# ESPN Tennis Scoreboard Scraper (ATP + WTA) (`jungle_synthesizer/espn-tennis-us-open-wimbledon-scoreboard-scraper`) Actor Scrapes live and historical ATP + WTA match scoreboards from ESPN's hidden API. Returns match-level records with scores, status, court, round, and player details for all tournaments including Wimbledon and US Open. - **URL**: https://apify.com/jungle\_synthesizer/espn-tennis-us-open-wimbledon-scoreboard-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Sports, AI - **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. 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 ## ESPN Tennis Scoreboard Scraper (ATP + WTA) Pulls match scoreboards from ESPN's hidden tennis API. Returns structured match records for ATP and WTA tournaments — including Wimbledon and the US Open — with round, court, player names, countries, set scores, and match status. The API is open, unauthenticated, and returns ~1 MB of data per request. No browser, no proxy, no parsing HTML. You get clean JSON in under five seconds. --- ### What You Get One record per match. The schema covers what actually matters for betting analytics, DFS, and sports media applications. | Field | Type | Description | |-------|------|-------------| | `event_id` | string | ESPN's unique competition ID | | `tournament_name` | string | Full tournament name (e.g. Wimbledon, US Open) | | `tournament_slug` | string | ESPN tournament UID | | `tour` | string | Tour designation — ATP or WTA | | `round` | string | Round label (e.g. Round 1, Quarterfinals, Final) | | `court_name` | string | Court within the venue (e.g. Centre Court, Court 17) | | `status` | string | Match state: pre, in, or post | | `status_detail` | string | Human-readable status (e.g. Final, In Progress, Scheduled) | | `date_utc` | string | Match date/time in UTC ISO-8601 | | `player_1_name` | string | Player 1 full name | | `player_1_country` | string | Player 1 country | | `player_1_winner` | boolean | Whether player 1 won | | `player_2_name` | string | Player 2 full name | | `player_2_country` | string | Player 2 country | | `player_2_winner` | boolean | Whether player 2 won | | `sets` | string | Pipe-separated set scores (e.g. 6-4\|3-6\|7-5) | | `score_summary` | string | ESPN's own match summary line (e.g. Sinner bt Alcaraz 6-3 6-4) | | `match_type` | string | Draw type (e.g. Men's Singles, Women's Doubles) | | `source` | string | Data origin tag (espn-atp or espn-wta) | --- ### How It Works Two API endpoints — one for ATP, one for WTA: ```` https://site.api.espn.com/apis/site/v2/sports/tennis/atp/scoreboard https://site.api.espn.com/apis/site/v2/sports/tennis/wta/scoreboard ```` Each call returns all tournaments in the current window with their full match slates. Add a `?dates=YYYYMMDD` parameter to retrieve a specific day's scoreboard — useful for backfilling a tournament's fortnight. The actor flattens the three-level nesting (tournament → round grouping → match) into a flat record stream. Both tours run as parallel queries when `tour: both`. --- ### Input Parameters | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | `tour` | string | `both` | Which tour to fetch: `atp`, `wta`, or `both` | | `dates` | string | _(today)_ | Comma-separated dates in YYYYMMDD format (e.g. `20260629,20260630`). Leave blank for the current window. | | `maxItems` | integer | 10 | Maximum match records to return. Set to 0 for unlimited. | --- ### Example Use Cases **During Wimbledon or US Open:** Fetch today's full match slate for both tours with one run. Add to a scheduled task to refresh every hour during the tournament fortnight. **DFS lineup research:** Pull all completed first-round matches to see who's fresh and who went three sets. The `sets` field tells the story. **Backfill a tournament:** Pass a comma-separated list of dates covering the full draw (e.g. all 14 days of Wimbledon) to reconstruct the complete results history. **Multi-sport pipelines:** The same ESPN API pattern covers other sports. The `source` field tags each record's origin for downstream deduplication. --- ### Notes - ATP and WTA scoreboards are separate API roots — this actor pulls both and tags each record with `tour: ATP` or `tour: WTA`. - Odds and ATP/WTA rankings are not in the scoreboard API. The `moneyline` fields from the original spec were removed — they aren't available at this endpoint. - The `sets` field uses pipe notation (`6-4|3-6|7-5`) — split on `|` to get individual set scores as an array. - Works year-round, not just during Grand Slams. All ATP and WTA tour events are covered. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `tour` (type: `string`): Tennis tour to scrape. "atp" for men's, "wta" for women's, "both" for all. ## `dates` (type: `string`): Comma-separated dates in YYYYMMDD format to fetch (e.g. 20260629,20260630). Leave blank for today's scoreboard. ## `maxItems` (type: `integer`): Maximum number of match records to return (0 = unlimited) ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "tour": "both", "maxItems": 10 } ```` # Actor output Schema ## `results` (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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "tour": "both", "dates": "", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/espn-tennis-us-open-wimbledon-scoreboard-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 = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "tour": "both", "dates": "", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/espn-tennis-us-open-wimbledon-scoreboard-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 '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "tour": "both", "dates": "", "maxItems": 10 }' | apify call jungle_synthesizer/espn-tennis-us-open-wimbledon-scoreboard-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/espn-tennis-us-open-wimbledon-scoreboard-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ESPN Tennis Scoreboard Scraper (ATP + WTA)", "description": "Scrapes live and historical ATP + WTA match scoreboards from ESPN's hidden API. Returns match-level records with scores, status, court, round, and player details for all tournaments including Wimbledon and US Open.", "version": "0.1", "x-build-id": "Dcp10qc5dTD6hWZaH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~espn-tennis-us-open-wimbledon-scoreboard-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-espn-tennis-us-open-wimbledon-scoreboard-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/jungle_synthesizer~espn-tennis-us-open-wimbledon-scoreboard-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-espn-tennis-us-open-wimbledon-scoreboard-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/jungle_synthesizer~espn-tennis-us-open-wimbledon-scoreboard-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-espn-tennis-us-open-wimbledon-scoreboard-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": [ "tour" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "tour": { "title": "Tour", "enum": [ "atp", "wta", "both" ], "type": "string", "description": "Tennis tour to scrape. \"atp\" for men's, \"wta\" for women's, \"both\" for all.", "default": "both" }, "dates": { "title": "Dates", "type": "string", "description": "Comma-separated dates in YYYYMMDD format to fetch (e.g. 20260629,20260630). Leave blank for today's scoreboard." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of match records to return (0 = unlimited)" } } }, "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 } } } } } } } } } } ```