# Tennis Data & Analytics (`livetennisapi/tennis-data-analytics`) Actor Real-time tennis scores, players, match-winner market prices, and model win-probability analysis. Live match streaming to dataset. - **URL**: https://apify.com/livetennisapi/tennis-data-analytics.md - **Developed by:** [Live Tennis API](https://apify.com/livetennisapi) (community) - **Categories:** Developer tools, AI, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 list fetches 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/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 ## Tennis Data & Analytics Real-time tennis data for **ATP, WTA, Challenger and ITF** — singles and doubles. Live scores, players and rankings, match-winner market prices, and model-driven win-probability, delivered straight into an Apify dataset. Pay per event. No subscription, no API key to manage. ### What you can fetch | Resource | What you get | |---|---| | `live` | Every match currently in progress, with live score state | | `history` | Recently completed matches with the derived winner | | `players` | Player search — bio, ranking, country, tour | | `match` | Full detail for one match | | `score` | Current score only — the lowest-latency read | | `analysis` | Model win-probability, confidence and key factors | | `stream` | Live WebSocket feed streamed to your dataset for N minutes | ### Score format `sets` is `[sets_p1, sets_p2]`. `games` is `[games_p1, games_p2]` where **each side is a per-set list** — so `[[6,3,2],[4,6,1]]` reads 6-4, 3-6, 2-1. It is player-major, not set-major; reading it the other way gives a plausible-looking but wrong scoreline. All timestamps are UTC ISO 8601 with a `Z` suffix. ### Prefer to call the API directly? This Actor wraps the [Live Tennis API](https://livetennisapi.com). If you would rather integrate it into your own application, there are official clients: | Language | Install | |---|---| | Python | `pip install livetennisapi` | | JavaScript / TypeScript | `npm install livetennisapi` | | MCP server for LLM agents | `npx livetennisapi-mcp` | Both SDKs are typed, MIT licensed, and ship a `livetennis` CLI plus a reconnecting client for the WebSocket feed. The MCP server gives Claude, Cursor and other agents 12 read-only tools over the same data, and is listed in the official MCP Registry. - **Full API reference** — https://docs.livetennisapi.com/reference.html - **OpenAPI 3.1 specification** — https://github.com/livetennisapi/openapi - **Source, all MIT** — https://github.com/livetennisapi - **Plans and pricing** — https://livetennisapi.com ### Support Questions or a data problem? Open an issue at https://github.com/livetennisapi/openapi/issues or see https://livetennisapi.com # Actor input Schema ## `resource` (type: `string`): What to fetch. ## `matchId` (type: `integer`): Required for match / score / analysis. ## `search` (type: `string`): Name substring for resource=players. ## `limit` (type: `integer`): Page size for list resources (max 200). ## `streamMinutes` (type: `integer`): resource=stream: how long to stream live scores into the dataset (1-60). ## Actor input object example ```json { "resource": "live", "limit": 50, "streamMinutes": 5 } ```` # Actor output Schema ## `results` (type: `string`): All rows returned by this run, in the default dataset. # 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("livetennisapi/tennis-data-analytics").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("livetennisapi/tennis-data-analytics").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 livetennisapi/tennis-data-analytics --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=livetennisapi/tennis-data-analytics", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Tennis Data & Analytics", "description": "Real-time tennis scores, players, match-winner market prices, and model win-probability analysis. Live match streaming to dataset.", "version": "1.0", "x-build-id": "xgObqSOGthAlEN2z5" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/livetennisapi~tennis-data-analytics/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-livetennisapi-tennis-data-analytics", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/livetennisapi~tennis-data-analytics/runs": { "post": { "operationId": "runs-sync-livetennisapi-tennis-data-analytics", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/livetennisapi~tennis-data-analytics/run-sync": { "post": { "operationId": "run-sync-livetennisapi-tennis-data-analytics", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "resource" ], "properties": { "resource": { "title": "Resource", "enum": [ "live", "match", "score", "analysis", "players", "history", "stream" ], "type": "string", "description": "What to fetch.", "default": "live" }, "matchId": { "title": "Match id", "type": "integer", "description": "Required for match / score / analysis." }, "search": { "title": "Player search", "type": "string", "description": "Name substring for resource=players." }, "limit": { "title": "Limit", "type": "integer", "description": "Page size for list resources (max 200).", "default": 50 }, "streamMinutes": { "title": "Stream minutes", "type": "integer", "description": "resource=stream: how long to stream live scores into the dataset (1-60).", "default": 5 } } }, "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 } } } } } } } } } } ```