# SportsBookReview Odds Scraper - Betting Lines (`lulzasaur/sportsbookreview-scraper`) Actor Scrape betting odds from SportsBookReview. Compare spreads, moneylines, and totals across sportsbooks for NBA, NFL, MLB, NHL, NCAAB, NCAAF, soccer, and more. - **URL**: https://apify.com/lulzasaur/sportsbookreview-scraper.md - **Developed by:** [lulz bot](https://apify.com/lulzasaur) (community) - **Categories:** Other - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## SportsBookReview Odds Scraper Scrape live betting odds from [SportsBookReview.com](https://www.sportsbookreview.com). Compare point spreads, moneylines, and over/under totals across multiple sportsbooks for all major sports. ### Features - **Multi-sport support**: NBA, NFL, MLB, NHL, NCAAB, NCAAF, WNBA, MLS, EPL, Champions League, CFL - **All odds types**: Point spreads, moneylines, and totals (over/under) - **Cross-sportsbook comparison**: See odds from every available sportsbook side-by-side - **Date selection**: Scrape odds for any date, not just today - **Structured output**: Clean JSON with game details, venue, and odds arrays - **JSON-LD enrichment**: Combines __NEXT_DATA__ odds with JSON-LD SportsEvent data for rich game metadata ### Input | Field | Type | Description | Default | |-------|------|-------------|---------| | `sport` | string | Sport to scrape (e.g., `nba-basketball`, `nfl-football`) | `nba-basketball` | | `oddsType` | string | Odds type: `all`, `spreads`, `moneyline`, or `totals` | `all` | | `date` | string | Date in YYYY-MM-DD format (optional, defaults to today) | | | `proxyConfiguration` | object | Proxy settings | Apify datacenter proxies | #### Supported Sports | Sport | Slug | |-------|------| | NBA | `nba-basketball` | | NFL | `nfl-football` | | MLB | `mlb-baseball` | | NHL | `nhl-hockey` | | NCAAB | `ncaa-basketball` | | NCAAF | `college-football` | | WNBA | `wnba-basketball` | | MLS | `major-league-soccer` | | EPL | `english-premier-league` | | Champions League | `champions-league` | | CFL | `cfl-football` | ### Output Each dataset item represents one game with odds from all available sportsbooks: ```json { "gameId": 377351, "homeTeam": "Philadelphia 76ers", "homeTeamShort": "PHI", "awayTeam": "Boston Celtics", "awayTeamShort": "BOS", "gameDate": "2026-04-24", "gameTime": "07:00 PM", "sport": "NBA", "venue": "Xfinity Mobile Arena", "city": "Philadelphia", "state": "PA", "gameStatus": "Scheduled", "spreads": [ { "sportsbook": "kalshi", "homeSpread": 7.5, "awaySpread": -7.5, "homeOdds": -116, "awayOdds": -107 }, { "sportsbook": "prophetx", "homeSpread": 7.5, "awaySpread": -7.5, "homeOdds": -105, "awayOdds": -103 } ], "moneylines": [ { "sportsbook": "kalshi", "homeOdds": 280, "awayOdds": -350, "drawOdds": 0 } ], "totals": [ { "sportsbook": "kalshi", "overUnder": 215.5, "overOdds": -112, "underOdds": -112 } ], "url": "https://www.sportsbookreview.com/betting-odds/nba-basketball/", "scrapedAt": "2026-04-23T12:00:00.000Z" } ```` ### Use Cases - **Line shopping**: Find the best odds across sportsbooks before placing bets - **Odds monitoring**: Track line movement over time by scraping periodically - **Sports analytics**: Build models using historical odds data - **Arbitrage detection**: Compare odds across books to find arbitrage opportunities - **Research**: Academic or journalistic research on sports betting markets ### How It Works The scraper extracts data from SportsBookReview's Next.js server-rendered pages: 1. Fetches the odds page for the selected sport and odds type 2. Parses the embedded `__NEXT_DATA__` JSON containing structured odds data 3. Extracts JSON-LD `SportsEvent` blocks for venue and scheduling details 4. Merges odds from multiple page types (spreads, moneylines, totals) when `oddsType` is `all` 5. Deduplicates and outputs one clean record per game ### Cost This actor uses Pay-Per-Event pricing at $0.005 per result (game). A typical run scraping one sport costs a few cents. ### Proxy The scraper uses Apify datacenter proxies by default. You can adjust proxy settings in the input configuration. # Actor input Schema ## `sport` (type: `string`): Sport to scrape betting odds for. ## `oddsType` (type: `string`): Type of odds to scrape. 'all' fetches spreads, moneylines, and totals. ## `date` (type: `string`): Specific date to scrape odds for (format: YYYY-MM-DD). Leave empty for today's games. ## `proxyConfiguration` (type: `object`): Proxy settings for avoiding rate limits. Datacenter proxies recommended. ## Actor input object example ```json { "sport": "nba-basketball", "oddsType": "all", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "SHADER" ] } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sport": "nba-basketball", "oddsType": "all" }; // Run the Actor and wait for it to finish const run = await client.actor("lulzasaur/sportsbookreview-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "sport": "nba-basketball", "oddsType": "all", } # Run the Actor and wait for it to finish run = client.actor("lulzasaur/sportsbookreview-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 '{ "sport": "nba-basketball", "oddsType": "all" }' | apify call lulzasaur/sportsbookreview-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lulzasaur/sportsbookreview-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SportsBookReview Odds Scraper - Betting Lines", "description": "Scrape betting odds from SportsBookReview. Compare spreads, moneylines, and totals across sportsbooks for NBA, NFL, MLB, NHL, NCAAB, NCAAF, soccer, and more.", "version": "1.0", "x-build-id": "90VSFUdjV6YwTH1bT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lulzasaur~sportsbookreview-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lulzasaur-sportsbookreview-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lulzasaur~sportsbookreview-scraper/runs": { "post": { "operationId": "runs-sync-lulzasaur-sportsbookreview-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lulzasaur~sportsbookreview-scraper/run-sync": { "post": { "operationId": "run-sync-lulzasaur-sportsbookreview-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "sport" ], "properties": { "sport": { "title": "Sport", "enum": [ "nba-basketball", "nfl-football", "mlb-baseball", "nhl-hockey", "ncaa-basketball", "college-football", "wnba-basketball", "major-league-soccer", "english-premier-league", "champions-league", "cfl-football" ], "type": "string", "description": "Sport to scrape betting odds for.", "default": "nba-basketball" }, "oddsType": { "title": "Odds Type", "enum": [ "all", "spreads", "moneyline", "totals" ], "type": "string", "description": "Type of odds to scrape. 'all' fetches spreads, moneylines, and totals.", "default": "all" }, "date": { "title": "Date (YYYY-MM-DD)", "type": "string", "description": "Specific date to scrape odds for (format: YYYY-MM-DD). Leave empty for today's games." }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for avoiding rate limits. Datacenter proxies recommended.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "SHADER" ] } } } }, "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 } } } } } } } } } } ```