{"id":3062,"date":"2026-06-22T10:00:00","date_gmt":"2026-06-22T10:00:00","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=3062"},"modified":"2026-06-21T14:40:17","modified_gmt":"2026-06-21T14:40:17","slug":"odds-comparison-api","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/odds-comparison-api\/","title":{"rendered":"Odds Comparison API: Compare 372 Bookmakers in Python (Free)"},"content":{"rendered":"<h2>The Odds Comparison API You Actually Wanted<\/h2>\n<p>You have used the odds comparison sites. Oddschecker, Oddspedia, OddsJam: they all do one thing well, line up the same bet across dozens of bookmakers so you can see who is pricing it best. The problem is obvious the moment you try to build anything on top of them. None of the consumer comparison sites ship a public API. You are left scraping a table that changes its markup every other week, or paying enterprise rates for a feed locked behind a sales call.<\/p>\n<p>This guide skips that. OddsPapi is an <strong>odds comparison API<\/strong>: one HTTP call returns every bookmaker&#8217;s price for a fixture as clean JSON, across <strong>372 bookmakers<\/strong> and 69 sports, on a free tier. You build the comparison grid yourself in about 30 lines of Python, with no scraping and no contract.<\/p>\n<h2>Why Scraping a Comparison Site Is a Dead End<\/h2>\n<p>The comparison sites are products, not data providers. Their whole business is the front end, so the data underneath is deliberately hard to reach:<\/p>\n<ul>\n<li><strong>No public API.<\/strong> Oddschecker and Oddspedia have no documented developer endpoint. jedibets (a sharp player-props screener with Discord alerts) is a tool, not a feed. You cannot query them programmatically without reverse-engineering private calls.<\/li>\n<li><strong>Markup roulette.<\/strong> A scraper keys off CSS classes and DOM structure. One redesign and your parser is dead. You end up maintaining a brittle scraper instead of shipping features.<\/li>\n<li><strong>Rate limits and blocks.<\/strong> Hit a consumer site hard enough to power your own product and you get Cloudflare-challenged or IP-banned.<\/li>\n<li><strong>Enterprise feeds cost a sales call.<\/strong> The Sportradar \/ Genius tier gives you the data, but only after pricing negotiation and a contract.<\/li>\n<\/ul>\n<p>OddsPapi is the third option: enterprise-grade coverage (sharps like Pinnacle, crypto books, exchanges, prediction markets) at a developer price, queried over a plain REST endpoint with a free key.<\/p>\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>The Old Way<\/th>\n<th>OddsPapi Comparison API<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Scrape Oddschecker HTML, re-fix on every redesign<\/td>\n<td>One <code>GET \/odds<\/code> call returns clean JSON<\/td>\n<\/tr>\n<tr>\n<td>~40 bookmakers on a typical comparison site<\/td>\n<td>372 bookmakers, including sharps and exchanges<\/td>\n<\/tr>\n<tr>\n<td>No historical prices without paying<\/td>\n<td>Free historical odds endpoint for backtesting<\/td>\n<\/tr>\n<tr>\n<td>Poll a web page and hope it loads<\/td>\n<td>REST now, WebSocket push for real-time<\/td>\n<\/tr>\n<tr>\n<td>Risk of IP bans and CAPTCHAs<\/td>\n<td>API key auth, documented rate limits<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<h2>Build a Live Odds Comparison Grid in Python<\/h2>\n<p>The goal is the same data structure every comparison site renders: rows of bookmakers, columns of outcomes, each cell a price, with the best price in each column flagged. Here is the whole thing against the live API.<\/p>\n<h3>Step 1: Authenticate<\/h3>\n<p>Auth is a query parameter, never a header. That is the single most common mistake when starting out.<\/p>\n<pre class=\"wp-block-code\"><code>import requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"\n\ndef api_get(path, **params):\n    params[\"apiKey\"] = API_KEY\n    r = requests.get(f\"{BASE_URL}\/{path}\", params=params)\n    r.raise_for_status()\n    return r.json()\n\n# Smoke test: should print 200-style data\nprint(len(api_get(\"sports\")), \"sports available\")<\/code><\/pre>\n<h3>Step 2: Find a Fixture<\/h3>\n<p>Pull fixtures for a sport in a date window and keep the ones that already have odds. Sport IDs come from <code>\/sports<\/code> (baseball is 13). The <code>from<\/code>\/<code>to<\/code> window is capped at 10 days.<\/p>\n<pre class=\"wp-block-code\"><code>from datetime import datetime, timedelta\n\nnow = datetime.utcnow()\nwindow = {\n    \"from\": now.strftime(\"%Y-%m-%dT%H:%M:%S\"),\n    \"to\": (now + timedelta(days=1)).strftime(\"%Y-%m-%dT%H:%M:%S\"),\n}\n\nfixtures = api_get(\"fixtures\", sportId=13, **window)\nlive = [f for f in fixtures if f.get(\"hasOdds\")]\nlive.sort(key=lambda f: f[\"startTime\"])  # nearest first = best coverage\n\ngame = live[0]\nprint(game[\"participant1Name\"], \"vs\", game[\"participant2Name\"], game[\"fixtureId\"])<\/code><\/pre>\n<p>One thing to know: a fixture can report <code>hasOdds: true<\/code> days out but only return a populated book list once it is close to start time. Sort by start time and take the nearest fixtures and you get the deepest comparison.<\/p>\n<h3>Step 3: Fetch Every Book&#8217;s Price<\/h3>\n<p>A single <code>\/odds<\/code> call returns all bookmakers pricing the fixture. The payload is deeply nested, so never assume flat JSON. The path to a price is <code>bookmakerOdds[slug]['markets'][marketId]['outcomes'][outcomeId]['players']['0']['price']<\/code>.<\/p>\n<pre class=\"wp-block-code\"><code>odds = api_get(\"odds\", fixtureId=game[\"fixtureId\"])\nbooks = odds[\"bookmakerOdds\"]\nprint(len(books), \"bookmakers:\", sorted(books))<\/code><\/pre>\n<p>On Atlanta Braves vs Milwaukee Brewers this returned <strong>15 bookmakers<\/strong> on the one fixture, including Pinnacle (the sharp benchmark), Kalshi and Polymarket (prediction markets), and the US books DraftKings, FanDuel, BetMGM, Caesars and William Hill.<\/p>\n<h3>Step 4: Build the Comparison Grid<\/h3>\n<p>Now the core. Pick a market (moneyline on baseball is market <code>131<\/code>, &#8220;Winner incl. extra innings&#8221;; its two outcomes are <code>131<\/code> home and <code>132<\/code> away). Walk every book, pull the current price for each outcome, and skip anything inactive or zero-priced.<\/p>\n<pre class=\"wp-block-code\"><code>def comparison_grid(books, market_id, outcome_ids):\n    \"\"\"Return {bookmaker: {outcome_id: price}} for active prices only.\"\"\"\n    grid = {}\n    for slug, data in books.items():\n        market = data.get(\"markets\", {}).get(str(market_id))\n        if not market:\n            continue\n        row = {}\n        for oid in outcome_ids:\n            leg = market.get(\"outcomes\", {}).get(str(oid), {})\n            price = leg.get(\"players\", {}).get(\"0\")\n            # sportsbooks: players['0'] is a dict with one current price\n            if price and price.get(\"active\") and price.get(\"price\"):\n                row[oid] = price[\"price\"]\n        if len(row) == len(outcome_ids):  # only books pricing the full market\n            grid[slug] = row\n    return grid\n\ngrid = comparison_grid(books, market_id=131, outcome_ids=[131, 132])\n\n# Print it like a comparison table\nprint(f\"{'Bookmaker':16} {'Braves':>8} {'Brewers':>8}\")\nfor slug in sorted(grid):\n    print(f\"{slug:16} {grid[slug][131]:>8} {grid[slug][132]:>8}\")<\/code><\/pre>\n<p>Live output (11 of the 15 books priced this market):<\/p>\n<pre class=\"wp-block-code\"><code>Bookmaker         Braves  Brewers\nbetmgm              1.87     1.95\nborgata             1.87     1.95\ncaesars             1.87    1.952\ndraftkings          1.84     1.98\nfanduel             1.85      2.0\nhardrockbet          1.8     2.05\nkalshi             1.887    2.041\npinnacle           1.909     2.02\npointsbet.com.au    1.83      2.0\npolymarket         1.923    2.041\nwilliamhill         1.87    1.952<\/code><\/pre>\n<h3>Step 5: Flag the Best Price and the Spread<\/h3>\n<p>The reason anyone compares odds: the best available number is meaningfully better than the worst. Add two helpers.<\/p>\n<pre class=\"wp-block-code\"><code>def best_per_outcome(grid, outcome_ids):\n    out = {}\n    for oid in outcome_ids:\n        prices = {slug: row[oid] for slug, row in grid.items()}\n        best_book = max(prices, key=prices.get)\n        worst = min(prices.values())\n        out[oid] = {\"best_book\": best_book, \"best\": prices[best_book],\n                    \"worst\": worst,\n                    \"spread_pct\": (prices[best_book] \/ worst - 1) * 100}\n    return out\n\nfor oid, info in best_per_outcome(grid, [131, 132]).items():\n    print(oid, info)<\/code><\/pre>\n<p>Result, with real numbers:<\/p>\n<ul>\n<li><strong>Braves (home):<\/strong> best price <strong>1.923 at Polymarket<\/strong>, worst 1.80 at HardRock. A 6.8% spread on the exact same bet.<\/li>\n<li><strong>Brewers (away):<\/strong> best price <strong>2.05 at HardRock<\/strong>, worst 1.95 at BetMGM\/Borgata.<\/li>\n<\/ul>\n<p>Notice the prediction market (Polymarket) topped the field on the favorite while a US sportsbook (HardRock) led on the underdog. That cross-category coverage is exactly what a 40-book comparison site misses. These are best available prices, not value calls: against Pinnacle&#8217;s no-vig fair line (below) both sit just under fair. If your goal is to always bet the best number, our <a href=\"https:\/\/oddspapi.io\/blog\/line-shopping-python-best-odds\/\">line shopping guide<\/a> turns this grid into a single best-price helper.<\/p>\n<h3>Step 6: Anchor It to the Sharp Line<\/h3>\n<p>A comparison grid is more useful when you know the fair price. Pinnacle runs the tightest margin in the field, so strip its vig and use it as the benchmark.<\/p>\n<pre class=\"wp-block-code\"><code>def devig_two_way(p_home, p_away):\n    ih, ia = 1 \/ p_home, 1 \/ p_away\n    overround = ih + ia\n    return {\n        \"overround_pct\": (overround - 1) * 100,\n        \"fair_home\": overround \/ ih,   # fair decimal odds\n        \"fair_away\": overround \/ ia,\n        \"prob_home\": ih \/ overround,\n        \"prob_away\": ia \/ overround,\n    }\n\npin = grid[\"pinnacle\"]\nprint(devig_two_way(pin[131], pin[132]))<\/code><\/pre>\n<p>Pinnacle priced the Braves at 1.909 and the Brewers at 2.02, an overround of just <strong>1.89%<\/strong>. Stripped of vig that is Braves <strong>51.4%<\/strong> (fair 1.946) and Brewers <strong>48.6%<\/strong> (fair 2.058). Now every cell in your grid can be read against a real fair line instead of guessed at. To benchmark across many books at once, see <a href=\"https:\/\/oddspapi.io\/blog\/consensus-odds-fair-odds-calculator-python\/\">consensus odds<\/a>, and to measure each book&#8217;s margin, the <a href=\"https:\/\/oddspapi.io\/blog\/vig-calculator-python-sportsbook-margin\/\">vig calculator<\/a>.<\/p>\n<h3>One Function, Any Market<\/h3>\n<p>The grid builder is market-agnostic. To compare a soccer 1X2 board, pass <code>market_id=101<\/code> and <code>outcome_ids=[101, 102, 103]<\/code> (home, draw, away). For totals, pass the over\/under outcome pair. Look the IDs up per sport instead of hardcoding them:<\/p>\n<pre class=\"wp-block-code\"><code>catalog = api_get(\"markets\", sportId=13)\nmarket_names = {m[\"marketId\"]: m[\"marketName\"] for m in catalog}\noutcome_names = {(m[\"marketId\"], o[\"outcomeId\"]): o[\"outcomeName\"]\n                 for m in catalog for o in m.get(\"outcomes\", [])}\n# market_names[131] -> \"Winner (incl. extra innings)\"<\/code><\/pre>\n<h2>How OddsPapi Compares to the Comparison Sites<\/h2>\n<figure class=\"wp-block-table\">\n<table>\n<thead>\n<tr>\n<th>Service<\/th>\n<th>Type<\/th>\n<th>Public API?<\/th>\n<th>Books<\/th>\n<th>Historical<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Oddschecker<\/td>\n<td>Consumer screener<\/td>\n<td>No<\/td>\n<td>~40<\/td>\n<td>No<\/td>\n<\/tr>\n<tr>\n<td>Oddspedia<\/td>\n<td>Consumer screener<\/td>\n<td>No<\/td>\n<td>~40<\/td>\n<td>Limited<\/td>\n<\/tr>\n<tr>\n<td>jedibets<\/td>\n<td>Player-props screener + alerts<\/td>\n<td>No<\/td>\n<td>Props focus<\/td>\n<td>No<\/td>\n<\/tr>\n<tr>\n<td>OddsJam<\/td>\n<td>Tool + paid API<\/td>\n<td>Paid<\/td>\n<td>~70<\/td>\n<td>Paid<\/td>\n<\/tr>\n<tr>\n<td><strong>OddsPapi<\/strong><\/td>\n<td><strong>Developer API<\/strong><\/td>\n<td><strong>Yes, free tier<\/strong><\/td>\n<td><strong>372<\/strong><\/td>\n<td><strong>Free<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p>If you want to <em>read<\/em> a comparison, the consumer sites are fine. If you want to <em>build<\/em> one, or feed compared prices into a model, an alert bot, or your own front end, you need an API. That is the gap OddsPapi fills. Want a ready-made UI? See our <a href=\"https:\/\/oddspapi.io\/blog\/odds-comparison-dashboard-python-streamlit\/\">Streamlit odds comparison dashboard<\/a>, or weigh the providers in our <a href=\"https:\/\/oddspapi.io\/blog\/best-odds-apis-2026-comparison\/\">best odds APIs of 2026<\/a> rundown.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Is there an Oddschecker API?<\/h3>\n<p>No. Oddschecker does not publish a public developer API. To get the same multi-bookmaker comparison data programmatically, use an odds API like OddsPapi, which returns every book&#8217;s price for a fixture as JSON on a free tier.<\/p>\n<h3>What is an odds comparison API?<\/h3>\n<p>An odds comparison API returns the same bet&#8217;s price across many bookmakers in one call, so you can line them up, find the best price, and measure the spread. OddsPapi covers 372 bookmakers and 69 sports over a single REST endpoint.<\/p>\n<h3>How many bookmakers can I compare at once?<\/h3>\n<p>A single <code>\/odds<\/code> call returns every bookmaker pricing that fixture. In practice that is around 9 to 15 books on a typical fixture and more on marquee events, spanning sharps (Pinnacle), US sportsbooks, exchanges, and prediction markets (Kalshi, Polymarket).<\/p>\n<h3>Can I get historical odds for backtesting?<\/h3>\n<p>Yes. The <code>\/historical-odds<\/code> endpoint returns the full price history for a fixture (up to 3 bookmakers per call) on the free tier, where most comparison sites charge for or do not offer historical data.<\/p>\n<h3>Do I need to convert American or fractional odds?<\/h3>\n<p>No. Every price ships pre-converted: <code>price<\/code> is decimal, <code>priceAmerican<\/code> and <code>priceFractional<\/code> are strings in those formats. Pick the field you need.<\/p>\n<h2>Stop Scraping. Start Comparing.<\/h2>\n<p>The consumer comparison sites solved the display problem and locked the data away. OddsPapi hands you the data: 372 bookmakers, free historical odds, and a WebSocket feed when you need real-time, all on a free key. Build your own grid, your own alerts, your own edge. <strong><a href=\"https:\/\/oddspapi.io\">Grab your free API key<\/a><\/strong> and run the code above in five minutes.<\/p>\n<p><!--\nFocus Keyphrase: odds comparison api\nSEO Title: Odds Comparison API: Compare 372 Bookmakers in Python (Free)\nMeta Description: Build an odds comparison grid across 372 bookmakers in Python. Oddschecker has no API, OddsPapi does, free tier. Tested code, real prices.\nSlug: odds-comparison-api\n--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Build an odds comparison grid across 372 bookmakers in Python. Oddschecker has no public API, OddsPapi does, free tier. Tested code, real prices.<\/p>\n","protected":false},"author":2,"featured_media":3063,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[47,8,9,11,10],"class_list":["post-3062","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-how-to-guides","tag-comparison","tag-free-api","tag-odds-api","tag-python","tag-sports-betting-api"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Odds Comparison API: Compare 372 Bookmakers in Python (Free) | OddsPapi Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/oddspapi.io\/blog\/odds-comparison-api\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Odds Comparison API: Compare 372 Bookmakers in Python (Free) | OddsPapi Blog\" \/>\n<meta property=\"og:description\" content=\"Build an odds comparison grid across 372 bookmakers in Python. 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