{"id":4689,"date":"2026-10-13T10:00:00","date_gmt":"2026-10-13T10:00:00","guid":{"rendered":"https:\/\/oddspapi.io\/blog\/?p=4689"},"modified":"2026-10-02T13:28:28","modified_gmt":"2026-10-02T13:28:28","slug":"novig-api-odds-access","status":"publish","type":"post","link":"https:\/\/oddspapi.io\/blog\/novig-api-odds-access\/","title":{"rendered":"Novig API: Read Both Sides of a 191-Market NFL Board"},"content":{"rendered":"

Does Novig have an API? Yes. Novig publishes a trading API at docs.novig.com<\/a>: you create a management key in your Novig profile, open a subaccount, sign each REST request with Ed25519 or P-256, and follow the order book and your fills on a websocket. It is built to place orders on the Novig exchange.<\/p>\n

If you want to read Novig’s prices next to Pinnacle, Kalshi, ProphetX and 350+ other bookmakers, with closing lines for backtests, OddsPapi carries the book under one slug: novig.us<\/code>. It ships three levels of back and lay depth on every outcome. On 16 NFL Week 3 fixtures (24-29 September 2026), novig.us<\/code> closed the moneyline at a 0.51% median overround against Pinnacle’s 2.63%<\/strong>, and its no-vig price sat a median 0.67 points from Pinnacle’s. This guide shows you how to find the slug, read both sides of the order book, pull the player props and check the close yourself.<\/p>\n

What the Novig trading API covers, and what a price pipeline needs<\/h2>\n

A trading API answers one question well: what can I fill on this venue right now? A pricing model asks more. It wants the Novig price next to the sharp books, in one schema, plus every snapshot after the fixture is played. If you already read Kalshi or ProphetX, you know the cost: one auth scheme per venue, one market format per venue, and your own recorder for history.<\/p>\n

OddsPapi gives Novig the same JSON shape as every other book on the board. A Novig outcome looks like a Pinnacle outcome, plus an exchangeMeta<\/code> block that holds the ladder. Our NFL prediction market liquidity study<\/a> ranked seven peer-to-peer venues on depth. This post goes one level down: how to read the novig.us<\/code> block itself.<\/p>\n

\n\n\n\n\n\n\n\n\n\n
<\/th>\nNovig trading API<\/th>\nOddsPapi novig.us<\/code><\/th>\n<\/tr>\n<\/thead>\n
Built for<\/td>\nPlacing and managing orders on Novig<\/td>\nReading Novig prices next to other books<\/td>\n<\/tr>\n
Auth<\/td>\nManagement key from your Novig profile, signed requests (Ed25519 or P-256)<\/td>\nOne apiKey<\/code> query parameter<\/td>\n<\/tr>\n
Venues<\/td>\nNovig<\/td>\nNovig plus 350+ bookmakers and exchanges, one schema<\/td>\n<\/tr>\n
Depth<\/td>\nOrder book and fills on a websocket<\/td>\nThree back and three lay levels per outcome, REST and WebSocket<\/td>\n<\/tr>\n
Played fixtures<\/td>\nSee Novig’s docs<\/td>\n\/v4\/historical-odds<\/code> on the free tier, back to 20+ days before an NFL kickoff<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

Where novig.us prices today<\/h2>\n

We probed up to five upcoming fixtures with odds in each of 35 tournaments across 17 sports on 2 October 2026 at 13:08 UTC, with bookmakers=novig.us<\/code>. Novig priced six tournaments:<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n
Tournament<\/th>\nFixtures priced<\/th>\nMarkets per fixture<\/th>\nPlayer prop markets<\/th>\n<\/tr>\n<\/thead>\n
NFL<\/td>\n5 of 5<\/td>\n175 to 197 (median 191)<\/td>\n121 to 140<\/td>\n<\/tr>\n
NCAA football<\/td>\n5 of 5<\/td>\n55 to 77<\/td>\n0<\/td>\n<\/tr>\n
MLB (postseason)<\/td>\n4 of 5<\/td>\n22 to 45<\/td>\n3 to 18<\/td>\n<\/tr>\n
NHL<\/td>\n5 of 5<\/td>\n23 to 25<\/td>\n7 to 9<\/td>\n<\/tr>\n
WTA Beijing singles<\/td>\n5 of 5<\/td>\n13 to 14<\/td>\n0<\/td>\n<\/tr>\n
UFC 332<\/td>\n5 of 5<\/td>\n5 to 6<\/td>\n0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n

The fifth MLB fixture was the next day’s game in the same series, and Novig had not opened it yet. On the soccer, basketball, CFL, AHL, Japanese and Korean baseball, darts, rugby and esports fixtures we sampled, the slug returned no prices. For those, Kalshi, polymarket.us<\/code> and Pinnacle cover most of the gap. NBA preseason had no fixtures in the window, so we did not test it.<\/p>\n

Step 1: find the Novig slug<\/h2>\n

Authentication is one query parameter, apiKey<\/code>. The helper waits out a 429 for as long as the error body says, backs off on a 5xx, and returns None<\/code> on a 404.<\/p>\n

import os, time, requests\nfrom datetime import datetime, timedelta, timezone\nfrom statistics import median\n\nAPI_KEY = os.environ["ODDSPAPI_API_KEY"]\nBASE = "https:\/\/api.oddspapi.io\/v4"\n\ndef get(path, **params):\n    params["apiKey"] = API_KEY\n    for _ in range(5):\n        r = requests.get(f"{BASE}\/{path}", params=params, timeout=180)\n        if r.status_code == 429:                      # rate limited: wait as told\n            time.sleep(r.json().get("error", {}).get("retryMs", 2000) \/ 1000 + 0.5)\n            continue\n        if r.status_code >= 500:                      # transient: back off and retry\n            time.sleep(2)\n            continue\n        if r.status_code == 404:                      # empty window\n            return None\n        r.raise_for_status()\n        return r.json()\n    raise RuntimeError(f"gave up on {path}")\n\nbooks = get("bookmakers")\nnovig = next(b for b in books if b["slug"] == "novig.us")\nprint(novig)\nprint(f"{len(books)} bookmakers in the catalogue")<\/code><\/pre>\n
{'bookmakerName': 'Novig US', 'slug': 'novig.us', 'liveOdds': True, 'cloneOf': None}\n363 bookmakers in the catalogue<\/code><\/pre>\n
One slug, liveOdds: True<\/code>, no clone. Pass it alone in bookmakers=<\/code> when you probe coverage. A 200 with the slug missing means Novig has not priced that fixture; a 403 would mean your key cannot read the book.<\/p>\n
Step 2: count Novig’s markets per fixture<\/h2>\n
This step pulls the next two fixtures in four tournaments and counts Novig’s markets. It reads the global \/v4\/markets<\/code> catalogue once to flag player props.<\/p>\n
# Reuses get() and the imports from Step 1.\nMARKETS = {m["marketId"]: m for m in get("markets")}       # name lookup for every market ID\n\nnow = datetime.now(timezone.utc)\nwindow = {"from": now.strftime("%Y-%m-%d"), "to": (now + timedelta(days=4)).strftime("%Y-%m-%d")}\nTOURNAMENTS = {31: "NFL", 27653: "NCAAF", 109: "MLB", 234: "NHL"}\n\nfor tid, name in TOURNAMENTS.items():\n    fixtures = get("fixtures", tournamentId=tid, **window) or []\n    upcoming = sorted((f for f in fixtures if f["hasOdds"] and f["startTime"] > now.isoformat()),\n                      key=lambda f: f["startTime"])[:2]\n    for fx in upcoming:\n        data = get("odds", fixtureId=fx["fixtureId"], bookmakers="novig.us")\n        book = data["bookmakerOdds"].get("novig.us")\n        time.sleep(1.0)\n        if not book:\n            print(f"{name:<6} {fx['participant1Name'][:22]:<22} not priced yet")\n            continue\n        props = sum(MARKETS.get(int(mid), {}).get("playerProp", False) for mid in book["markets"])\n        print(f"{name:<6} {fx['participant1Name'][:22]:<22} v {fx['participant2Name'][:22]:<22} "\n              f"{len(book['markets']):>3} markets, {props:>3} player props")<\/code><\/pre>\n
NFL    Washington Commanders  v Indianapolis Colts     193 markets, 136 player props\nNFL    Baltimore Ravens       v Tennessee Titans       197 markets, 140 player props\nNCAAF  Delaware Blue Hens     v Liberty Flames          72 markets,   0 player props\nNCAAF  Virginia Tech Hokies   v Pittsburgh Panthers     77 markets,   0 player props\nMLB    Cleveland Guardians    v Chicago White Sox       43 markets,  18 player props\nMLB    Los Angeles Dodgers    v Atlanta Braves          22 markets,   3 player props\nNHL    Detroit Red Wings      v New York Rangers        24 markets,   7 player props\nNHL    Carolina Hurricanes    v Washington Capitals     25 markets,   9 player props<\/code><\/pre>\n
The NFL board is the deep one. On the five Week 4 Sunday fixtures we pulled, Novig listed 26 to 31 players with props each, across receiving yards, rushing yards, passing yards, receptions, touchdowns and first touchdown scorer. College football carries spreads, totals, team totals and first-half lines, with no props. The NHL board adds shots on goal, points, assists, goals and saves.<\/p>\n
Step 3: read both sides of the Novig order book<\/h2>\n
Every Novig price ships an exchangeMeta<\/code> block with up to three back<\/code> levels and three lay<\/code> levels. Each level carries four numbers: price<\/code> (decimal odds), cents<\/code> (the contract price, 0 to 1), size<\/code> (contracts) and limit<\/code> (the stake at that level). This step resolves the moneyline by name, then prints the ladder for both outcomes.<\/p>\n
# Reuses get() and the imports from Step 1.\nMARKETS = {m["marketId"]: m for m in get("markets")}\nnow = datetime.now(timezone.utc)\nfixtures = get("fixtures", tournamentId=31,                   # NFL\n               **{"from": now.strftime("%Y-%m-%d"), "to": (now + timedelta(days=4)).strftime("%Y-%m-%d")})\nfx = min((f for f in fixtures if f["startTime"] > now.isoformat()), key=lambda f: f["startTime"])\nbook = get("odds", fixtureId=fx["fixtureId"], bookmakers="novig.us")["bookmakerOdds"]["novig.us"]\n\n# Resolve the moneyline by name, never by a hardcoded ID\nmid = next(k for k in book["markets"]\n           if MARKETS[int(k)]["marketName"].startswith("Winner") and MARKETS[int(k)]["period"] == "result")\nnames = {o["outcomeId"]: o["outcomeName"] for o in MARKETS[int(mid)]["outcomes"]}\nlegs = {names[int(oid)]: o["players"]["0"] for oid, o in book["markets"][mid]["outcomes"].items()}\nlegs = dict(sorted(legs.items()))                            # "1" first, then "2"\nteam = {"1": fx["participant1Name"], "2": fx["participant2Name"]}\nprint(f"{fx['participant1Name']} v {fx['participant2Name']}  market {mid}\\n")\n\nfor name, p in legs.items():\n    print(f"Outcome {name} ({team[name]})")\n    for side in ("back", "lay"):\n        for lvl in p["exchangeMeta"][side]:\n            ok = abs(lvl["limit"] - lvl["size"] * lvl["cents"]) < 0.02\n            print(f"  {side:<4} {lvl['price']:>6.3f}  cents {lvl['cents']:.3f}  size {lvl['size']:>9.2f}  "\n                  f"limit {lvl['limit']:>8.2f}  limit == size x cents: {ok}")\n\na, b = legs.values()\nmirror = all(abs(x["limit"] - y["limit"]) <= 0.02 and x["price"] == y["price"]\n             for x, y in zip(a["exchangeMeta"]["lay"], b["exchangeMeta"]["back"]))\nover = 1 \/ a["price"] + 1 \/ b["price"] - 1\nprint(f"\\nOutcome 1 lay ladder == outcome 2 back ladder: {mirror}")\nprint(f"Best-back overround: {over:.2%}   top-of-book stake: {min(a['limit'], b['limit']):.2f}")<\/code><\/pre>\n
Washington Commanders v Indianapolis Colts  market 141\n\nOutcome 1 (Washington Commanders)\n  back  2.778  cents 0.360  size  67211.32  limit 24196.08  limit == size x cents: True\n  back  2.740  cents 0.365  size  14071.56  limit  5136.12  limit == size x cents: True\n  back  2.703  cents 0.370  size  47064.12  limit 17413.72  limit == size x cents: True\n  lay   1.550  cents 0.645  size    351.63  limit   226.80  limit == size x cents: True\n  lay   1.538  cents 0.650  size  19536.17  limit 12698.51  limit == size x cents: True\n  lay   1.527  cents 0.655  size  16487.45  limit 10799.28  limit == size x cents: True\nOutcome 2 (Indianapolis Colts)\n  back  1.550  cents 0.645  size    351.63  limit   226.80  limit == size x cents: True\n  back  1.538  cents 0.650  size  19536.17  limit 12698.51  limit == size x cents: True\n  back  1.527  cents 0.655  size  16487.45  limit 10799.28  limit == size x cents: True\n  lay   2.778  cents 0.360  size  67211.32  limit 24196.08  limit == size x cents: True\n  lay   2.740  cents 0.365  size  14071.56  limit  5136.12  limit == size x cents: True\n  lay   2.703  cents 0.370  size  47064.12  limit 17413.72  limit == size x cents: True\n\nOutcome 1 lay ladder == outcome 2 back ladder: True\nBest-back overround: 0.51%   top-of-book stake: 226.80<\/code><\/pre>\n
Read three things off that output.<\/p>\n