# MLB Bovada Picks Analyzer (`dashing_excitement/mlb-bovada-picks-analyzer`) Actor Scrape live MLB betting lines from Bovada and get instant picks analysis. Detects value bets across Moneyline, Run Line, and Totals markets. Returns implied probability, true probability, value %, and RECOMMENDED picks per game. - **URL**: https://apify.com/dashing\_excitement/mlb-bovada-picks-analyzer.md - **Developed by:** [gustavo pulgar](https://apify.com/dashing_excitement) (community) - **Categories:** News, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $25.00 / 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 ## MLB Bovada Picks Analyzer ⚾ Extract real-time MLB betting lines from Bovada and get instant picks analysis with value bet detection — all in one automated run. ### What does this Actor do? This Actor navigates Bovada's MLB section using a headless browser, intercepts the live betting API responses, and analyzes each game across three markets: - **Moneyline (ML)** — Who wins the game - **Run Line (RL)** — Spread betting with -1.5 / +1.5 runs - **Total (Over/Under)** — Combined runs scored For each game it calculates the **implied probability**, removes the bookmaker's juice (vig), estimates the **true probability**, and computes a **value percentage** for each side. Picks with value above your configured threshold are flagged as **RECOMMENDED**. ### Key Features - ✅ Scrapes live Bovada MLB lines (updated in real time) - ✅ Analyzes Moneyline, Run Line, and Total markets per game - ✅ Removes vig and calculates true implied probabilities - ✅ Flags value bets above a configurable threshold - ✅ Outputs confidence level (HIGH / MEDIUM / LOW) per game - ✅ Produces a top picks summary at the end of each run - ✅ Exports data as JSON, CSV, or via API ### How to use 1. Click **Try for free** and run the Actor with default settings 2. The Actor will scrape today's MLB games from Bovada (usually 10–20 games per day) 3. Review the **Output** tab for full picks analysis 4. Export results to JSON, CSV, Google Sheets, or connect via API ### Input parameters | Parameter | Type | Default | Description | |---|---|---|---| | `maxGames` | integer | 20 | Maximum number of MLB games to analyze | | `includeAnalysis` | boolean | true | Generate picks analysis for each game | | `minValueThreshold` | integer | 5 | Minimum value % to flag a pick as RECOMMENDED | ### Output format Each game record in the dataset contains: ```json { "gameId": "25257514", "description": "Kansas City Royals @ Minnesota Twins", "startTime": "6/6/2026, 12:10:00 p.m.", "competitors": [ { "name": "Kansas City Royals", "home": false }, { "name": "Minnesota Twins", "home": true } ], "odds": { "moneyline": { "Minnesota Twins": { "price": "-165", "decimal": "1.606" } }, "runLine": { "Minnesota Twins": { "handicap": "-1.5", "price": "+145" } }, "total": { "Over": { "handicap": "8.5", "price": "-115" } } }, "analysis": { "game": "Kansas City Royals @ Minnesota Twins", "picks": [ { "tipo": "Moneyline (ML)", "pick": "Minnesota Twins", "odds": "-165", "probImplicita": "62.3%", "valor": "-6.3%", "recomendacion": "NEUTRAL", "razonamiento": "Local favorito. Odds: -165. Prob. implicita: 62.3%" } ], "recommendation": "Pick destacado: Total (O/U)", "confidence": "MEDIA" }, "source": "Bovada", "scrapedAt": "2026-06-06T20:09:20.000Z" } ```` ### Use cases - **Sports bettors** looking for daily MLB value bet suggestions - **Analysts** tracking line movements and market efficiency - **Developers** building sports betting dashboards or bots - **Researchers** studying bookmaker pricing models in baseball ### Notes & Disclaimer > This tool is for **informational and analytical purposes only**. The value percentages and recommendations generated by this Actor are based on mathematical analysis of the betting lines, not predictions of game outcomes. Sports betting involves risk — always bet responsibly within your limits and in compliance with local laws. The author is not responsible for any financial losses. ### Frequently asked questions **How often should I run this Actor?** Run it once per day, ideally 1–2 hours before the first pitch of the day when lines are most stable. **Does it work for playoffs and postseason?** Yes, as long as Bovada has MLB events listed, the Actor will scrape them. **Can I schedule it to run automatically?** Yes! Use Apify's built-in Scheduler to run this Actor every morning automatically. **What if no games are found?** During the MLB off-season or on days with no scheduled games, the Actor will return 0 results. This is expected behavior. # Actor input Schema ## `maxGames` (type: `integer`): Numero maximo de juegos MLB a analizar. Por defecto 20. ## `includeAnalysis` (type: `boolean`): Si es true, genera analisis de picks con value bets y recomendaciones para cada juego. ## `minValueThreshold` (type: `integer`): Porcentaje minimo de valor para recomendar un pick. Picks con valor menor se marcan como NEUTRAL. ## Actor input object example ```json { "maxGames": 20, "includeAnalysis": true, "minValueThreshold": 5 } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("dashing_excitement/mlb-bovada-picks-analyzer").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("dashing_excitement/mlb-bovada-picks-analyzer").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 dashing_excitement/mlb-bovada-picks-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dashing_excitement/mlb-bovada-picks-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MLB Bovada Picks Analyzer", "description": "Scrape live MLB betting lines from Bovada and get instant picks analysis. Detects value bets across Moneyline, Run Line, and Totals markets. Returns implied probability, true probability, value %, and RECOMMENDED picks per game.", "version": "0.0", "x-build-id": "qX0JXcW8EIQZlcVoo" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dashing_excitement~mlb-bovada-picks-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dashing_excitement-mlb-bovada-picks-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dashing_excitement~mlb-bovada-picks-analyzer/runs": { "post": { "operationId": "runs-sync-dashing_excitement-mlb-bovada-picks-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dashing_excitement~mlb-bovada-picks-analyzer/run-sync": { "post": { "operationId": "run-sync-dashing_excitement-mlb-bovada-picks-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "maxGames": { "title": "Maximo de juegos a analizar", "minimum": 1, "maximum": 100, "type": "integer", "description": "Numero maximo de juegos MLB a analizar. Por defecto 20.", "default": 20 }, "includeAnalysis": { "title": "Incluir analisis de picks", "type": "boolean", "description": "Si es true, genera analisis de picks con value bets y recomendaciones para cada juego.", "default": true }, "minValueThreshold": { "title": "Umbral minimo de valor (%)", "minimum": 0, "maximum": 50, "type": "integer", "description": "Porcentaje minimo de valor para recomendar un pick. Picks con valor menor se marcan como NEUTRAL.", "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 } } } } } } } } } } ```