# Understat xG & Shot Data - Football Analytics (`constructive_calm/understat-football-analytics`) Actor Scrapes Understat: player/team season aggregates, per-match xG/PPDA/win-probabilities, shot-level data with x/y coords + xG, situation/formation/game-state breakdowns, player career histories, and fixtures across the Big 5 + RFPL (2014–2025). Multi-season + multi-league in a single run. - **URL**: https://apify.com/constructive\_calm/understat-football-analytics.md - **Developed by:** [Omar Eldeeb](https://apify.com/constructive_calm) (community) - **Categories:** Agents, Developer tools, Other - **Stats:** 6 total users, 4 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $0.80 / 1,000 player-season aggregate rows 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 & Shot Data — Football Analytics Scrapes [understat.com](https://understat.com/): player and team season aggregates, per-match xG/PPDA/win-probabilities, **shot-level data with x/y coordinates**, situation/formation/game-state breakdowns, player career multi-season histories, and upcoming fixtures across the Big 5 European leagues + RFPL (2014/15 → 2025/26). ### Why this actor Most Understat scrapers stop at league-level aggregates. This one exposes every data tier Understat has, in a single run, with multi-season + multi-league support out of the box. | Workload | Typical alternative | This actor | |---|---|---| | EPL one-season player stats (~600 players) | $0.59 | **$0.59** (parity) | | All 6 leagues × one season player stats | $3.04 | **~$2.99** | | 5 years EPL player stats in one run | impossible (5 separate runs) | **~$2.95** | | Shot-level data, EPL one season (~9,500 shots) | unavailable | **~$7.22** | | Every EPL match + shots in one run | unavailable | **~$7.22** | Pricing is pure pay-per-event. Per-row meters mean you pay for what you extract — no per-run fee, no per-league surcharge. ### Modes Pick a mode to determine what the actor scrapes: | Mode | What you get | Source | |---|---|---| | `league_players` | One row per (player × season). xG, xA, npxG, xGChain, xGBuildup, per-90 derivations, position, appearances. | `/league/{LEAGUE}/{SEASON}` HTML or `/main/getPlayersStats/` POST when filters are set | | `league_teams` | One row per (team × season). W/D/L, GF/GA, xG/xGA, npxG/npxGA, npxGD, PPDA, deep, points, xPoints. | `/league/{LEAGUE}/{SEASON}` (aggregated from per-match `history` array) | | `league_matches` | One row per played match. Score, xG home/away, win-probability forecast. | `/league/{LEAGUE}/{SEASON}` `datesData` | | `match_shots` ⭐ | One row per shot. x/y normalized pitch coordinates, xG model value, situation, body part, result, derived distance and angle, score at time of shot. | `/match/{ID}` (parses `shotsData` + `match_info`) | | `team_situations` | Long-format: one row per (team × season × breakdown × label). Covers situation (OpenPlay/SetPiece/Corner/FreeKick/Penalty), formation (4-3-3 etc), gameState (leading/tied/trailing), timing, shotZones, attackSpeed, result. | `/team/{TEAM}/{SEASON}` `statisticsData` | | `player_career` | One row per (player × every season the player appears in). Full multi-season trajectory for any player ID. | `/player/{ID}` `playersData` | | `fixtures` | One row per upcoming match. Date/time, teams, pre-match win-probability forecast (xG fields are null until played). | `/league/{LEAGUE}/{SEASON}` `datesData` (isResult=false) | ⭐ **`match_shots`** is the killer feature — most scrapers don't expose it. ### Pricing | Event | Price | |---|---| | `league-season-scraped` | $0.01 — one league × one season "discovery" fetch | | `team-season-scraped` | $0.005 | | `player-season-scraped` | $0.0008 | | `match-scraped` | $0.004 | | `shot-scraped` | $0.0006 | | `situation-breakdown-scraped` | $0.003 | | `player-career-scraped` | $0.0008 | | `fixture-scraped` | $0.0008 | **No actor-start fee. No per-league surcharge.** First 100 chargeable events of every run are free — enough to taste every tier including a 75-shot heatmap and a multi-season career bundle. ### Cost cap The `maxCostUsd` input (default **$25**) is a hard cap. The actor estimates cost up-front from your scope and warns you. Mid-run, every charge is checked against the cap; once hit, the run stops gracefully and writes a `cost_cap_reached` status row to the dataset. Without this, a typo like *all 6 leagues × all 11 seasons × match_shots* could cost ~$375. With it, you're protected. ### Sample inputs #### Player season stats (parity workload) ```json { "mode": "league_players", "leagues": ["EPL"], "seasons": [2024] } ```` #### Multi-season comparison (one run, not five) ```json { "mode": "league_players", "leagues": ["EPL"], "seasons": [2020, 2021, 2022, 2023, 2024] } ``` #### Forwards-only with date range ```json { "mode": "league_players", "leagues": ["La_liga"], "seasons": [2024], "positions": ["F"], "dateStart": "2024-08-01", "dateEnd": "2024-12-31", "minAppearances": 5 } ``` #### Shot map for a single match ```json { "mode": "match_shots", "matchIds": [26630] } ``` #### All shots in a season (heavy — uses your cost cap) ```json { "mode": "match_shots", "leagues": ["EPL"], "seasons": [2024], "maxCostUsd": 10 } ``` #### Mo Salah's complete career ```json { "mode": "player_career", "playerIds": [1250] } ``` #### Arsenal's situation breakdowns ```json { "mode": "team_situations", "leagues": ["EPL"], "seasons": [2024], "teams": ["Arsenal"] } ``` #### Upcoming Premier League fixtures ```json { "mode": "fixtures", "leagues": ["EPL"], "seasons": [2025] } ``` ### Output Every row carries: - `type` — discriminator (`player_season`, `team_season`, `match`, `shot`, `team_situation`, `player_career_season`, `fixture`, or `status`) - `_meta.mode` — which mode produced the row - `_meta.scrapedAt` — ISO 8601 timestamp - `_meta.source` — always `"understat"` Set `includeRawJson: true` to attach the original Understat record under `_raw` (useful for power users; off by default to keep the dataset light). ### Resume Long runs check-point progress to a named key-value store keyed by mode + input scope. If a run is interrupted, the next run with the same input picks up where it left off — already-completed (league × season) or (matchId) or (playerId) units are skipped. ### Limits - **leagues × seasons ≤ 60** — protects against accidental over-scope (e.g. 6 × 11 = 66 league-seasons). - **Cost cap** defaults to $25/run. - **`match_shots`** throttles requests to 6 s between matches to mirror the empirically safe rate. Expect a full EPL season (~380 matches × ~25 shots) to take ~40 minutes. - **RFPL** team names are Cyrillic — the actor URL-encodes them automatically. - **Future fixtures** have null `xG` fields until played. ### Coverage | League | Slug | Earliest season | |---|---|---| | English Premier League | `EPL` | 2014/15 | | Spanish La Liga | `La_liga` | 2014/15 | | German Bundesliga | `Bundesliga` | 2014/15 | | Italian Serie A | `Serie_A` | 2014/15 | | French Ligue 1 | `Ligue_1` | 2014/15 | | Russian Premier League | `RFPL` | 2014/15 | ### Notes - This actor scrapes a publicly accessible website. Be mindful of Understat's resources — the built-in throttle is conservative, and `match_shots` adds an extra delay because of its high fan-out. - The xG values are Understat's neural-net model output. If you need the underlying training data or the model itself, you'll need to talk to them. - Data freshness lags Understat's updates by minutes-to-hours depending on their crawl cadence. # Actor input Schema ## `mode` (type: `string`): What to scrape. 'league\_players' returns per-player season aggregates (xG, xA, xGChain, xGBuildup, etc). 'league\_teams' returns team season aggregates (W/D/L, xG, xGA, PPDA, points, xPoints). 'league\_matches' returns played-match metadata (xG, win-prob, deep, ppda). 'match\_shots' returns every shot with x/y coords, xG, situation, body part, result. 'team\_situations' returns long-format breakdowns by situation/formation/game-state/timing/zones. 'player\_career' returns one player's full multi-season history. 'fixtures' returns upcoming matches. ## `leagues` (type: `array`): One or more of the six leagues Understat covers. Required for league\_\*, match\_shots (when matchIds is empty), team\_situations (when teams is empty), and fixtures. ## `seasons` (type: `array`): List of season start years. 2024 = 2024/25 season. Understat history goes back to 2014 (the 2014/15 season). Multi-season is supported in a single run — pull 11 years of EPL in one go instead of 11 separate invocations. ## `teams` (type: `array`): Team names exactly as Understat displays them — e.g. 'Arsenal', 'Manchester United', 'Real Madrid', 'Зенит'. Spaces become underscores in URLs automatically. Used by team\_situations to filter to specific teams. ## `matchIds` (type: `array`): Specific match IDs to scrape directly (used by match\_shots). When set, leagues/seasons are ignored. Find IDs in Understat URLs: /match/{ID}. ## `playerIds` (type: `array`): Player IDs for player\_career mode. Find IDs in Understat URLs: /player/{ID}. Examples: 553 (Messi), 619 (Agüero), 1250 (Salah), 8260 (Haaland). ## `positions` (type: `array`): Filter players by position. Only applies to league\_players mode (uses Understat's filtered POST endpoint). ## `nLastMatches` (type: `integer`): Filter player stats to the last N matches. 0 = full season. Only applies to league\_players mode. ## `dateStart` (type: `string`): Restrict league\_players stats to matches on or after this date. Leave empty for none. ## `dateEnd` (type: `string`): Restrict league\_players stats to matches on or before this date. Leave empty for none. ## `minAppearances` (type: `integer`): Drop player rows where games played is below this threshold (client-side filter). 0 = no filter. ## `maxResults` (type: `integer`): Stop emitting after this many dataset rows. 0 = unlimited (subject to maxCostUsd). ## `maxCostUsd` (type: `integer`): Hard cap on the run's total billed amount. The actor estimates cost up-front and stops gracefully if the cap is hit mid-run. Default $25 protects you from accidental high-volume runs (e.g. all 6 leagues × 11 seasons of shots ≈ $375). ## `includeRawJson` (type: `boolean`): When true, attach the original Understat record under `_raw` on every output row. Useful for power users / debugging. Default off — keeps the dataset light. ## `proxyConfiguration` (type: `object`): Apify proxy. Understat does not block scrapers, but residential proxies help users behind corporate networks. Default = Apify residential. ## Actor input object example ```json { "mode": "league_players", "leagues": [ "EPL" ], "seasons": [ "2024" ], "teams": [], "matchIds": [], "playerIds": [], "positions": [], "nLastMatches": 0, "dateStart": "", "dateEnd": "", "minAppearances": 0, "maxResults": 50, "maxCostUsd": 25, "includeRawJson": false, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `dataset` (type: `string`): Dataset of Understat football analytics records. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "leagues": [ "EPL" ], "seasons": [ "2024" ], "teams": [], "matchIds": [], "playerIds": [], "maxResults": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("constructive_calm/understat-football-analytics").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "leagues": ["EPL"], "seasons": ["2024"], "teams": [], "matchIds": [], "playerIds": [], "maxResults": 50, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("constructive_calm/understat-football-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 '{ "leagues": [ "EPL" ], "seasons": [ "2024" ], "teams": [], "matchIds": [], "playerIds": [], "maxResults": 50, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call constructive_calm/understat-football-analytics --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=constructive_calm/understat-football-analytics", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Understat xG & Shot Data - Football Analytics", "description": "Scrapes Understat: player/team season aggregates, per-match xG/PPDA/win-probabilities, shot-level data with x/y coords + xG, situation/formation/game-state breakdowns, player career histories, and fixtures across the Big 5 + RFPL (2014–2025). Multi-season + multi-league in a single run.", "version": "1.0", "x-build-id": "7uMU5asDKlKZGVjLb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/constructive_calm~understat-football-analytics/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-constructive_calm-understat-football-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/constructive_calm~understat-football-analytics/runs": { "post": { "operationId": "runs-sync-constructive_calm-understat-football-analytics", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/constructive_calm~understat-football-analytics/run-sync": { "post": { "operationId": "run-sync-constructive_calm-understat-football-analytics", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "mode" ], "properties": { "mode": { "title": "Scraping mode", "enum": [ "league_players", "league_teams", "league_matches", "match_shots", "team_situations", "player_career", "fixtures" ], "type": "string", "description": "What to scrape. 'league_players' returns per-player season aggregates (xG, xA, xGChain, xGBuildup, etc). 'league_teams' returns team season aggregates (W/D/L, xG, xGA, PPDA, points, xPoints). 'league_matches' returns played-match metadata (xG, win-prob, deep, ppda). 'match_shots' returns every shot with x/y coords, xG, situation, body part, result. 'team_situations' returns long-format breakdowns by situation/formation/game-state/timing/zones. 'player_career' returns one player's full multi-season history. 'fixtures' returns upcoming matches.", "default": "league_players" }, "leagues": { "title": "Leagues", "type": "array", "description": "One or more of the six leagues Understat covers. Required for league_*, match_shots (when matchIds is empty), team_situations (when teams is empty), and fixtures.", "items": { "type": "string", "enum": [ "EPL", "La_liga", "Bundesliga", "Serie_A", "Ligue_1", "RFPL" ], "enumTitles": [ "Premier League (England)", "La Liga (Spain)", "Bundesliga (Germany)", "Serie A (Italy)", "Ligue 1 (France)", "Russian Premier League" ] }, "default": [] }, "seasons": { "title": "Seasons (start year)", "type": "array", "description": "List of season start years. 2024 = 2024/25 season. Understat history goes back to 2014 (the 2014/15 season). Multi-season is supported in a single run — pull 11 years of EPL in one go instead of 11 separate invocations.", "default": [], "items": { "type": "string" } }, "teams": { "title": "Teams (titles)", "type": "array", "description": "Team names exactly as Understat displays them — e.g. 'Arsenal', 'Manchester United', 'Real Madrid', 'Зенит'. Spaces become underscores in URLs automatically. Used by team_situations to filter to specific teams.", "default": [], "items": { "type": "string" } }, "matchIds": { "title": "Match IDs (skip enumeration)", "type": "array", "description": "Specific match IDs to scrape directly (used by match_shots). When set, leagues/seasons are ignored. Find IDs in Understat URLs: /match/{ID}.", "default": [], "items": { "type": "string" } }, "playerIds": { "title": "Player IDs", "type": "array", "description": "Player IDs for player_career mode. Find IDs in Understat URLs: /player/{ID}. Examples: 553 (Messi), 619 (Agüero), 1250 (Salah), 8260 (Haaland).", "default": [], "items": { "type": "string" } }, "positions": { "title": "Positions filter", "type": "array", "description": "Filter players by position. Only applies to league_players mode (uses Understat's filtered POST endpoint).", "items": { "type": "string", "enum": [ "GK", "D", "M", "F", "S", "Sub" ], "enumTitles": [ "Goalkeeper", "Defender", "Midfielder", "Forward", "Striker", "Substitute" ] }, "default": [] }, "nLastMatches": { "title": "Last N matches", "minimum": 0, "maximum": 50, "type": "integer", "description": "Filter player stats to the last N matches. 0 = full season. Only applies to league_players mode.", "default": 0 }, "dateStart": { "title": "Date from (YYYY-MM-DD)", "type": "string", "description": "Restrict league_players stats to matches on or after this date. Leave empty for none.", "default": "" }, "dateEnd": { "title": "Date to (YYYY-MM-DD)", "type": "string", "description": "Restrict league_players stats to matches on or before this date. Leave empty for none.", "default": "" }, "minAppearances": { "title": "Minimum appearances", "minimum": 0, "type": "integer", "description": "Drop player rows where games played is below this threshold (client-side filter). 0 = no filter.", "default": 0 }, "maxResults": { "title": "Max results", "minimum": 0, "maximum": 200000, "type": "integer", "description": "Stop emitting after this many dataset rows. 0 = unlimited (subject to maxCostUsd).", "default": 0 }, "maxCostUsd": { "title": "Max cost (USD per run)", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Hard cap on the run's total billed amount. The actor estimates cost up-front and stops gracefully if the cap is hit mid-run. Default $25 protects you from accidental high-volume runs (e.g. all 6 leagues × 11 seasons of shots ≈ $375).", "default": 25 }, "includeRawJson": { "title": "Include raw Understat JSON", "type": "boolean", "description": "When true, attach the original Understat record under `_raw` on every output row. Useful for power users / debugging. Default off — keeps the dataset light.", "default": false }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify proxy. Understat does not block scrapers, but residential proxies help users behind corporate networks. Default = Apify residential.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```