Line shopping in Python<\/a> · Live betting API<\/a> · The Odds API free tier limits<\/a>.<\/p>\n\n