# ESPN Sports Scraper — Scores, Teams & Standings (`hipersoft/espn-sports-scraper`) Actor Scrape live and past scores, teams, standings and news across NBA, NFL, MLB, NHL, EPL, La Liga and more via ESPN's public data. For sports analytics, betting and fantasy. No key. - **URL**: https://apify.com/hipersoft/espn-sports-scraper.md - **Developed by:** [hiper soft](https://apify.com/hipersoft) (community) - **Categories:** Sports - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.001 / item scraped 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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 ## ESPN Sports Scraper — Scores, Teams, Standings & News Pull live and past scores, team directories, league standings, and news headlines across NBA, NFL, MLB, NHL, EPL, La Liga, and many more — all as clean structured JSON. Pick your leagues, choose a data type, and optionally set a date or date range for the scoreboard. No API key. Made for sports analytics, betting models, fantasy tools, and media dashboards. ### Features - 🏆 **17 leagues** — nba, wnba, ncaab, nfl, ncaaf, mlb, nhl, epl, laliga, seriea, bundesliga, ligue1, ucl, mls, pga, f1, atp. - 🗓️ **Scoreboard by date** — `dates` accepts a single `YYYYMMDD` or a `YYYYMMDD-YYYYMMDD` range; empty means today. - 📊 **Four data types** — switch `dataType` between scoreboard, teams, standings, and news. - ⚽ **Detailed game rows** — `homeTeam`, `awayTeam`, `homeScore`, `awayScore`, `status`, `completed`, plus a full `competitors` array. - 🏟️ **Venue context** — each game includes `venue` and `city`. - 📰 **News feed** — headlines with `description`, `published`, `byline`, `url`, and `image`. - 🔢 **Standings metrics** — wins, losses, win percentage, points for/against, streak, and rank per team. ### What you get Each scoreboard game is one clean JSON record: ```json { "league": "nba", "dataType": "scoreboard", "gameId": "401585123", "name": "Boston Celtics at Los Angeles Lakers", "shortName": "BOS @ LAL", "date": "2026-01-15T03:00Z", "status": "Final", "completed": true, "venue": "Crypto.com Arena", "city": "Los Angeles", "competitors": [ { "team": "Los Angeles Lakers", "abbreviation": "LAL", "homeAway": "home", "score": 112, "winner": true, "record": "24-14" }, { "team": "Boston Celtics", "abbreviation": "BOS", "homeAway": "away", "score": 108, "winner": false, "record": "30-9" } ], "homeTeam": "Los Angeles Lakers", "awayTeam": "Boston Celtics", "homeScore": 112, "awayScore": 108 } ```` ### Input ```json { "leagues": ["nba", "epl"], "dataType": "scoreboard", "dates": "20260115" } ``` | Field | Description | | --- | --- | | `leagues` | League codes to scrape (e.g. nba, nfl, epl). | | `dataType` | `scoreboard`, `teams`, `standings`, or `news`. | | `dates` | Scoreboard date `YYYYMMDD` or range; empty = today. | ### Use cases - Feed live and final scores into a betting or model pipeline. - Build league standings tables for a sports site or app. - Pull team directories with colors and logos for branding. - Aggregate league news headlines for a media dashboard. ### Pricing Pay-per-event: you're billed a small amount per run and per item scraped — you only pay for what you get. See the **Pricing** tab for current rates. ### Notes This Actor uses ESPN's public data endpoints and reads only publicly available scores, teams, standings, and news. It is an independent tool and is not affiliated with, endorsed by, or connected to ESPN, Inc. or its affiliates. # Actor input Schema ## `leagues` (type: `array`): Leagues to scrape: nba, wnba, ncaab, nfl, ncaaf, mlb, nhl, epl, laliga, seriea, bundesliga, ligue1, ucl, mls, pga, f1, atp. ## `dataType` (type: `string`): What to scrape for each league. ## `dates` (type: `string`): For the scoreboard: a date YYYYMMDD or range YYYYMMDD-YYYYMMDD. Empty = today / current. ## Actor input object example ```json { "leagues": [ "nfl", "epl" ], "dataType": "scoreboard", "dates": "" } ``` # 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": [ "nba" ] }; // Run the Actor and wait for it to finish const run = await client.actor("hipersoft/espn-sports-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": ["nba"] } # Run the Actor and wait for it to finish run = client.actor("hipersoft/espn-sports-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": [ "nba" ] }' | apify call hipersoft/espn-sports-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=hipersoft/espn-sports-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ESPN Sports Scraper — Scores, Teams & Standings", "description": "Scrape live and past scores, teams, standings and news across NBA, NFL, MLB, NHL, EPL, La Liga and more via ESPN's public data. For sports analytics, betting and fantasy. No key.", "version": "1.0", "x-build-id": "2dI0AvTIN2gxWi95g" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/hipersoft~espn-sports-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-hipersoft-espn-sports-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/hipersoft~espn-sports-scraper/runs": { "post": { "operationId": "runs-sync-hipersoft-espn-sports-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/hipersoft~espn-sports-scraper/run-sync": { "post": { "operationId": "run-sync-hipersoft-espn-sports-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "leagues": { "title": "Leagues", "type": "array", "description": "Leagues to scrape: nba, wnba, ncaab, nfl, ncaaf, mlb, nhl, epl, laliga, seriea, bundesliga, ligue1, ucl, mls, pga, f1, atp.", "items": { "type": "string" } }, "dataType": { "title": "Data type", "enum": [ "scoreboard", "teams", "standings", "news" ], "type": "string", "description": "What to scrape for each league.", "default": "scoreboard" }, "dates": { "title": "Dates (scoreboard only)", "type": "string", "description": "For the scoreboard: a date YYYYMMDD or range YYYYMMDD-YYYYMMDD. Empty = today / current.", "default": "" } } }, "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 } } } } } } } } } } ```