# Daily Bet Prediction Scraper (`rikunk/bet-prediction-scraper`) Actor Daily Bet Prediction Scraper consolidates over 1000+ shared predictions from multiple renowned betting prediction sites. This powerful tool enhances accuracy, emphasizes common tips, aims to boost successful betting outcomes, and provides a valuable, structured dataset. - **URL**: https://apify.com/rikunk/bet-prediction-scraper.md - **Developed by:** [rikunk](https://apify.com/rikunk) (community) - **Categories:** Other - **Stats:** 292 total users, 1 monthly users, 0.0% runs succeeded, 8 bookmarks - **User rating**: No ratings yet ## Pricing $20.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ### Bet Prediction Scraper Bet Prediction Scraper is developed to fetch and combine betting predictions from 9 distinguished betting websites. The primary objective is to enhance and streamline betting predictions' accuracy by incorporating data from multiple sources. At present, the scraper functions on the following sites: - 1960Tips - Forebet - MeritPredict - Predictz - SaferTip - StakeGains - StatArea - VictorsPredict - WinDrawWin These websites are well-known for their soccer betting predictions. However, predictions from each site can vary, sometimes causing uncertainty. The Bet Prediction Scraper works to mitigate this uncertainty by merging these predictions, ordering them from the most common to the least common across the sites. ### Cost Estimation for Scraping When it comes to scraping, it can be challenging to estimate the resources needed to extract data as use cases may vary significantly. That's why the best course of action is to run a test scrape with a small sample of input data and limited output. You’ll get your price per scrape, which you’ll then multiply by the number of scrapes you intend to do. [Watch this video](https://www.youtube.com/watch?v=-wyz2iscZ30&ab_channel=Apify) for a few helpful tips. And don't forget that choosing a higher plan will save you money in the long run. ### Legality of Scraping Our scrapers are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. We therefore believe that our scrapers, when used for ethical purposes by Bet Prediction Scraper users, are safe. However, you should be aware that your results could contain personal data. Personal data is protected by the [GDPR](https://en.wikipedia.org/wiki/General_Data_Protection_Regulation) in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers. You can also read this [Apify blog post](https://blog.apify.com/is-web-scraping-legal/) on the legality of web scraping. ### Input Bet Prediction Scraper does not require any specific input from the user, as it is pre-programmed to scrape football data from the aforementioned websites. ### Output The dataset extracted by Bet Prediction Scraper can be downloaded in various formats such as JSON, HTML, CSV, or Excel. The output for each website's match predictions will include details such as the teams playing, the type of bet (e.g., match result, both teams to score), and the specific prediction (e.g., Team A to win, yes for both teams to score). Furthermore, each prediction will provide information about how many of the ten sites shared that particular prediction, enabling users to easily identify the most agreed-upon predictions. Here is an example of the output: ```` \[ { event\_name: 'Pogon Szczecin - Linfield', bet\_choice: '1', bet\_odds: '1.32', number: 5, names: '1960Tips, MeritPredict, predictz, statarea, windrawwin' }, { event\_name: 'KuPS - Derry City', bet\_choice: 'X', bet\_odds: '3.20', number: 4, names: 'forebet, predictz, victorspredict, windrawwin' }, { event\_name: 'Petrocub - Maccabi Tel Aviv', bet\_choice: '2', bet\_odds: '1.42', number: 4, names: '1960Tips, predictz, statarea, windrawwin' }, { event\_name: 'Maribor - Differdange', bet\_choice: '1', bet\_odds: '1.38', number: 4, names: 'Forebet, Meritpredict, predictz, windrawwin' }, ] ```` ### Disclaimer While Bet Prediction Scraper strives to increase prediction accuracy, it's important to remember that betting always carries a risk, and outcomes cannot be guaranteed. Always bet responsibly. # Actor input Schema ## `siteSelection` (type: `string`): Choose the website to scrape. ## Actor input object example ```json { "siteSelection": "All Websites" } ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("rikunk/bet-prediction-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 = {} # Run the Actor and wait for it to finish run = client.actor("rikunk/bet-prediction-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 '{}' | apify call rikunk/bet-prediction-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=rikunk/bet-prediction-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Daily Bet Prediction Scraper", "description": "Daily Bet Prediction Scraper consolidates over 1000+ shared predictions from multiple renowned betting prediction sites. This powerful tool enhances accuracy, emphasizes common tips, aims to boost successful betting outcomes, and provides a valuable, structured dataset.", "version": "1.0", "x-build-id": "lqKzEOC7XFislAHRt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/rikunk~bet-prediction-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-rikunk-bet-prediction-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/rikunk~bet-prediction-scraper/runs": { "post": { "operationId": "runs-sync-rikunk-bet-prediction-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/rikunk~bet-prediction-scraper/run-sync": { "post": { "operationId": "run-sync-rikunk-bet-prediction-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", "properties": { "siteSelection": { "title": "Website Selection", "enum": [ "All Websites", "1960Tips", "Forebet", "MeritPredict", "Predictz", "SaferTip", "StakeGains", "StatArea", "VictorsPredict", "WinDrawWin" ], "type": "string", "description": "Choose the website to scrape.", "default": "All Websites" } } }, "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 } } } } } } } } } } ```