# Action Network Sports Odds Line Movement Scraper (`automation-lab/action-network-sports-odds-line-movement-scraper`) Actor Scrape Action Network odds, spreads, totals, public betting percentages, and game metadata by league/date for sports betting analytics. - **URL**: https://apify.com/automation-lab/action-network-sports-odds-line-movement-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ## Action Network Sports Odds Line Movement Scraper Action Network Sports Odds Line Movement Scraper extracts sportsbook odds snapshots from public Action Network scoreboard endpoints. Use it to monitor moneylines, spreads, totals, public betting splits, game metadata, and source URLs across MLB, NBA, NFL, NHL, college football, and college basketball. ### What does Action Network Sports Odds Line Movement Scraper do? It turns Action Network scoreboard API responses into clean Apify dataset rows. - 🏟️ Fetch leagues such as `mlb`, `nba`, `nfl`, `nhl`, `ncaab`, and `ncaaf` - 📅 Query one or more dates - 📈 Save one row per sportsbook odds snapshot - 💸 Extract moneylines, spreads, spread prices, totals, over prices, and under prices - 👥 Include public betting and money percentages when Action Network provides them - 🔗 Include both the API URL and a user-facing Action Network game URL ### Who is this Action Network odds scraper for? Sports betting analysts use it to compare lines across books. Affiliate and content teams use it to create odds tables and daily market summaries. Quantitative bettors use it to collect repeatable historical snapshots for models. Media researchers use it to track market movement around high-profile games. Automation teams use it to feed dashboards, alerts, spreadsheets, and data warehouses. ### Why use it? Action Network exposes rich scoreboard data, but the raw JSON is nested by game and sportsbook. This actor normalizes the data into flat rows that are easy to export, join, filter, and analyze. You can run it on a schedule, compare books, archive odds snapshots, or pipe the data into your own betting intelligence workflow. ### What data can you extract? | Field group | Examples | |---|---| | League and event | league, sport, season, gameId, startTime, status | | Teams | homeTeamName, homeTeamAbbr, awayTeamName, awayTeamAbbr | | Sportsbook | bookId, bookName | | Moneyline | moneylineHome, moneylineAway | | Spread | spreadHome, spreadHomePrice, spreadAway, spreadAwayPrice | | Total | total, overPrice, underPrice | | Consensus | public bet percentages and money percentages when present | | Traceability | apiUrl, sourceUrl, insertedAt, scrapedAt | ### How much does it cost to scrape Action Network sports odds? The actor uses pay-per-event pricing. - Start event: small one-time run fee - Item event: charged per saved odds row - Higher usage tiers receive discounted per-row pricing Use `maxItems` to control spend during tests. A normal monitoring run can start with 100-250 rows and scale up after you verify the leagues and books you need. ### Input options | Input | Description | |---|---| | `leagues` | Action Network league slugs to fetch | | `dates` | Dates in `YYYY-MM-DD` or `YYYYMMDD`; leave empty for today | | `bookIds` | Action Network sportsbook IDs | | `period` | Market period, usually `game` | | `maxItems` | Maximum odds rows saved | | `includeLive` | Include live/in-progress games | | `includeCompleted` | Include completed games | ### Example input ```json { "leagues": ["mlb", "nba", "nfl", "nhl"], "dates": ["2026-07-06", "2026-07-07"], "bookIds": ["15", "30", "76", "75", "123"], "period": "game", "maxItems": 250, "includeLive": true, "includeCompleted": true } ```` ### Example output ```json { "league": "mlb", "sport": "baseball", "gameId": 291585, "startTime": "2026-07-06T22:45:00.000Z", "status": "complete", "bookName": "DraftKings", "homeTeamName": "Washington Nationals", "awayTeamName": "Houston Astros", "moneylineHome": -115, "moneylineAway": -105, "spreadHome": -1.5, "spreadHomePrice": 145, "total": 10, "overPrice": -101, "underPrice": -119, "apiUrl": "https://api.actionnetwork.com/web/v1/scoreboard/mlb?...", "sourceUrl": "https://www.actionnetwork.com/mlb/game/...", "scrapedAt": "2026-07-07T02:33:10.608Z" } ``` ### How to scrape Action Network odds 1. Choose one or more leagues. 2. Add dates or leave dates empty for today. 3. Keep the default sportsbook IDs or enter your own Action Network book IDs. 4. Set `maxItems` to a safe number for your first run. 5. Run the actor. 6. Export the dataset as JSON, CSV, Excel, or via the Apify API. ### Tips for best results - Use active sports seasons for the largest output volume. - Include multiple leagues when you need at least 100 rows. - Keep completed games enabled for historical date backfills. - Use scheduled runs for daily line monitoring. - Store `apiUrl` and `insertedAt` so you can audit exactly which Action Network snapshot produced each row. ### Integrations Send results to Google Sheets for editorial odds tables. Send results to BigQuery, Snowflake, or Postgres for modeling. Use webhooks to trigger Slack or Discord alerts when a scheduled run completes. Use Make, Zapier, or n8n to combine odds rows with internal content calendars. ### API usage with Node.js ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/action-network-sports-odds-line-movement-scraper').call({ leagues: ['mlb', 'nba'], dates: ['2026-07-06'], maxItems: 100 }); console.log(run.defaultDatasetId); ``` ### API usage with Python ```python from apify_client import ApifyClient client = ApifyClient('MY-APIFY-TOKEN') run = client.actor('automation-lab/action-network-sports-odds-line-movement-scraper').call(run_input={ 'leagues': ['mlb', 'nba'], 'dates': ['2026-07-06'], 'maxItems': 100, }) print(run['defaultDatasetId']) ``` ### API usage with cURL ```bash curl -X POST 'https://api.apify.com/v2/acts/automation-lab~action-network-sports-odds-line-movement-scraper/runs?token=MY-APIFY-TOKEN' \ -H 'Content-Type: application/json' \ -d '{"leagues":["mlb","nba"],"dates":["2026-07-06"],"maxItems":100}' ``` ### MCP integration Use this actor from Claude Code or Claude Desktop through Apify MCP. MCP server URL: ```text https://mcp.apify.com/?tools=automation-lab/action-network-sports-odds-line-movement-scraper ``` Example prompts: - "Run the Action Network sports odds scraper for MLB and NBA today and summarize the tightest spreads." - "Fetch 250 Action Network odds rows and identify books with different totals for the same game." - "Export Action Network line movement snapshots for yesterday into a CSV-ready dataset." ### Scheduling workflows Run every morning for pre-game odds. Run hourly during busy slates. Run after major injury news if your workflow needs fresh market data. Keep `maxItems` high enough to cover all selected leagues and books. ### Data quality notes The actor saves exactly what the public Action Network scoreboard endpoint returns at run time. If Action Network omits public betting percentages for a market, the actor returns `null` for those percentage fields instead of guessing. Book names are mapped for common sportsbook IDs. Unknown IDs are preserved as numeric `bookId` values and labeled as `Book `. ### Troubleshooting If you get no rows, try an active league/date combination such as MLB during regular season. If you expected a specific book, verify that its Action Network book ID is included in `bookIds`. If public betting percentages are null, Action Network did not include them in the public response for that game/book/market. ### Legality This actor extracts publicly accessible Action Network data. You are responsible for using the data in compliance with Action Network terms, applicable law, sportsbook rules, and any regulations that apply to betting data in your jurisdiction. ### Related sports data scrapers Explore related automation-lab actors on Apify: - https://apify.com/automation-lab/google-news-scraper - https://apify.com/automation-lab/espn-scraper - https://apify.com/automation-lab/youtube-channel-scraper ### Changelog #### 0.1 Initial release with Action Network scoreboard odds extraction by league, date, sportsbook IDs, period, and max item cap. ### FAQ #### Can it scrape line movement history? Version 0.1 captures the current public scoreboard odds snapshot and Action Network line timestamp fields. Schedule the actor to build your own line movement archive over time. #### Does it require a sportsbook account? No. It uses unauthenticated public Action Network scoreboard responses. #### Can I choose sportsbooks? Yes. Pass Action Network sportsbook IDs in `bookIds`. #### Can I run it for multiple days? Yes. Add multiple values to `dates`. #### Can I export to CSV? Yes. Apify datasets can be exported as CSV, JSON, Excel, XML, RSS, and HTML. #### Is this a betting recommendation tool? No. It provides structured odds data. It does not advise bets or guarantee price availability. # Actor input Schema ## `leagues` (type: `array`): Action Network league slugs to fetch. Common values: mlb, nba, nfl, nhl, ncaab, ncaaf. ## `dates` (type: `array`): Optional dates to fetch. Use YYYY-MM-DD or YYYYMMDD. Leave empty to fetch today. ## `maxItems` (type: `integer`): Maximum sportsbook odds rows to save across all selected leagues and dates. ## `bookIds` (type: `array`): Action Network book IDs. Defaults include DraftKings, FanDuel, Consensus, PointsBet, and ESPN BET where available. ## `period` (type: `string`): Market period passed to Action Network. Use game for full-game markets. ## `includeLive` (type: `boolean`): Include games that Action Network marks live or in progress. ## `includeCompleted` (type: `boolean`): Keep completed games in the output. Turn off for pre-game/live monitoring only. ## Actor input object example ```json { "leagues": [ "mlb", "nba", "nfl", "nhl" ], "dates": [ "2026-07-06", "2026-07-07" ], "maxItems": 250, "bookIds": [ "15", "30", "76", "75", "123" ], "period": "game", "includeLive": true, "includeCompleted": true } ``` # Actor output Schema ## `overview` (type: `string`): No description # 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": [ "mlb", "nba", "nfl", "nhl" ], "dates": [ "2026-07-06", "2026-07-07" ], "maxItems": 250, "bookIds": [ "15", "30", "76", "75", "123" ], "period": "game" }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/action-network-sports-odds-line-movement-scraper").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": [ "mlb", "nba", "nfl", "nhl", ], "dates": [ "2026-07-06", "2026-07-07", ], "maxItems": 250, "bookIds": [ "15", "30", "76", "75", "123", ], "period": "game", } # Run the Actor and wait for it to finish run = client.actor("automation-lab/action-network-sports-odds-line-movement-scraper").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": [ "mlb", "nba", "nfl", "nhl" ], "dates": [ "2026-07-06", "2026-07-07" ], "maxItems": 250, "bookIds": [ "15", "30", "76", "75", "123" ], "period": "game" }' | apify call automation-lab/action-network-sports-odds-line-movement-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/action-network-sports-odds-line-movement-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Action Network Sports Odds Line Movement Scraper", "description": "Scrape Action Network odds, spreads, totals, public betting percentages, and game metadata by league/date for sports betting analytics.", "version": "0.1", "x-build-id": "ScLHaaD4Wla8GXpQM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~action-network-sports-odds-line-movement-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-action-network-sports-odds-line-movement-scraper", "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/automation-lab~action-network-sports-odds-line-movement-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-action-network-sports-odds-line-movement-scraper", "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/automation-lab~action-network-sports-odds-line-movement-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-action-network-sports-odds-line-movement-scraper", "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": "Action Network league slugs to fetch. Common values: mlb, nba, nfl, nhl, ncaab, ncaaf.", "default": [ "mlb", "nba", "nfl", "nhl" ], "items": { "type": "string" } }, "dates": { "title": "Dates", "type": "array", "description": "Optional dates to fetch. Use YYYY-MM-DD or YYYYMMDD. Leave empty to fetch today.", "default": [], "items": { "type": "string" } }, "maxItems": { "title": "Maximum odds records", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum sportsbook odds rows to save across all selected leagues and dates.", "default": 250 }, "bookIds": { "title": "Sportsbook IDs", "type": "array", "description": "Action Network book IDs. Defaults include DraftKings, FanDuel, Consensus, PointsBet, and ESPN BET where available.", "default": [ "15", "30", "76", "75", "123" ], "items": { "type": "string" } }, "period": { "title": "Betting period", "type": "string", "description": "Market period passed to Action Network. Use game for full-game markets.", "default": "game" }, "includeLive": { "title": "Include live games", "type": "boolean", "description": "Include games that Action Network marks live or in progress.", "default": true }, "includeCompleted": { "title": "Include completed games", "type": "boolean", "description": "Keep completed games in the output. Turn off for pre-game/live monitoring only.", "default": true } } }, "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 } } } } } } } } } } ```