Myriad Markets is the onchain prediction market you probably haven’t heard of yet. It’s live, it’s onchain, it trades binary “yes\/no” markets across politics, crypto, sports and culture \u2014 and because it’s a decentralized exchange, there’s no KYC, no jurisdiction check, no “sorry, you can’t trade from your country.” From a market-data perspective, that’s exactly the kind of venue traders care about: unfiltered, uncensored, and priced by whoever shows up with capital.<\/p>\n
So if you want to pull Myriad’s data and fold it into a trading model, what does that look like?<\/p>\n For read-only access \u2014 the 90% use case \u2014 you don’t want to maintain any of that. You want the price.<\/p>\n OddsPapi aggregates 350+ venues into one API. Myriad Markets is one of them. You send one request with Myriad Markets is an onchain prediction market \u2014 binary outcomes, share prices that read as implied probability, settlement via smart contract. When a trader buys a “yes” share at 0.44, the market is collectively pricing the event at a 44% probability. The opposite side \u2014 the “no” share \u2014 trades at roughly Unlike sportsbooks, Myriad has no house. There’s no vig extracted from both sides. The only cost of trading is the spread between yes and no prices plus any onchain fees. For modelers, this is useful: the mid-market on a Myriad binary is roughly the market’s true implied probability<\/em>, not a vig-loaded sportsbook number you have to de-juice yourself.<\/p>\n Take any sportsbook moneyline, strip the vig, and you get an implied probability. Take a Myriad market on the same event and you already have one. If they disagree by more than the sportsbook’s bid\/ask spread, you have a potential edge \u2014 one side is mispriced relative to the other.<\/p>\n This is the core trade in the prediction-market-versus-sportsbook arbitrage playbook. You can’t run it without reliable, low-friction access to Myriad’s price data.<\/p>\n Myriad routes through the same Two rules to remember:<\/p>\n Coverage note:<\/strong> Myriad’s sports coverage is deep on headline events and thin on the long tail. Niche fixtures will often return no Myriad book \u2014 that’s normal for onchain markets. Political, crypto and culture markets tend to have the deepest liquidity, but those live outside OddsPapi’s sports-fixture schema.<\/p>\n<\/blockquote>\n That spread number is the overround \u2014 how much the market is charging you to round-trip. On a healthy onchain binary it should be sub-2%. On a thin market it can be 10%+. Use it as a liquidity signal before trusting any individual line.<\/p>\n The real use case: grab Myriad and a sharp sportsbook in the same request, strip Pinnacle’s vig, compare implied probabilities.<\/p>\n You’ll still need to de-juice Pinnacle (divide each implied probability by their sum, which will exceed 1 thanks to the vig) before you have a fair comparison. Once you do, the gap between Pinnacle’s no-vig number and Myriad’s raw implied probability is your signal.<\/p>\n No onchain glue code<\/strong> \u2014 Reading Myriad directly means running an RPC client, parsing conditional token events, and maintaining a metadata cache. OddsPapi turns all of that into one HTTP GET.<\/p>\n Already decimal<\/strong> \u2014 Myriad’s native share price is converted to decimal odds at ingestion. Implied probability is a one-line calculation.<\/p>\n Cross-venue in one call<\/strong> \u2014 Myriad + Polymarket + Limitless + Kalshi + Pinnacle, side by side. That’s the entire “sportsbook vs prediction market” comparison stack in a single JSON response.<\/p>\n Free historical data<\/strong> \u2014 \n\n
\n \nRequirement<\/th>\n Reality<\/th>\n<\/tr>\n<\/thead>\n \n Market Resolution<\/strong><\/td>\n Onchain condition IDs, not human names<\/td>\n<\/tr>\n \n Price Format<\/strong><\/td>\n Native share price 0\u20131, not decimal odds<\/td>\n<\/tr>\n \n Metadata<\/strong><\/td>\n Separate off-chain service for names, descriptions, end dates<\/td>\n<\/tr>\n \n Trading<\/strong><\/td>\n Signed orders, onchain settlement<\/td>\n<\/tr>\n \n Cross-venue Comparison<\/strong><\/td>\n Not offered \u2014 bring your own data<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n The “Third Option” for Myriad Markets Data<\/h2>\n
?bookmakers=myriad<\/code> and get decimal-formatted prices for every outcome OddsPapi has indexed, no wallet, no share-price conversion, no metadata-service glue code.<\/p>\n\n\n
\n \nFeature<\/th>\n Myriad Direct<\/th>\n OddsPapi<\/th>\n<\/tr>\n<\/thead>\n \n Auth<\/strong><\/td>\n Wallet + signed reads (depending on deployment)<\/td>\n Single API key<\/td>\n<\/tr>\n \n Odds Format<\/strong><\/td>\n Native share price 0\u20131<\/td>\n Decimal, ready to use<\/td>\n<\/tr>\n \n Metadata<\/strong><\/td>\n Separate off-chain endpoint<\/td>\n Same response as prices<\/td>\n<\/tr>\n \n Compare vs Sportsbooks<\/strong><\/td>\n Impossible from one source<\/td>\n One request, 350+ venues<\/td>\n<\/tr>\n \n Historical Data<\/strong><\/td>\n Query onchain event logs yourself<\/td>\n Free on entry tier<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n What Myriad Actually Is<\/h2>\n
1 - yes_price<\/code>, minus whatever bid\/ask spread the market makers are running.<\/p>\nWhy This Data Matters<\/h3>\n
The JSON Shape<\/h2>\n
\/v4\/odds<\/code> endpoint as every other bookmaker. The outcome structure mirrors OddsPapi’s exchange-type shape (same as Polymarket, Limitless, Kalshi):<\/p>\n# Myriad exchange-style outcome\n{\n \"active\": true,\n \"betslip\": \"https:\/\/myriad.markets\/market\/0x...\",\n \"bookmakerOutcomeId\": \"0x...\", # onchain market identifier\n \"changedAt\": \"2026-04-11T09:38:17+00:00\",\n \"limit\": null,\n \"price\": 2.27, # decimal odds (yes side)\n \"exchangeMeta\": {\n \"lay\": 1.79, # no side in decimal\n \"bookmakerLayOutcomeId\": \"0x...\"\n }\n}<\/code><\/pre>\n\n
price<\/code><\/strong> is the “yes” side in decimal. For implied probability, compute 1 \/ price<\/code>. For the 2.27 above, that’s ~44%.<\/li>\nexchangeMeta.lay<\/code><\/strong> is the “no” side in decimal. Yes + no shouldn’t add to exactly 1 in probability terms \u2014 the gap is the market’s spread, which is your cost to round-trip.<\/li>\n<\/ul>\n\n
Tutorial: Pull Myriad Markets Data in Python<\/h2>\n
Step 1: Auth<\/h3>\n
import requests\n\nAPI_KEY = \"YOUR_API_KEY\"\nBASE_URL = \"https:\/\/api.oddspapi.io\/v4\"<\/code><\/pre>\nStep 2: Find a Headline Fixture<\/h3>\n
from datetime import datetime, timezone, timedelta\n\ntoday = datetime.now(timezone.utc)\nparams = {\n \"apiKey\": API_KEY,\n \"sportId\": 11, # NBA \u2014 highest onchain liquidity\n \"from\": today.strftime(\"%Y-%m-%d\"),\n \"to\": (today + timedelta(days=2)).strftime(\"%Y-%m-%d\"),\n}\nfixtures = requests.get(f\"{BASE_URL}\/fixtures\", params=params).json()\ncandidates = [f for f in fixtures if f.get(\"hasOdds\")]<\/code><\/pre>\nStep 3: Query Myriad<\/h3>\n
fixture_id = candidates[0][\"fixtureId\"]\n\nr = requests.get(\n f\"{BASE_URL}\/odds\",\n params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id,\n \"bookmakers\": \"myriad\",\n },\n)\ndata = r.json()\n\nmyriad = data.get(\"bookmakerOdds\", {}).get(\"myriad\")\nif not myriad:\n print(\"No Myriad market on this fixture \u2014 try a headline game.\")\nelse:\n print(f\"{len(myriad['markets'])} markets from Myriad\")<\/code><\/pre>\nStep 4: Parse Yes\/No, Compute Implied Probability<\/h3>\n
market = myriad[\"markets\"][\"101\"] # Full Time Result \/ Moneyline\n\nfor outcome_id, outcome in market[\"outcomes\"].items():\n p = outcome[\"players\"][\"0\"]\n if not p[\"active\"]:\n continue\n\n yes_decimal = p[\"price\"]\n yes_prob = 1 \/ yes_decimal\n\n meta = p.get(\"exchangeMeta\") or {}\n no_decimal = meta.get(\"lay\") if isinstance(meta, dict) else None\n no_prob = 1 \/ no_decimal if no_decimal else None\n\n spread = (yes_prob + no_prob - 1) if no_prob else None\n print(f\"outcome {outcome_id}: yes {yes_prob:.1%} | no {no_prob and f'{no_prob:.1%}'} | spread {spread and f'{spread:.2%}'}\")<\/code><\/pre>\nStep 5: Cross-Check Against Pinnacle<\/h3>\n
r = requests.get(\n f\"{BASE_URL}\/odds\",\n params={\n \"apiKey\": API_KEY,\n \"fixtureId\": fixture_id,\n \"bookmakers\": \"myriad,pinnacle\",\n },\n)\nbooks = r.json()[\"bookmakerOdds\"]\n\ndef implied(x):\n return 1 \/ x if x else None\n\npinny = books.get(\"pinnacle\", {}).get(\"markets\", {}).get(\"101\", {}).get(\"outcomes\", {})\nmyr = books.get(\"myriad\", {}).get(\"markets\", {}).get(\"101\", {}).get(\"outcomes\", {})\n\nfor oid in (\"101\", \"102\", \"103\"):\n pin_price = pinny.get(oid, {}).get(\"players\", {}).get(\"0\", {}).get(\"price\")\n myr_price = myr.get(oid, {}).get(\"players\", {}).get(\"0\", {}).get(\"price\")\n print(f\"outcome {oid}: pinnacle {implied(pin_price)} vs myriad {implied(myr_price)}\")<\/code><\/pre>\nWhy Devs Pick OddsPapi Over a Direct Myriad Integration<\/h2>\n
\/v4\/historical-odds?fixtureId=\u2026&bookmakers=myriad<\/code> gives you price history without querying event logs yourself.<\/p>\nGet Started<\/h2>\n