# Sports Betting Operator Lead Pipeline (`george.the.developer/sports-betting-operator-leads`) Actor Turn App Store betting apps into a B2B lead list of sportsbook and casino operators, one row per company with website, install signal, tier, and a verified contact email. No API key, no proxy. - **URL**: https://apify.com/george.the.developer/sports-betting-operator-leads.md - **Developed by:** [George Kioko](https://apify.com/george.the.developer) (community) - **Categories:** Lead generation - **Stats:** 4 total users, 2 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event + usage This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Betting Operator Lead Pipeline Turn App Store betting apps into a ranked B2B lead list of sportsbook and casino operators. Keyword in, one row per operator out, with the company, website, install signal, tier, and a contact email where one is public. No API key, no proxy. ### What it does The betting and iGaming industry buys leads aggressively. This pipeline finds the operators for you. It searches the Apple App Store for betting apps, folds every app back to the company that ships it (one operator can run several apps), ranks them by install signal, and enriches each with a contact email pulled from the operator website and verified against live MX records. ### What each lead row contains - **operator** and **apps** it publishes - **website** and **domain** - **email** and **email_verified** (MX checked) - **rating** and **rating_count** (the install and popularity signal) - **score** 0 to 100 and **tier** (major, established, emerging) - **genres**, **matched_keywords**, **country**, **source** ### Use cases 1. **iGaming affiliate and marketing agencies** building an outreach list of operators. 2. **Data and B2B SaaS vendors** selling into sportsbooks and casinos. 3. **Market mapping**: who operates in a country, ranked by size. ### Input - **keywords**: App Store search terms. Defaults to sportsbook, sports betting, casino, betting. - **country**: two letter App Store code (us, gb, ca, au). - **limitPerKeyword**: apps to pull per term before folding into operators. - **enrich**: scrape each homepage for an email and MX verify. Turn off for a faster, cheaper run. - **maxLeads**: cap on leads returned. ### Standby / API mode ```` GET /leads?keyword=sportsbook\&country=us\&limit=25\&enrich=true ```` Returns the same lead rows as JSON. ### Output example ```json { "operator": "DRAFTKINGS LLC.", "apps": ["DraftKings: Sports & Casino"], "website": "https://sportsbook.draftkings.com", "domain": "sportsbook.draftkings.com", "email": null, "email_verified": false, "rating": 4.81, "rating_count": 1078052, "score": 100, "tier": "major", "country": "us", "source": "apple-app-store" } ```` ### Pricing Pay per event: - **Actor start**: $0.25 per batch run - **Operator lead**: $0.06 per lead returned - **Verified contact email**: $0.10 per lead enriched with an MX verified email A run that returns leads but finds no emails only pays the per lead rate. ### Notes Data comes from the public Apple App Store search and the operators' own public websites. The large national books rarely publish a plain contact email, so verified emails skew toward mid size and emerging operators, which are usually the more useful outreach targets anyway. # Actor input Schema ## `keywords` (type: `array`): App Store search terms that surface betting operators. Defaults to sportsbook, sports betting, casino, betting. ## `keyword` (type: `string`): Quick single term instead of the list. ## `country` (type: `string`): Two letter App Store country code (us, gb, ca, au, ...). ## `limitPerKeyword` (type: `integer`): How many apps to pull per keyword before folding into operators. ## `enrich` (type: `boolean`): Scrape each operator homepage for a contact email and MX verify the domain. Turn off for a faster, cheaper run. ## `maxLeads` (type: `integer`): Cap on operator leads returned in one run. ## Actor input object example ```json { "keywords": [ "sportsbook", "sports betting", "casino", "betting" ], "country": "us", "limitPerKeyword": 20, "enrich": true, "maxLeads": 100 } ``` # 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 = { "keywords": [ "sportsbook", "sports betting", "casino", "betting" ] }; // Run the Actor and wait for it to finish const run = await client.actor("george.the.developer/sports-betting-operator-leads").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 = { "keywords": [ "sportsbook", "sports betting", "casino", "betting", ] } # Run the Actor and wait for it to finish run = client.actor("george.the.developer/sports-betting-operator-leads").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 '{ "keywords": [ "sportsbook", "sports betting", "casino", "betting" ] }' | apify call george.the.developer/sports-betting-operator-leads --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=george.the.developer/sports-betting-operator-leads", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Sports Betting Operator Lead Pipeline", "description": "Turn App Store betting apps into a B2B lead list of sportsbook and casino operators, one row per company with website, install signal, tier, and a verified contact email. No API key, no proxy.", "version": "1.0", "x-build-id": "UilIzdDl8YHWdMVKU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/george.the.developer~sports-betting-operator-leads/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-george.the.developer-sports-betting-operator-leads", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/george.the.developer~sports-betting-operator-leads/runs": { "post": { "operationId": "runs-sync-george.the.developer-sports-betting-operator-leads", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/george.the.developer~sports-betting-operator-leads/run-sync": { "post": { "operationId": "run-sync-george.the.developer-sports-betting-operator-leads", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "keywords": { "title": "Search keywords", "type": "array", "description": "App Store search terms that surface betting operators. Defaults to sportsbook, sports betting, casino, betting.", "items": { "type": "string" } }, "keyword": { "title": "Single keyword", "type": "string", "description": "Quick single term instead of the list." }, "country": { "title": "Country", "type": "string", "description": "Two letter App Store country code (us, gb, ca, au, ...).", "default": "us" }, "limitPerKeyword": { "title": "Results per keyword", "minimum": 1, "maximum": 200, "type": "integer", "description": "How many apps to pull per keyword before folding into operators.", "default": 20 }, "enrich": { "title": "Enrich with contact email", "type": "boolean", "description": "Scrape each operator homepage for a contact email and MX verify the domain. Turn off for a faster, cheaper run.", "default": true }, "maxLeads": { "title": "Max leads", "minimum": 1, "maximum": 500, "type": "integer", "description": "Cap on operator leads returned in one run.", "default": 100 } } }, "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 } } } } } } } } } } ```