# DFS Multi-Source Player Props Scraper (`crawloop/dfs-multi-source-props-scraper`) Actor Scrape PrizePicks, Betr, DraftKings Pick6, and Underdog player props in one run. Normalized schema, comparison\_key joins, and line spread dataset. Crawloop DFS Props Suite. - **URL**: https://apify.com/crawloop/dfs-multi-source-props-scraper.md - **Developed by:** [Andrej Kiva](https://apify.com/crawloop) (community) - **Categories:** Developer tools, Automation, Integrations - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.25 / 1,000 results 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 ## DFS Multi-Source Player Props Scraper — Cross-Platform Lines > **Crawloop DFS Player Props Suite** — Real-time player props, over/under lines, and odds from US daily fantasy sports (DFS) pick'em platforms. Built for odds-comparison tools, sports analytics pipelines, and research dashboards. > > Suite hub: [github.com/PLCSPS-DEV/dfs-player-props](https://github.com/PLCSPS-DEV/dfs-player-props) > > Product site: [crawloop.com/sports-data](https://crawloop.com/sports-data.html) | PrizePicks | Betr Picks | DraftKings Pick6 | Underdog Fantasy | Multi-Source | | :--- | :--- | :--- | :--- | :--- | | [Player Props Scraper](https://apify.com/crawloop/prizepicks-player-props-scraper?fpr=guboir) | [Picks Scraper](https://apify.com/crawloop/betr-picks-scraper?fpr=guboir) | [Pick6 Scraper](https://apify.com/crawloop/draftkings-pick6-scraper?fpr=guboir) | [Player Props Scraper](https://apify.com/crawloop/underdog-player-props?fpr=guboir) | **Props Board** ◄── you are here | > **Disclaimer:** These are unofficial integrations developed independently of PrizePicks, Betr, DraftKings, Underdog Fantasy, or any of their subsidiaries. They are not affiliated with, sponsored by, or endorsed by those companies. > > Platform names are trademarks of their respective owners. Data is read from publicly accessible web sources only. > > This Actor is provided **for informational and research purposes only**. It does not provide betting advice. You are solely responsible for ensuring your use complies with applicable laws, platform terms of use, and your organization's policies. > > No warranty is given as to accuracy, completeness, or continued availability of third-party data. Use at your own risk. One Apify run → **PrizePicks**, **Betr Picks**, **DraftKings Pick6**, and **Underdog Fantasy** in a single normalized dataset with shared schema, `comparison_key` for cross-platform joins, and an optional **comparisons** dataset with side-by-side line spreads. Need only one platform? Use the cheaper single-source scrapers linked above. ### When to use this Actor Use the **Multi-Source Board** when you need cross-platform line shopping, gap analysis, or a unified props feed for a dashboard — not when monitoring a single book at minimum cost. For PrizePicks-only demon/goblin tiers or Underdog-only odds metadata, use the dedicated scrapers. This Actor reuses the same proven collectors as the standalone actors with merge, enrichment, and comparison logic on top. ### DFS Props Pipeline ```` Phase 1 — Single-platform boards Phase 2 — Cross-platform compare ──────────────────────────────── ───────────────────────────────── PrizePicks Scraper ───────────────────┐ Betr Picks Scraper ───────────────────┤ Pick6 Scraper ───────────────────┼──► Multi-Source Board ◄── you are here Underdog Scraper ───────────────────┘ │ ▼ comparisons dataset line\_spread, source\_count ```` ### Key Features - **Four platforms** — `prizepicks`, `betr-picks`, `draftkings-pick6`, `underdog`. - **Unified schema** — every row includes `source`, `player_name`, `line`, `market`, `comparison_key`. - **Comparison dataset** — optional output with `line_prizepicks`, `line_betr`, `line_pick6`, `line_underdog`, `line_spread`. - **Matchup enrichment** — Underdog props inherit matchup context from PrizePicks/Betr when the board omits teams. - **Partial success** — if one source fails, successful sources still land in the dataset. - **Proven collectors** — same Betr/Pick6/PrizePicks/Underdog logic as standalone actors. ### Input Parameters | Parameter | Description | Default | | :--- | :--- | :--- | | `sources` | Platforms to scrape. | all four | | `leagues` | Leagues across all sources (MLB, NBA, All). | `["MLB"]` | | `playerNames` | Comma-separated names. Applied after merge. | — | | `statTypes` | Comma-separated stat types or market labels. | — | | `teams` | Comma-separated team names or abbreviations. | — | | `outputComparisons` | Push side-by-side rows to comparisons dataset. | `true` | | `minComparisonSources` | Min platforms required for a comparison row. | `2` | | `maxProjectionsPerSource` | Max raw records per platform. | `10000` | | `maxTotalProjections` | Max rows in merged dataset. | `30000` | | `collectionMode` | `parallel` (faster) or `sequential` (lower memory). | `"parallel"` | | `includePartialResults` | Keep rows when some sources fail. | `true` | | `proxyConfiguration` | US residential proxy recommended. | Apify RESIDENTIAL US | #### Input Example ```json { "sources": ["prizepicks", "betr-picks", "draftkings-pick6", "underdog"], "leagues": ["MLB"], "outputComparisons": true, "minComparisonSources": 2, "maxProjectionsPerSource": 10000, "collectionMode": "parallel", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US" } } ```` ### Output Format — Props Dataset ```json { "source": "betr-picks", "player_name": "Juan Soto", "market": "player_total_bases", "market_display": "Total Bases", "line": 1.5, "side": "over", "league": "MLB", "event_name": "NYM @ PHI", "comparison_key": "mlb|juansoto|player_total_bases|nym@phi", "collected_at": "2026-06-21T12:00:00+00:00" } ``` ### Output Format — Comparisons Dataset When `outputComparisons` is true, a second dataset named **comparisons** is populated: ```json { "comparison_key": "mlb|juansoto|player_total_bases|nym@phi", "player_name": "Juan Soto", "market": "player_total_bases", "market_display": "Total Bases", "matchup": "nym@phi", "source_count": 3, "sources": ["betr-picks", "prizepicks", "underdog"], "line_prizepicks": 4.5, "line_betr": 1.5, "line_underdog": 1.5, "line_pick6": null, "line_min": 1.5, "line_max": 4.5, "line_spread": 3.0, "collected_at": "2026-06-21T12:00:00+00:00" } ``` ### OUTPUT\_SUMMARY Each run stores a key-value summary with per-source status, timing, and cross-platform metrics: | Field | Meaning | | :--- | :--- | | `multiSourceKeys` | Props found on 2+ platforms | | `multiSourceKeys3Plus` | Props found on 3+ platforms | | `comparisonRows` | Rows pushed to comparisons dataset | | `matchupsEnriched` | Props that received inferred matchups | ### Typical Workflow **1. Full slate snapshot** Run all four sources with `leagues: ["MLB"]` and `outputComparisons: true`. **2. Filter wide spreads** Export comparisons dataset; filter where `line_spread >= 1.0` and `source_count >= 3`. **3. Schedule during game windows** Trigger via Apify scheduler or API every 5–15 minutes. Pull dataset via webhook or `apify-client`. ### Actor Comparison | Task | Multi-Source | Single-source scrapers | | :--- | :---: | :---: | | All platforms in one run | Yes | No | | Lowest cost per platform | No | Yes | | `comparison_key` joins | Yes | Manual | | Line spread dataset | Yes | No | | Partial source failure tolerance | Yes | N/A | ### Pricing Multi-source runs use more compute and proxy bandwidth than single-source scrapers. | Event | Price | | :--- | :--- | | Actor start | **$0.15** per run | | Prop row | **$0.08 / 1,000** | | Comparison row | **$0.20 / 1,000** | ### Limitations - **Slate coverage varies** — Pick6 may feature a different slate than PrizePicks/Betr. - **Underdog is slowest** — CloakBrowser adds 15–30s; use `collectionMode: "sequential"` to reduce peak memory. - **Matchup enrichment** — inferred from player/league index when a source omits team metadata. - **Sleeper** — not supported (account-gated). ### API Integration ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("crawloop/dfs-multi-source-props-scraper").call( run_input={ "sources": ["prizepicks", "betr-picks", "underdog"], "leagues": ["NBA"], "outputComparisons": True, } ) props = client.dataset(run["defaultDatasetId"]).iterate_items() comparisons = client.dataset(run["defaultKeyValueStoreId"]) # use comparisons dataset ID from run meta ``` *** **Learn more:** [Suite hub](https://crawloop.com/sports-data.html) · [GitHub docs](https://github.com/PLCSPS-DEV/dfs-player-props) **Also from Crawloop:** [Industrial Automation Suite](https://crawloop.com/industrial-automation.html) # Actor input Schema ## `sources` (type: `array`): Platforms to scrape in this run. Choose one for a single book or all for a full cross-platform snapshot. ## `leagues` (type: `array`): Leagues to collect across all selected sources. Use sport abbreviations like MLB, NBA, NFL, or All. ## `playerNames` (type: `string`): Comma-separated player names. Case-insensitive, partial matching across all sources. ## `statTypes` (type: `string`): Comma-separated stat types or market labels (Total Bases, Strikeouts, PTS). Applied after collection. ## `teams` (type: `string`): Comma-separated team names or abbreviations. Matches either side of the matchup. ## `maxProjectionsPerSource` (type: `integer`): Maximum raw records to collect from each platform before merge and filters. ## `maxTotalProjections` (type: `integer`): Maximum records in the final merged dataset. ## `collectionMode` (type: `string`): Run source collectors in parallel (faster) or sequentially (lower peak memory). ## `includePartialResults` (type: `boolean`): If one source fails, still output records from successful sources. ## `failOnAllSourcesFailed` (type: `boolean`): Mark the run as failed when every selected source fails or returns zero records. ## `outputComparisons` (type: `boolean`): Push side-by-side cross-platform line rows to the named comparisons dataset. ## `minComparisonSources` (type: `integer`): Only emit comparison rows when at least this many platforms share the same comparison\_key. ## `prizePicksStateCode` (type: `string`): US state code used by PrizePicks public middleware fallback. ## `underdogBoardUrls` (type: `array`): Optional direct Underdog JSON board URLs. When empty, Underdog uses CloakBrowser automation (slower, no manual setup). ## `underdogRequestHeaders` (type: `object`): Optional headers for Underdog direct board URL mode. ## `underdogMaxPages` (type: `integer`): Maximum paginated API pages to fetch for Underdog browser/direct collection. ## `proxyConfiguration` (type: `object`): US residential proxy is strongly recommended for all sources. ## Actor input object example ```json { "sources": [ "prizepicks", "betr-picks", "draftkings-pick6", "underdog" ], "leagues": [ "MLB", "NBA", "NFL", "WNBA", "NHL", "All" ], "playerNames": "", "statTypes": "", "teams": "", "maxProjectionsPerSource": 10000, "maxTotalProjections": 30000, "collectionMode": "parallel", "includePartialResults": true, "failOnAllSourcesFailed": true, "outputComparisons": true, "minComparisonSources": 2, "prizePicksStateCode": "GA", "underdogBoardUrls": [], "underdogRequestHeaders": {}, "underdogMaxPages": 25, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } ``` # 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 = { "sources": [ "prizepicks", "betr-picks", "draftkings-pick6", "underdog" ], "leagues": [ "MLB", "NBA", "NFL", "WNBA", "NHL", "All" ] }; // Run the Actor and wait for it to finish const run = await client.actor("crawloop/dfs-multi-source-props-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 = { "sources": [ "prizepicks", "betr-picks", "draftkings-pick6", "underdog", ], "leagues": [ "MLB", "NBA", "NFL", "WNBA", "NHL", "All", ], } # Run the Actor and wait for it to finish run = client.actor("crawloop/dfs-multi-source-props-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 '{ "sources": [ "prizepicks", "betr-picks", "draftkings-pick6", "underdog" ], "leagues": [ "MLB", "NBA", "NFL", "WNBA", "NHL", "All" ] }' | apify call crawloop/dfs-multi-source-props-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crawloop/dfs-multi-source-props-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "DFS Multi-Source Player Props Scraper", "description": "Scrape PrizePicks, Betr, DraftKings Pick6, and Underdog player props in one run. Normalized schema, comparison_key joins, and line spread dataset. Crawloop DFS Props Suite.", "version": "0.6", "x-build-id": "7d5aC3UId0eOp0jzM" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crawloop~dfs-multi-source-props-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crawloop-dfs-multi-source-props-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crawloop~dfs-multi-source-props-scraper/runs": { "post": { "operationId": "runs-sync-crawloop-dfs-multi-source-props-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crawloop~dfs-multi-source-props-scraper/run-sync": { "post": { "operationId": "run-sync-crawloop-dfs-multi-source-props-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "sources": { "title": "Sources", "type": "array", "description": "Platforms to scrape in this run. Choose one for a single book or all for a full cross-platform snapshot.", "default": [ "prizepicks", "betr-picks", "draftkings-pick6", "underdog" ], "items": { "type": "string" } }, "leagues": { "title": "Leagues", "type": "array", "description": "Leagues to collect across all selected sources. Use sport abbreviations like MLB, NBA, NFL, or All.", "default": [ "MLB" ], "items": { "type": "string" } }, "playerNames": { "title": "Player Names", "type": "string", "description": "Comma-separated player names. Case-insensitive, partial matching across all sources.", "default": "" }, "statTypes": { "title": "Stat Types", "type": "string", "description": "Comma-separated stat types or market labels (Total Bases, Strikeouts, PTS). Applied after collection.", "default": "" }, "teams": { "title": "Teams", "type": "string", "description": "Comma-separated team names or abbreviations. Matches either side of the matchup.", "default": "" }, "maxProjectionsPerSource": { "title": "Max Projections Per Source", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Maximum raw records to collect from each platform before merge and filters.", "default": 10000 }, "maxTotalProjections": { "title": "Max Total Projections", "minimum": 1, "maximum": 200000, "type": "integer", "description": "Maximum records in the final merged dataset.", "default": 30000 }, "collectionMode": { "title": "Collection Mode", "enum": [ "parallel", "sequential" ], "type": "string", "description": "Run source collectors in parallel (faster) or sequentially (lower peak memory).", "default": "parallel" }, "includePartialResults": { "title": "Include Partial Results", "type": "boolean", "description": "If one source fails, still output records from successful sources.", "default": true }, "failOnAllSourcesFailed": { "title": "Fail When All Sources Fail", "type": "boolean", "description": "Mark the run as failed when every selected source fails or returns zero records.", "default": true }, "outputComparisons": { "title": "Output Comparisons Dataset", "type": "boolean", "description": "Push side-by-side cross-platform line rows to the named comparisons dataset.", "default": true }, "minComparisonSources": { "title": "Minimum Sources Per Comparison Row", "minimum": 2, "maximum": 4, "type": "integer", "description": "Only emit comparison rows when at least this many platforms share the same comparison_key.", "default": 2 }, "prizePicksStateCode": { "title": "PrizePicks State Code", "type": "string", "description": "US state code used by PrizePicks public middleware fallback.", "default": "GA" }, "underdogBoardUrls": { "title": "Underdog Board URLs", "type": "array", "description": "Optional direct Underdog JSON board URLs. When empty, Underdog uses CloakBrowser automation (slower, no manual setup).", "default": [], "items": { "type": "string" } }, "underdogRequestHeaders": { "title": "Underdog Request Headers", "type": "object", "description": "Optional headers for Underdog direct board URL mode.", "default": {} }, "underdogMaxPages": { "title": "Underdog Max Pages", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum paginated API pages to fetch for Underdog browser/direct collection.", "default": 25 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "US residential proxy is strongly recommended for all sources.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } } }, "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 } } } } } } } } } } ```