# Underdog Fantasy Prop Lines Scraper (`brilliant_gum/sports-props-aggregator`) Actor Scrape live player prop lines from Underdog Fantasy. Points, rebounds, assists, combo props, over/under odds, implied probability. Filter by player, team, sport, or prop type. NBA, NFL, MLB, NHL, soccer. - **URL**: https://apify.com/brilliant\_gum/sports-props-aggregator.md - **Developed by:** [Yuliia Kulakova](https://apify.com/brilliant_gum) (community) - **Categories:** Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.015 / prop line This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ![Underdog Fantasy Prop Lines Scraper](https://i.imgur.com/yChGH0C.png) ## Underdog Fantasy Prop Lines Scraper **Pull every player prop line from Underdog Fantasy in seconds — points, rebounds, assists, combo props, odds, implied probability, and more. Filter by player, team, sport, or prop type. No API key required.** Whether you're line shopping, building a model, or setting up automated alerts — this actor gives you clean, structured data ready to use. --- ### What You Get Every record includes: | Field | Description | |---|---| | `playerName` | Full player name | | `team` / `opponent` | Team abbreviations, e.g. `LAL`, `OKC` | | `gameVenue` | `"home"` or `"away"` | | `sport` | `NBA`, `NFL`, `MLB`, `NHL`, `SOCCER` | | `propType` | Normalized stat key: `points`, `rebounds`, `pts_rebs_asts`, `passing_yards`, etc. | | `propCategory` | Broad category: `scoring`, `defensive`, `passing`, `rush_receiving`, `pitching`, `hitting` | | `line` | The over/under threshold | | `overOdds` / `underOdds` | American odds for each side | | `impliedProbability` | Win probability derived from odds (0–1) | | `payoutMultiplier` | Per-leg payout multiplier | | `gameDate` / `gameTime` | When the game tips off | | `isLive` | Whether the game is in progress | | `boostedMultiplier` | `true` when the over payout is boosted (contrarian value signal) | | `scrapedAt` | Timestamp of the scrape | --- ### Example Output ```json { "platform": "underdog", "playerName": "LeBron James", "team": "LAL", "opponent": "OKC", "gameVenue": "away", "sport": "NBA", "propType": "pts_rebs_asts", "propCategory": "scoring", "line": 35.5, "overOdds": -118, "underOdds": -105, "impliedProbability": 0.5413, "payoutMultiplier": 1, "gameDate": "2026-05-08", "gameTime": "2026-05-08T01:30:00Z", "isLive": false, "boostedMultiplier": false, "scrapedAt": "2026-05-08T12:00:00.000Z" } ```` *** ### Input Options | Parameter | Type | Default | Description | |---|---|---|---| | `sports` | string\[] | all | Sports to include: `NBA`, `NFL`, `MLB`, `NHL`, `SOCCER` | | `propTypes` | string\[] | all | Filter by prop type, e.g. `["points", "rebounds", "passing_yards"]` | | `playerNames` | string\[] | all | Filter to specific players, e.g. `["LeBron James", "Stephen Curry"]` | | `teams` | string\[] | all | Filter by team abbreviation, e.g. `["LAL", "GSW"]` | | `minLine` | number | `0` | Only return props where line ≥ this value | | `maxLine` | number | none | Only return props where line ≤ this value | | `onlyLiveGames` | boolean | `false` | Only return props for games currently in progress | | `includeInjuredPlayers` | boolean | `false` | When `false`, props for Questionable / GTD / Out players are excluded | | `outputFormat` | enum | `"per-prop"` | `"per-prop"` — one flat record per line (best for spreadsheets). `"per-player"` — one record per player with a nested `props` array (best for player-centric analysis) | *** ### Use Cases **Line Shopping** — pull all available props before your slate and see exact odds for each side before locking in. **Model Building** — schedule the actor to run every 15–30 minutes and accumulate timestamped snapshots. Track how lines move around injury news and game time. **Automated Alerts** — connect to Apify webhooks and trigger notifications when a player you follow has a new prop posted. **Injury-Aware Filtering** — set `includeInjuredPlayers: false` (default) to automatically skip props for players listed as Questionable, GTD, or Out. **Per-Player Grouping** — use `outputFormat: "per-player"` to get one clean record per player with all their props nested — perfect for building slips or player cards. *** ### Supported Prop Types **NBA** — `points`, `rebounds`, `assists`, `threes_made`, `free_throws_made`, `steals`, `blocks`, `turnovers`, `pts_rebs_asts`, `pts_rebs`, `pts_asts`, `rebs_asts`, `points_rebounds`, `fg_attempted` **NFL** — `passing_yards`, `rushing_yards`, `receiving_yards`, `passing_tds`, `receptions`, `interceptions`, `rush_rec_yards` **MLB** — `hits`, `home_runs`, `rbi`, `runs_scored`, `strikeouts`, `pitcher_strikeouts`, `walks`, `stolen_bases`, `total_bases` **NHL** — `goals`, `shots_on_goal`, `saves` **SOCCER** — `shots`, `shots_on_target` # Actor input Schema ## `platforms` (type: `array`): Which DFS platforms to scrape. Currently Underdog Fantasy is fully supported. PrizePicks and DraftKings may return 0 results due to their bot protection. ## `sports` (type: `array`): Which sports to include. Defaults to all supported sports. ## `propTypes` (type: `array`): Filter by specific prop types. Leave empty for all prop types. ## `playerNames` (type: `array`): Filter results to specific player names (case-insensitive). Leave empty for all players. ## `teams` (type: `array`): Filter results to specific team abbreviations (e.g. LAL, GSW). Leave empty for all teams. ## `onlyLiveGames` (type: `boolean`): If true, only return props for games currently in progress. ## `minLine` (type: `number`): Only include props where the line value is at or above this number. ## `maxLine` (type: `number`): Only include props where the line value is at or below this number. Leave empty for no upper limit. ## `includeInjuredPlayers` (type: `boolean`): When false (default), props for players listed as Questionable, GTD, or Out are excluded. Set to true to include all props regardless of injury status. ## `outputFormat` (type: `string`): Controls how results are grouped. 'per-prop' (default) returns one record per prop line — the flat format best for spreadsheets and databases. 'per-player' groups all props for a player into a single record with a nested 'props' array — useful for player-centric analysis. ## Actor input object example ```json { "platforms": [ "underdog" ], "sports": [ "NBA", "NFL" ], "propTypes": [ "points", "rebounds", "assists", "passing_yards", "rushing_yards" ], "playerNames": [ "LeBron James", "Stephen Curry" ], "teams": [ "LAL", "GSW", "BOS" ], "onlyLiveGames": false, "minLine": 10, "maxLine": 50, "includeInjuredPlayers": false, "outputFormat": "per-prop" } ``` # 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 = { "platforms": [ "underdog" ], "sports": [ "NBA" ] }; // Run the Actor and wait for it to finish const run = await client.actor("brilliant_gum/sports-props-aggregator").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 = { "platforms": ["underdog"], "sports": ["NBA"], } # Run the Actor and wait for it to finish run = client.actor("brilliant_gum/sports-props-aggregator").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 '{ "platforms": [ "underdog" ], "sports": [ "NBA" ] }' | apify call brilliant_gum/sports-props-aggregator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=brilliant_gum/sports-props-aggregator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Underdog Fantasy Prop Lines Scraper", "description": "Scrape live player prop lines from Underdog Fantasy. Points, rebounds, assists, combo props, over/under odds, implied probability. Filter by player, team, sport, or prop type. NBA, NFL, MLB, NHL, soccer.", "version": "1.0", "x-build-id": "hTSAJBjPmEv4THra6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/brilliant_gum~sports-props-aggregator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-brilliant_gum-sports-props-aggregator", "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/brilliant_gum~sports-props-aggregator/runs": { "post": { "operationId": "runs-sync-brilliant_gum-sports-props-aggregator", "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/brilliant_gum~sports-props-aggregator/run-sync": { "post": { "operationId": "run-sync-brilliant_gum-sports-props-aggregator", "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": { "platforms": { "title": "Platforms", "type": "array", "description": "Which DFS platforms to scrape. Currently Underdog Fantasy is fully supported. PrizePicks and DraftKings may return 0 results due to their bot protection.", "items": { "type": "string", "enum": [ "prizepicks", "underdog", "draftkings" ] }, "default": [ "underdog" ] }, "sports": { "title": "Sports", "type": "array", "description": "Which sports to include. Defaults to all supported sports.", "items": { "type": "string", "enum": [ "NBA", "NFL", "MLB", "NHL", "SOCCER" ] }, "default": [ "NBA", "NFL", "MLB", "NHL", "SOCCER" ] }, "propTypes": { "title": "Prop Types", "type": "array", "description": "Filter by specific prop types. Leave empty for all prop types.", "items": { "type": "string" }, "default": [] }, "playerNames": { "title": "Player Names", "type": "array", "description": "Filter results to specific player names (case-insensitive). Leave empty for all players.", "items": { "type": "string" }, "default": [] }, "teams": { "title": "Teams", "type": "array", "description": "Filter results to specific team abbreviations (e.g. LAL, GSW). Leave empty for all teams.", "items": { "type": "string" }, "default": [] }, "onlyLiveGames": { "title": "Only Live Games", "type": "boolean", "description": "If true, only return props for games currently in progress.", "default": false }, "minLine": { "title": "Minimum Line Value", "minimum": 0, "type": "number", "description": "Only include props where the line value is at or above this number.", "default": 0 }, "maxLine": { "title": "Maximum Line Value", "type": "number", "description": "Only include props where the line value is at or below this number. Leave empty for no upper limit." }, "includeInjuredPlayers": { "title": "Include Injured / Questionable Players", "type": "boolean", "description": "When false (default), props for players listed as Questionable, GTD, or Out are excluded. Set to true to include all props regardless of injury status.", "default": false }, "outputFormat": { "title": "Output Format", "enum": [ "per-prop", "per-player" ], "type": "string", "description": "Controls how results are grouped. 'per-prop' (default) returns one record per prop line — the flat format best for spreadsheets and databases. 'per-player' groups all props for a player into a single record with a nested 'props' array — useful for player-centric analysis.", "default": "per-prop" } } }, "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 } } } } } } } } } } ```