{"id":3108,"date":"2026-07-14T10:00:00","date_gmt":"2026-07-14T10:00:00","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=3108"},"modified":"2026-06-21T18:18:25","modified_gmt":"2026-06-21T18:18:25","slug":"circa-sports-api-odds-access","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/circa-sports-api-odds-access\/","title":{"rendered":"Circa Sports API: How to Access Circa Odds Without an Official API"},"content":{"rendered":"

Looking for a Circa Sports API? There Isn’t One<\/h2>\n

Circa Sports is the sharpest book in Las Vegas. It opened the Circa Resort in 2020 on a simple promise: post early, take the biggest limits in the state, and let sharp money set the line. Professionals bet Circa because it doesn’t ban winners the way the soft books do. That makes Circa’s prices some of the most informative in the US market.<\/p>\n

So naturally, developers want to pull Circa odds programmatically. The problem: Circa has no public API.<\/strong> No developer portal, no documentation, no API key. If you want Circa’s lines in JSON, you are out of luck through official channels.<\/p>\n

This guide shows you how to get Circa Sports odds anyway, in Python, alongside Pinnacle and 350+ other bookmakers<\/strong> through a single OddsPapi call. We’ll pull a live World Cup line, de-vig it, and show how Circa stacks up against Pinnacle, the only other book in its sharpness class.<\/p>\n

Why Scraping Circa Yourself Is a Bad Idea<\/h2>\n

The DIY route is to scrape circasports.com or reverse-engineer their mobile app. Both are fragile and adversarial:<\/p>\n

\n\n\n\n\n\n\n\n\n\n
The Old Way (Scraping Circa)<\/th>\nThe OddsPapi Way<\/th>\n<\/tr>\n<\/thead>\n
Geo-fenced to NV\/CO\/IA \u2014 needs a presence in-state<\/td>\nOne HTTPS call from anywhere<\/td>\n<\/tr>\n
Markup changes break your parser weekly<\/td>\nStable JSON schema<\/td>\n<\/tr>\n
One book in isolation, no context<\/td>\nCirca next to Pinnacle + 350 books in the same payload<\/td>\n<\/tr>\n
Rate-limited \/ blocked on detection<\/td>\nClean API key, documented limits<\/td>\n<\/tr>\n
You build the odds converter<\/td>\nDecimal, American & fractional pre-computed<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

OddsPapi already aggregates Circa under the slug circasports<\/code>. You query the fixture, you get Circa’s price in the response. No state residency, no headless browser, no cat-and-mouse.<\/p>\n

The Tutorial: Pull Circa Odds in Python<\/h2>\n

Step 1 \u2014 Authenticate<\/h3>\n

OddsPapi auth is a query parameter, never a header. One key works on every endpoint.<\/p>\n

import requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE = \"https:\/\/api.oddspapi.io\/v4\"\n\nr = requests.get(f\"{BASE}\/sports\", params={\"apiKey\": API_KEY})\nprint(r.status_code)  # 200<\/code><\/pre>\n
Step 2 \u2014 Find a Fixture Circa Prices<\/h3>\n
Circa is a US-regulated mainline book. It prices the events its sharp customers bet most: the big soccer tournaments, MLB, NFL, NBA and futures. Pull fixtures for a sport and date range, then keep the ones that carry odds.<\/p>\n
def fixtures(sport_id, frm, to):\n    r = requests.get(f\"{BASE}\/fixtures\", params={\n        \"apiKey\": API_KEY, \"sportId\": sport_id, \"from\": frm, \"to\": to,\n    })\n    r.raise_for_status()\n    return [f for f in r.json() if f.get(\"hasOdds\")]\n\n# Soccer = sportId 10. World Cup window:\nfx = fixtures(10, \"2026-06-22\", \"2026-06-24\")\nprint(len(fx), \"fixtures with odds\")<\/code><\/pre>\n
Step 3 \u2014 Read Circa’s Price From the Nested JSON<\/h3>\n
The \/odds<\/code> response is deeply nested: bookmakerOdds \u2192 slug \u2192 markets \u2192 marketId \u2192 outcomes \u2192 outcomeId \u2192 players[\"0\"]<\/code>. For Circa, the slug is circasports<\/code>. Here is a defensive parser for the Full Time Result market (1X2, market 101<\/code>; outcomes 101<\/code> home, 102<\/code> draw, 103<\/code> away):<\/p>\n
def market_prices(fixture_id, slug, market_id=\"101\"):\n    o = requests.get(f\"{BASE}\/odds\", params={\n        \"apiKey\": API_KEY, \"fixtureId\": fixture_id,\n    }).json()\n    book = o.get(\"bookmakerOdds\", {}).get(slug)\n    if not book:\n        return None  # this book doesn't price this fixture\n    market = book[\"markets\"].get(market_id)\n    if not market:\n        return None\n    out = {}\n    for oid, od in market[\"outcomes\"].items():\n        node = od.get(\"players\", {}).get(\"0\")\n        if node and node.get(\"active\") and node.get(\"price\", 0) > 0:\n            out[oid] = node[\"price\"]\n    return out\n\n# Argentina v Austria, World Cup\nfid = \"id1000001666457022\"\ncirca = market_prices(fid, \"circasports\")\nprint(circa)  # {'101': 1.571, '102': 4.03, '103': 6.33}<\/code><\/pre>\n
That is Circa’s real line, captured live: Argentina 1.571, Draw 4.03, Austria 6.33<\/strong>.<\/p>\n
Step 4 \u2014 De-Vig and Compare Circa to Pinnacle<\/h3>\n
A raw price includes the book’s margin (the vig). To read Circa’s true opinion, strip the vig by normalising the implied probabilities so they sum to 100%. Then do the same for Pinnacle and compare the two sharpest books in the world:<\/p>\n
def devig(prices):\n    inv = {k: 1 \/ v for k, v in prices.items()}\n    overround = sum(inv.values())\n    fair = {k: inv[k] \/ overround for k in prices}      # true probability\n    fair_odds = {k: 1 \/ p for k, p in fair.items()}      # fair decimal odds\n    return (overround - 1) * 100, fair, fair_odds\n\npinnacle = market_prices(fid, \"pinnacle\")\nfor slug, p in [(\"Circa\", circa), (\"Pinnacle\", pinnacle)]:\n    vig, fair, fair_odds = devig(p)\n    print(f\"{slug:9s} vig {vig:.2f}%  \"\n          f\"Arg {fair['101']*100:.1f}%  Draw {fair['102']*100:.1f}%  \"\n          f\"Aut {fair['103']*100:.1f}%\")<\/code><\/pre>\n
Output, captured live for Argentina v Austria:<\/p>\n
\n\n\n\n\n\n\n
Book<\/th>\nVig<\/th>\nArgentina (fair)<\/th>\nDraw (fair)<\/th>\nAustria (fair)<\/th>\n<\/tr>\n<\/thead>\n
Circa<\/td>\n4.27%<\/td>\n61.0% (1.64)<\/td>\n23.8% (4.20)<\/td>\n15.2% (6.60)<\/td>\n<\/tr>\n
Pinnacle<\/td>\n3.81%<\/td>\n62.7% (1.60)<\/td>\n23.4% (4.28)<\/td>\n13.9% (7.17)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
This is the whole point of pulling Circa. The two sharpest books on earth land within roughly 1.7 points on every outcome, and both run a tighter margin than the soft books. When Circa and Pinnacle agree, you have a high-confidence read on the true probability. When they diverge, that gap is itself a signal worth investigating.<\/p>\n
Step 5 \u2014 Line-Shop Circa Against the Full Board<\/h3>\n
Circa is sharp, but sharp does not mean “best price to bet into.” Widen the same parser across every book in the payload to find the best available number on each outcome:<\/p>\n
def best_prices(fixture_id, market_id=\"101\"):\n    o = requests.get(f\"{BASE}\/odds\", params={\n        \"apiKey\": API_KEY, \"fixtureId\": fixture_id,\n    }).json()\n    books = o.get(\"bookmakerOdds\", {})\n    best = {}\n    for slug, data in books.items():\n        m = data.get(\"markets\", {}).get(market_id)\n        if not m:\n            continue\n        for oid, od in m[\"outcomes\"].items():\n            node = od.get(\"players\", {}).get(\"0\")\n            if node and node.get(\"active\") and node.get(\"price\", 0) > 0:\n                if oid not in best or node[\"price\"] > best[oid][1]:\n                    best[oid] = (slug, node[\"price\"])\n    return best\n\nprint(best_prices(fid))\n# {'101': ('fourwinds', 1.637), '102': ('polymarket', 4.348), '103': ('betparx', 7.5)}<\/code><\/pre>\n
Across the 18 books pricing this fixture, the best Argentina price was 1.637 (Four Winds), the best Draw 4.348 (Polymarket), the best Austria 7.5 (BetParX) \u2014 all above Circa’s and Pinnacle’s numbers. That is the line-shopping lesson: use the sharps to find the true<\/em> price, then use the full board to find the best<\/em> price. (These are best-available numbers, not a guaranteed edge \u2014 always sanity-check outliers against the sharp consensus.)<\/p>\n
What Circa Actually Prices<\/h2>\n
Be realistic about Circa’s coverage. Circa is a mainline and futures book, not a prop factory. On the Argentina fixture, Circa priced three market families \u2014 Full Time Result, Over\/Under, and Asian Handicap<\/strong> \u2014 while Pinnacle carried 91 markets including corners, bookings, team totals and inning-by-inning lines. On MLB, Circa carries the moneyline, run line, totals and first-five-innings markets, plus a handful of strikeout and home-run props, typically 7 to 9 markets per game.<\/p>\n
\n\n\n\n\n\n\n\n
Sport<\/th>\nsportId<\/th>\nCirca markets<\/th>\nIn season?<\/th>\n<\/tr>\n<\/thead>\n
Soccer (World Cup)<\/td>\n10<\/td>\n1X2, Over\/Under, Asian Handicap<\/td>\nYes (summer 2026)<\/td>\n<\/tr>\n
MLB<\/td>\n13<\/td>\nMoneyline, run line, totals, F5, select props<\/td>\nYes<\/td>\n<\/tr>\n
NFL \/ NBA<\/td>\n14 \/ 11<\/td>\nSpreads, totals, moneylines, futures<\/td>\nSeasonal<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
If you need player props at depth, lean on FanDuel, DraftKings or Pinnacle in the same payload. If you want a sharp US benchmark for the mainline, Circa is exactly the book you want.<\/p>\n
Honest Caveats<\/h2>\n