# Sports Odds Aggregator (`masked_hacker/sports-odds-aggregator`) Actor Aggregate moneyline, spread, and totals odds from multiple sportsbooks per game into normalized event x market x book rows. - **URL**: https://apify.com/masked\_hacker/sports-odds-aggregator.md - **Developed by:** [Masked Hacker](https://apify.com/masked_hacker) (community) - **Categories:** Other - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 odds rows This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Sports Odds Aggregator: Moneyline, Spread & Totals Across Sportsbooks Pull live **moneyline, spread and totals odds** for NFL, NBA, MLB, NHL, WNBA, NCAAF and NCAAB games, aggregated across multiple sportsbooks into one clean, normalized dataset: one row per event x market x book x outcome. No API key, no scraping your own script against a dozen sportsbook sites. Built for **line shopping, arbitrage research, odds-comparison tools and model building** where you need every book's number for every game side by side. ### What you get - 🏈 **7 leagues:** nfl, nba, mlb, nhl, wnba, ncaaf, ncaab. - 💰 **All three core markets:** moneyline (h2h), spreads, and totals. - 📊 **Multiple sportsbooks per event:** DraftKings, FanDuel, BetMGM, Caesars, BetRivers and more. - 🧮 **Normalized rows:** every outcome is its own row, ready to pivot, join or feed a model. - 🔑 **No API key or login required.** - 📄 Clean JSON / CSV / Excel export, ready for a spreadsheet or a pipeline. ### Pricing **Pay per event: $0.003 per odds row returned, plus a small per-run start fee.** You only pay for rows you actually receive; no monthly subscription. ### Input | Field | Type | Description | |---|---|---| | `leagues` | string[] | **Required.** Leagues to pull odds for: nfl, nba, mlb, nhl, wnba, ncaaf, ncaab. | | `markets` | string[] | Bet markets to include: h2h (moneyline), spreads, totals. Defaults to all three. | | `books` | string[] | Optional. Keep only these sportsbooks, e.g. `["DraftKings", "FanDuel"]`. Empty keeps all. | | `date` | string | Optional. Restrict to games on this date, format `YYYYMMDD`. Empty uses the current scoreboard. | | `maxEvents` | int | Stop after this many events across all leagues (default 200). | | `proxyConfiguration` | proxy | Residential recommended for stable access to the odds backend. | #### Example input ```json { "leagues": ["nfl", "nba"], "markets": ["h2h", "spreads"], "maxEvents": 100 } ```` ### Output One record per outcome: a single book's line for one side of one market on one game. | Field | Description | |---|---| | `eventId`, `sport`, `league` | Game and league identity. | | `homeTeam`, `awayTeam`, `commenceTime` | Matchup and scheduled start time. | | `market` | h2h (moneyline), spreads, or totals. | | `book` | Sportsbook offering this line. | | `outcome` | Outcome label: team name or Over/Under. | | `price`, `point` | Odds price and the spread/total line (point is null for h2h). | | `sourceUrl`, `scrapedAt` | League odds page and scrape timestamp. | #### Example output ```json { "eventId": "1234567", "sport": "nfl", "league": "nfl", "homeTeam": "Kansas City Chiefs", "awayTeam": "Buffalo Bills", "commenceTime": "2026-09-13T17:00:00.000Z", "market": "spreads", "book": "DraftKings", "outcome": "Kansas City Chiefs", "price": -110, "point": -3.5, "sourceUrl": "https://www.actionnetwork.com/nfl/odds", "scrapedAt": "2026-07-15T10:20:30.000Z" } ``` ### FAQ **Do I need an API key or sportsbook account?** No. Just pick your leagues and run. **Which sportsbooks are covered?** Multiple major US sportsbooks per event, whichever books have posted a line for that game. Use `books` to filter to specific ones. **Can I get odds for a specific date?** Yes, set `date` in `YYYYMMDD` format. Leave it empty to get the current scoreboard. **Is this legal?** The actor collects only publicly available odds data. You are responsible for complying with applicable law and each sportsbook's terms in your use of the data. # Actor input Schema ## `leagues` (type: `array`): Leagues to pull odds for. Supported: nfl, nba, mlb, nhl, wnba, ncaaf, ncaab. ## `markets` (type: `array`): Bet markets to include: h2h (moneyline), spreads, totals. Defaults to all three. ## `books` (type: `array`): Optional. Keep only these sportsbooks (e.g. "DraftKings", "FanDuel", "BetMGM", "Caesars", "BetRivers"). Empty keeps all. ## `date` (type: `string`): Optional. Restrict to games on this date, format YYYYMMDD. Empty uses the current scoreboard. ## `maxEvents` (type: `integer`): Stop after processing this many events across all leagues. ## `proxyConfiguration` (type: `object`): Residential recommended for stable access to the odds backend. ## Actor input object example ```json { "leagues": [ "nfl", "nba" ], "markets": [ "h2h", "spreads", "totals" ], "maxEvents": 200, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): All scraped odds rows in the default dataset. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "leagues": [ "nfl" ] }; // Run the Actor and wait for it to finish const run = await client.actor("masked_hacker/sports-odds-aggregator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "leagues": ["nfl"] } # Run the Actor and wait for it to finish run = client.actor("masked_hacker/sports-odds-aggregator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "leagues": [ "nfl" ] }' | apify call masked_hacker/sports-odds-aggregator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=masked_hacker/sports-odds-aggregator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Sports Odds Aggregator", "description": "Aggregate moneyline, spread, and totals odds from multiple sportsbooks per game into normalized event x market x book rows.", "version": "0.0", "x-build-id": "ccuKO9bvrCugGvxAM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/masked_hacker~sports-odds-aggregator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-masked_hacker-sports-odds-aggregator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/masked_hacker~sports-odds-aggregator/runs": { "post": { "operationId": "runs-sync-masked_hacker-sports-odds-aggregator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/masked_hacker~sports-odds-aggregator/run-sync": { "post": { "operationId": "run-sync-masked_hacker-sports-odds-aggregator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "leagues" ], "properties": { "leagues": { "title": "Leagues", "type": "array", "description": "Leagues to pull odds for. Supported: nfl, nba, mlb, nhl, wnba, ncaaf, ncaab.", "items": { "type": "string" } }, "markets": { "title": "Markets", "type": "array", "description": "Bet markets to include: h2h (moneyline), spreads, totals. Defaults to all three.", "default": [ "h2h", "spreads", "totals" ], "items": { "type": "string" } }, "books": { "title": "Sportsbooks filter", "type": "array", "description": "Optional. Keep only these sportsbooks (e.g. \"DraftKings\", \"FanDuel\", \"BetMGM\", \"Caesars\", \"BetRivers\"). Empty keeps all.", "items": { "type": "string" } }, "date": { "title": "Date", "type": "string", "description": "Optional. Restrict to games on this date, format YYYYMMDD. Empty uses the current scoreboard." }, "maxEvents": { "title": "Max events", "minimum": 1, "type": "integer", "description": "Stop after processing this many events across all leagues.", "default": 200 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Residential recommended for stable access to the odds backend.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```