# Understat xG League & Team Stats Scraper (`jungle_synthesizer/understat-xg-league-team-stats-scraper`) Actor Scrape expected goals (xG), xGA, NPxG, PPDA and full standings for all 6 understat.com leagues across any available season. One row per team per season, aggregated from match-level history. - **URL**: https://apify.com/jungle\_synthesizer/understat-xg-league-team-stats-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Sports, Developer tools - **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 ## Understat xG League & Team Stats Scraper Scrape expected goals (xG), non-penalty xG (NPxG), xGA, PPDA, deep completions, and full W/D/L standings from [understat.com](https://understat.com/) — the canonical open xG data source for European football. Covers all 6 understat leagues (EPL, La Liga, Bundesliga, Serie A, Ligue 1, RFPL) across every available season (2014–present). One aggregated row per team per season. ### Features - **6 leagues** in a single run: EPL, La Liga, Bundesliga, Serie A, Ligue 1, RFPL - **All seasons** from 2014/2015 onwards — back-fill historical data or track the current season - **Match-level aggregation** — sums each team's per-match xG, xGA, NPxG, PPDA, deep completions into a clean season total - **PPDA ratio** computed correctly as `passes_attempted / defensive_actions` (lower = more pressing) - No proxy required — understat.com is open and datacenter-friendly ### Use Cases - Build football analytics dashboards (xG vs actual goals by team) - Feed betting or fantasy football models with expected-goals standings - Compare xG performance across leagues or seasons for research / Substack writing - Backtest xG-based table predictions ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `leagues` | array | (all 6) | Which leagues to scrape. Valid values: `EPL`, `La_liga`, `Bundesliga`, `Serie_A`, `Ligue_1`, `RFPL` | | `seasons` | array | `["2024"]` | Season start years to scrape. E.g. `["2024"]` = 2024/2025, `["2023","2024"]` = two seasons | | `maxItems` | integer | 10 | Maximum records to return (0 = unlimited) | #### Example input — EPL last 3 seasons ```json { "leagues": ["EPL"], "seasons": ["2022", "2023", "2024"], "maxItems": 0 } ```` #### Example input — All leagues, current season ```json { "leagues": ["EPL", "La_liga", "Bundesliga", "Serie_A", "Ligue_1", "RFPL"], "seasons": ["2024"], "maxItems": 0 } ``` ### Output One record per team per season: ```json { "season": "2024/2025", "league": "EPL", "team_name": "Aston Villa", "team_id": "71", "games_played": 38, "wins": 19, "draws": 9, "losses": 10, "goals_for": 58, "goals_against": 51, "xg_for": 66.39, "xg_against": 55.0, "npxg": 61.71, "deep": 285, "ppda": 12.39, "oppda": 9.96, "points": 66, "source": "https://understat.com/league/EPL" } ``` #### Field reference | Field | Description | |-------|-------------| | `season` | Season label (e.g. `2024/2025`) | | `league` | League name | | `team_name` | Team name as shown on understat.com | | `team_id` | Understat internal team ID | | `games_played` | Total matches played | | `wins` / `draws` / `losses` | Season record | | `goals_for` / `goals_against` | Goals scored / conceded | | `xg_for` | Total expected goals (xG) | | `xg_against` | Total expected goals against (xGA) | | `npxg` | Non-penalty xG (excludes spot-kick quality) | | `deep` | Deep completions — passes into the opposition's danger zone | | `ppda` | Passes per defensive action by opponent (lower = team presses more aggressively) | | `oppda` | Opponent PPDA — how much the team is pressed | | `points` | Total league points | | `source` | Source URL | ### Notes - Data is sourced from understat.com's internal XHR API (`/getLeagueData/{league}/{season}`), which returns gzip-compressed JSON. All values are aggregated from per-match history. - PPDA and OPPDA are `null` if a team played 0 defensive actions (extremely rare, data gap seasons only). - The RFPL (Russian Premier League) data coverage may be incomplete for recent seasons. - `maxItems` counts across all requested league × season combinations. Set to `0` to scrape everything. # 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. ## `leagues` (type: `array`): List of leagues to scrape. Valid values: EPL, La\_liga, Bundesliga, Serie\_A, Ligue\_1, RFPL. Leave empty to scrape all 6 leagues. ## `seasons` (type: `array`): List of seasons to scrape (start year, e.g. 2024 for 2024/2025). Leave empty for current season only. ## `maxItems` (type: `integer`): Maximum number of team-season 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...", "leagues": [ "EPL" ], "seasons": [ "2024" ], "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...", "leagues": [ "EPL" ], "seasons": [ "2024" ], "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/understat-xg-league-team-stats-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...", "leagues": ["EPL"], "seasons": ["2024"], "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/understat-xg-league-team-stats-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...", "leagues": [ "EPL" ], "seasons": [ "2024" ], "maxItems": 10 }' | apify call jungle_synthesizer/understat-xg-league-team-stats-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/understat-xg-league-team-stats-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Understat xG League & Team Stats Scraper", "description": "Scrape expected goals (xG), xGA, NPxG, PPDA and full standings for all 6 understat.com leagues across any available season. One row per team per season, aggregated from match-level history.", "version": "0.1", "x-build-id": "tzviZLPyxsj513dE0" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~understat-xg-league-team-stats-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-understat-xg-league-team-stats-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~understat-xg-league-team-stats-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-understat-xg-league-team-stats-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~understat-xg-league-team-stats-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-understat-xg-league-team-stats-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." }, "leagues": { "title": "Leagues", "type": "array", "description": "List of leagues to scrape. Valid values: EPL, La_liga, Bundesliga, Serie_A, Ligue_1, RFPL. Leave empty to scrape all 6 leagues.", "items": { "type": "string" } }, "seasons": { "title": "Seasons", "type": "array", "description": "List of seasons to scrape (start year, e.g. 2024 for 2024/2025). Leave empty for current season only.", "items": { "type": "string" } }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of team-season 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 } } } } } } } } } } ```