# US Open Tennis Draws & Scores Scraper (`jungle_synthesizer/us-open-tennis-feeds-draws-scraper`) Actor Scrapes US Open Tennis draws and match data directly from the official usopen.org JSON feeds. Returns player names, seeds, countries, match status, set scores, court assignments, and scheduled times for all five draws (Men's Singles, Ladies' Singles, Men's Doubles, Ladies' Doubles, Mixed Doubles). - **URL**: https://apify.com/jungle\_synthesizer/us-open-tennis-feeds-draws-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Sports - **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 ## US Open Tennis Draws & Scores Scraper Scrapes US Open Tennis draw data and match results directly from the official **usopen.org** JSON feeds — the same endpoints the US Open website uses to render its draws pages. Returns structured match records for all five draw types: **Men's Singles (MS), Ladies' Singles (LS), Men's Doubles (MD), Ladies' Doubles (LD), and Mixed Doubles (XD)**. ### What you get Each record contains: | Field | Description | |---|---| | `tournament_year` | Tournament year (e.g. 2024) | | `draw_code` | Draw code: MS, LS, MD, LD, or XD | | `draw_name` | Full draw name (e.g. "Men's Singles") | | `round` | Round name (e.g. "Round 1", "Semi-Finals", "Final") | | `match_id` | Match identifier from the feed | | `court_name` | Court name (e.g. "Arthur Ashe Stadium") | | `scheduled_start` | Scheduled start time (ISO 8601) | | `status` | Match status: Pre, Live, or Completed | | `player_1_name` | Player/team 1 name | | `player_1_seed` | Player 1 seed (null if unseeded) | | `player_1_country` | Player 1 nation code (e.g. USA, ITA) | | `player_1_atp_id` | Player 1 ATP/WTA ID | | `player_2_name` | Player/team 2 name | | `player_2_seed` | Player 2 seed | | `player_2_country` | Player 2 nation code | | `player_2_atp_id` | Player 2 ATP/WTA ID | | `sets` | Set scores as JSON string, e.g. [[6,4],[6,3],[6,2]] | | `winner` | Winner indicator: "1" (player 1) or "2" (player 2) | | `duration_minutes` | Match duration in minutes | | `aces_p1` | Aces by player 1 (null — not available in draws feed) | | `aces_p2` | Aces by player 2 (null — not available in draws feed) | | `source` | Source URL for this record | ### Input | Parameter | Type | Default | Description | |---|---|---|---| | `year` | integer | current year | Tournament year to fetch. Historical data (2019+) is available year-round. | | `drawCodes` | array | all five | Which draws to fetch. Any subset of: MS, LS, MD, LD, XD. | | `maxItems` | integer | required | Maximum match records to return across all draws. | ### Use cases - **Sports analytics & DFS** — seed draw data into bracket prediction or fantasy scoring models. - **AI sports content** — generate pre-match previews, draw brackets, and match result summaries. - **Historical research** — retrieve full draw trees for any US Open going back to 2019. - **Live scoring** — poll during the US Open fortnight (Aug 25 – Sep 7) for live match updates. ### Architecture notes Fetches from `https://www.usopen.org/en_US/scores/feeds/{year}/draws/{DRAW_CODE}.json` directly — no browser rendering, no proxy. One request per draw code (5 total). Runs in under 60 seconds. Sibling of **wimbledon-feeds-draws-scores-scraper** — same architecture, same field names. ### Notes - Draws publish only during the tournament window. Requesting year 2026 before late August 2026 returns no matches. - `aces_p1` / `aces_p2` are always null — aces appear only in the per-match scoring endpoint (reserved for future enhancement). # 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. ## `year` (type: `integer`): Year to fetch draws for. Defaults to the current year. Note: draws are only published during the US Open fortnight (late August – early September). ## `drawCodes` (type: `array`): Which draw(s) to fetch. Valid values: MS (Men's Singles), LS (Ladies' Singles), MD (Men's Doubles), LD (Ladies' Doubles), XD (Mixed Doubles). Defaults to all five draws. ## `maxItems` (type: `integer`): Maximum number of match records to return across all draws. ## 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...", "year": 2024, "drawCodes": [ "MS", "LS", "MD", "LD", "XD" ], "maxItems": 50 } ```` # 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...", "year": 2024, "drawCodes": [ "MS", "LS", "MD", "LD", "XD" ], "maxItems": 50 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/us-open-tennis-feeds-draws-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...", "year": 2024, "drawCodes": [ "MS", "LS", "MD", "LD", "XD", ], "maxItems": 50, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/us-open-tennis-feeds-draws-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...", "year": 2024, "drawCodes": [ "MS", "LS", "MD", "LD", "XD" ], "maxItems": 50 }' | apify call jungle_synthesizer/us-open-tennis-feeds-draws-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/us-open-tennis-feeds-draws-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "US Open Tennis Draws & Scores Scraper", "description": "Scrapes US Open Tennis draws and match data directly from the official usopen.org JSON feeds. Returns player names, seeds, countries, match status, set scores, court assignments, and scheduled times for all five draws (Men's Singles, Ladies' Singles, Men's Doubles, Ladies' Doubles, Mixed Doubles).", "version": "0.1", "x-build-id": "69e2OT2e5igt5yrGv" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~us-open-tennis-feeds-draws-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-us-open-tennis-feeds-draws-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~us-open-tennis-feeds-draws-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-us-open-tennis-feeds-draws-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~us-open-tennis-feeds-draws-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-us-open-tennis-feeds-draws-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": [ "maxItems" ], "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." }, "year": { "title": "Tournament Year", "type": "integer", "description": "Year to fetch draws for. Defaults to the current year. Note: draws are only published during the US Open fortnight (late August – early September)." }, "drawCodes": { "title": "Draw Codes", "type": "array", "description": "Which draw(s) to fetch. Valid values: MS (Men's Singles), LS (Ladies' Singles), MD (Men's Doubles), LD (Ladies' Doubles), XD (Mixed Doubles). Defaults to all five draws.", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of match records to return across all draws." } } }, "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 } } } } } } } } } } ```