Multi-Book Sports Betting Odds

Export public NFL, NBA, MLB and NHL odds with this sports betting odds API. Each result is one whole game containing the available sportsbook prices, equivalent-line comparisons, no-vig probabilities and price observations you have collected across runs.

Use it for comparison dashboards, sports data research and your own odds-monitoring integrations. No sportsbook login or paid odds-provider key is required. An Apify account is needed to run the Actor.

Quick start

1. Start with one league and a small game limit.
2. Run the Actor and open its dataset.
3. Export JSON to retain the nested books, markets and comparison groups. CSV and Excel exports are also available.

{
  "leagues": ["NFL"],
  "markets": ["moneyline", "spread", "total"],
  "maxEvents": 1,
  "includeLive": false
}

The limit applies to whole games, not individual bookmaker quotes. Prices and available games depend on the source at the time of the run. An off-season league can return no games.

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

What you get

One whole-game row with league, teams, kickoff and observation times, available requested book quotes and same-line comparison groups. Nested markets include American and decimal prices, no-vig probabilities and your own recorded quote observations. Unmatched game-state metadata stays null, and unavailable books or markets are not invented.

Who is it for?

• Sports-data researchers inspecting public full-game odds snapshots.
• Comparison-dashboard developers keeping equivalent line and outcome groups together.
• Quantitative analysts reviewing observed prices and no-vig calculations with their source times.
• Data integration teams retaining sequential caller-owned observations for their own reporting.

How you can use the data

• Inspect one NFL game across available markets. Use the existing one-game input with moneyline, spread and total selected and live inclusion off. Keep sourceUrl and observedAt beside quotes rather than treating them as an executable live feed.
• Compare equivalent book prices. Use the existing NFL/NBA moneyline example restricted to DraftKings and FanDuel with a small shared game cap. Compare only groups with the same market, line and outcome set, keeping missing books absent rather than substituting another price.
• Read probabilities in their source context. Inspect per-book no-vig probabilities and mathematical comparison indicators with collection times. These are calculations on displayed values, not a guarantee of matching settlement rules, available wagers or returns.
• Retain your own sequential price observations. Reuse the existing named NFL observation store across separate sequential runs. Review baseline, changed and unchanged states with actual prior observations. The first stored observation is not the bookmaker's opening price, and a new unnamed run does not inherit history.

Input settings

Setting	JSON key	Default	Meaning
Leagues	leagues	["NFL"]	Nonempty subset of NFL, NBA, MLB and NHL.
Markets	markets	All three	Nonempty subset of moneyline, spread and total.
Sportsbooks	bookmakers	All supported columns	Restrict results to supported book identifiers. Unavailable books remain absent.
Include live games	includeLive	true	Include games explicitly marked live. Games with unknown status remain explicitly unknown.
Game limit	maxEvents	100	Maximum whole-game records, from 1 to 1000. The form starts at 20. The quick start requests 1.
History store	historyStoreName	Omitted	Optional caller-owned store name: 1–63 alphanumeric or hyphen characters. Reuse it across sequential runs.

Games are returned by kickoff time, earliest first, with the game key breaking ties. Completed games are excluded when their status is known. There is no user-selectable sort order.

For a small two-league comparison:

{
  "leagues": ["NFL", "NBA"],
  "markets": ["moneyline"],
  "bookmakers": ["draftkings", "fanduel"],
  "maxEvents": 10,
  "includeLive": false
}

For repeated observations of a small slate:

{
  "leagues": ["NFL"],
  "maxEvents": 5,
  "includeLive": false,
  "historyStoreName": "my-nfl-odds-observations"
}

Output data

This is an excerpt from a previously observed game record. The full result also contains nested market quotes and comparison groups. Those arrays are omitted here for readability. It is an example of the returned shape, not a current price or game recommendation.

{
  "league": "NFL",
  "homeTeam": "Broncos",
  "awayTeam": "Seahawks",
  "startTime": "2026-10-16T00:15:00.000Z",
  "observedAt": "2026-10-14T17:40:45.087Z",
  "status": "scheduled",
  "isLive": false,
  "scores": null,
  "bookCount": 8
}

Field	What it contains
eventKey, sourceEventId	Stable game key and source identifier.
league, homeTeam, awayTeam	League and participating teams.
startTime, observedAt	UTC kickoff and collection timestamps.
sourceUrl	Public page from which the game prices were collected.
status, isLive, scores	Public game-state metadata when a matching scoreboard entry is available. Otherwise null.
books, bookCount	Sorted identifiers and count of books that actually contributed quotes.
markets	Available requested moneyline, spread and total quotes, with American and decimal odds and observation history. Unrequested or unavailable markets are omitted. Each delivered game has at least one market.
comparisons	Groups with the same market, line and outcome set, including best prices, no-vig probabilities and mathematical arbitrage indicators.

An arbitrage indicator describes the displayed numbers. It does not guarantee executable returns, available wagers or identical settlement rules. Public prices can be delayed, suspended or region-dependent.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

The game-record event is charged once per delivered whole-game record, including every requested available book and market in that row. There is no separate per-quote or comparison charge and no configured start event. Empty results and diagnostic summaries do not create game-record charges.

Use a small game cap and the Apify spending limit. See the live Pricing tab for current rates and Store discounts.

Tips for best results

• Start with one league and one game. The cap counts whole games rather than bookmaker quotes.
• Compare identical lines and outcomes. Different spread or total values remain separate groups.
• Keep observation times with displayed prices. Public data can be delayed or suspended.
• Use JSON for nested market data. Choose explicit fields when transforming it for a spreadsheet.
• Use one writer per named history store. Sequential runs can retain prior observations without claiming unseen opening prices.

Limits and caveats

Only public full-game NFL, NBA, MLB and NHL prices are supported. No player props, partial-game periods, credential-gated feeds, sportsbook accounts or wager placement are provided. No sportsbook login or paid provider key is required, but running the Actor requires Apify access.

Games are ordered by kickoff, earliest first, with a game-key tie break and no selectable sort. Known completed games are excluded. Live inclusion follows explicit source status, while unknown status stays unknown rather than being inferred from kickoff. Scoreboard metadata requires matching teams and kickoff, and a successful scoreboard request does not guarantee each game's state.

Requested books and markets can be unavailable by league, region or suspension state. Each delivered game has at least one market and bookCount counts actual contributors. Prices can be delayed and mathematical arbitrage indicators do not guarantee executable returns, wager availability or identical settlement rules.

SUMMARY records league-source failures, scoreboard request coverage and delivered counts. One unavailable league can be omitted while others return useful records. All league sources failing makes the run fail, while a valid empty slate or no usable requested quotes can succeed empty. Off-season leagues can expose no games.

Named history stores retain observations seen within thirty days, up to sixty thousand quotes. Concurrent writers to the same store are unsupported. History advances after dataset delivery, so a history-write failure can fail a run whose delivered dataset remains available. History cannot backfill unseen prices. Keep your own snapshot comparisons and reporting context alongside the existing detailed history guidance below.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST 'https://api.apify.com/v2/acts/fetch_cat~multi-book-sports-betting-odds/runs' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data @- <<'JSON'
{
  "leagues": [
    "NFL"
  ],
  "markets": [
    "moneyline",
    "spread",
    "total"
  ],
  "maxEvents": 1,
  "includeLive": false
}
JSON

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/multi-book-sports-betting-odds').call({
  "leagues": [
    "NFL"
  ],
  "markets": [
    "moneyline",
    "spread",
    "total"
  ],
  "maxEvents": 1,
  "includeLive": false
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/multi-book-sports-betting-odds').call(run_input={'leagues': ['NFL'], 'markets': ['moneyline', 'spread', 'total'], 'maxEvents': 1, 'includeLive': False})
print(client.dataset(run['defaultDatasetId']).list_items().items)

MCP and AI agents

You can ask your AI assistant to collect data with Multi-Book Sports Betting Odds and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's Multi-Book Sports Betting Odds. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/multi-book-sports-betting-odds"
    }
  }
}

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use Multi-Book Sports Betting Odds by Fetch Cat, or give it the identifier fetch_cat/multi-book-sports-betting-odds.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Use Fetch Cat’s Multi-Book Sports Betting Odds to collect one NFL game with moneyline, spread and total markets and live inclusion off. Keep source and observation times and compare only equivalent lines, without recommending a wager.”
• “Collect up to 2 games across NFL and NBA, moneyline only, restricted to DraftKings and FanDuel with live inclusion off. Show contributing books and available no-vig probabilities without filling missing quotes or promising executable returns.”
• “Retrieve one NFL game's public odds with live inclusion off and history store my-nfl-odds-observations. Inspect the actual stored baseline or prior observation without starting a second run, calling it true opening odds or claiming concurrent writers are supported.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

Sportsbook odds comparison

• Compare equivalent prices: See the best available price for the same full-game market, line and outcome. Different spreads and totals remain separate.
• Build a multi-book dataset: Keep all available requested books and markets together in a single game record.
• Read no-vig probabilities: Inspect normalized two-outcome probabilities for each contributing book.
• Track observed movement: Reuse a named history store to compare the current quote with an earlier observation from your runs.

Public columns currently supported are bet365, BetMGM, DraftKings, Caesars, FanDuel, Hard Rock, Fanatics and BetRivers (riverscasino). Availability varies by league, region and suspension state. bookCount reports actual contributing books. Missing books are not filled with invented prices.

Observed price history

Reuse the same caller-owned historyStoreName in sequential runs to retain each quote’s history.firstObserved, current and actual prior observation. history.status is baseline, changed or unchanged. Price or line movement appears only when a prior observation exists.

First observed means the first price this history store captured. It is not the bookmaker’s true opening price, and the Actor cannot backfill prices it never observed. Without a named store, each run starts a fresh baseline.

History retains observations seen within 30 days, up to 60,000 quotes. Concurrent writers to one history store are unsupported. History advances after dataset delivery. A history-write failure can fail a run even when its delivered dataset is already available.

FAQ

Does this replace an official sportsbook API?

It exports publicly displayed comparison data through Apify. It is not an official sportsbook feed and does not promise complete coverage, guaranteed latency or executable betting prices.

Are different spreads and totals compared together?

No. Comparisons keep the exact full-game market, line and outcome set together. A better price on a different handicap is not presented as an equivalent quote.

Why are some books or game-state fields missing?

Books can be unavailable by league, region or market state. Game-state enrichment requires a matching public scoreboard entry. Missing books remain absent and unverified metadata remains null.

Does the history show true opening odds?

No. It shows observations collected by your history store. The first observation is a baseline. Unseen earlier prices cannot be recovered.

Can I collect every league in one run?

You can request any supported combination of NFL, NBA, MLB and NHL. maxEvents caps the total delivered games. Availability still depends on each league’s public slate.

Related Actors

Combine odds observations with other public context when it is useful:

• Google News Scraper for sports headlines.
• Google News Journalist & Media Lead Finder for finding sports reporters from relevant news searches.
• Reddit Scraper for public sports discussions.
• Stocktwits Scraper for public market discussions.
• Yahoo Finance Stock Data Scraper for adjacent quantitative datasets.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥