# Mikatiming Marathon Results Scraper (`jungle_synthesizer/baa-mikatiming-marathon-results-scraper`) Actor Scrape finisher results and split times from Boston Marathon and other mikatiming-powered races. Extracts placement, finish times, 5K–40K splits, and runner demographics for pacing analysis and coaching tools. - **URL**: https://apify.com/jungle\_synthesizer/baa-mikatiming-marathon-results-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Sports, Education - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 ## Mikatiming Marathon Results Scraper Scrape finisher results and split times from Boston Marathon and other mikatiming-powered races. Extracts placement, finish times, 5K–40K splits, and runner demographics — the full dataset used by pacing analytics and coaching tools. ### What it scrapes The mikatiming platform powers race results for several major marathons: - **Boston Marathon** — `results.baa.org` - **Berlin Marathon** — `results.scc-events.com` (same platform) - **Tokyo Marathon** and other Abbott WMM events using mikatiming infrastructure For each finisher, the scraper collects: | Field | Description | |-------|-------------| | `race_name` | Race name including year | | `race_year` | Integer year | | `bib` | Runner bib number | | `runner_name` | Full name (Last, First) | | `age_group` | Age division (e.g. 18-39, 40-44) | | `state` | Runner home state/province | | `club` | Running club or team | | `finisher_place_overall` | Overall finish placement | | `finisher_place_gender` | Placement within gender division | | `finisher_place_age_group` | Placement within age group division | | `net_time` | Chip time (HH:MM:SS) | | `gross_time` | Gun time (HH:MM:SS) | | `5k_split` through `40k_split` | Cumulative split times at each checkpoint | | `half_split` | Half marathon split (21.1K) | | `pace_per_km` | Average pace (min/mile as reported by mikatiming) | ### Use cases - **Pacing analytics** — full 5K-through-40K split ladders for training plan modeling - **Coaching SaaS** — historical finishing data for runners by age group and event - **Race research** — compare performance across years, divisions, and events - **Academic sports science** — population-level marathon finishing data ### Input | Parameter | Type | Description | |-----------|------|-------------| | `baseUrl` | string | Base URL of the mikatiming results site (e.g. `https://results.baa.org/2024/`) | | `eventId` | string | Mikatiming event identifier from the URL's `?event=` parameter (e.g. `2024_boston`) | | `maxItems` | integer | Maximum finisher records to scrape. Set 0 for all finishers. Default: 10 | #### Boston Marathon example ```json { "baseUrl": "https://results.baa.org/2024/", "eventId": "2024_boston", "maxItems": 100 } ```` The `eventId` value appears in the site URL when you navigate to the results list: `?event=2024_boston`. ### How it works The scraper uses a two-level hierarchical crawl: 1. **Listing pages** — iterates the finisher list at `?pid=list&page=N` (25 runners per page, sequential pagination) 2. **Detail pages** — fetches each runner's `?content=detail&idp=` page to extract the full dataset including all splits The site is server-rendered HTML. No JavaScript execution required. Polite crawling at 2 concurrent requests with automatic rate-limit backoff. ### Notes - The BAA results page does not expose gender, city, or country fields for all runners — these fields will be null for entries where the data is not published. - Split times are cumulative from gun start (standard marathon split convention). - The `pace_per_km` field actually reports min/mile (as labeled by mikatiming) — the field name reflects the spec; the values are min/mile. - Boston 2024 has approximately 25,500 finishers across 1,022 listing pages. Use `maxItems` to cap output during testing. # 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. ## `baseUrl` (type: `string`): Base URL for the mikatiming race results site (e.g. https://results.baa.org/2024/ for Boston 2024) ## `eventId` (type: `string`): Mikatiming event identifier (e.g. 2024\_boston). Found in the URL: ?event= ## `maxItems` (type: `integer`): Maximum number of finisher records to scrape (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...", "baseUrl": "https://results.baa.org/2024/", "eventId": "2024_boston", "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...", "baseUrl": "https://results.baa.org/2024/", "eventId": "2024_boston", "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/baa-mikatiming-marathon-results-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...", "baseUrl": "https://results.baa.org/2024/", "eventId": "2024_boston", "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/baa-mikatiming-marathon-results-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...", "baseUrl": "https://results.baa.org/2024/", "eventId": "2024_boston", "maxItems": 10 }' | apify call jungle_synthesizer/baa-mikatiming-marathon-results-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/baa-mikatiming-marathon-results-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Mikatiming Marathon Results Scraper", "description": "Scrape finisher results and split times from Boston Marathon and other mikatiming-powered races. Extracts placement, finish times, 5K–40K splits, and runner demographics for pacing analysis and coaching tools.", "version": "0.1", "x-build-id": "SNo0xYxaL5Px3D9tQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~baa-mikatiming-marathon-results-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-baa-mikatiming-marathon-results-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~baa-mikatiming-marathon-results-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-baa-mikatiming-marathon-results-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~baa-mikatiming-marathon-results-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-baa-mikatiming-marathon-results-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": [ "baseUrl", "eventId", "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." }, "baseUrl": { "title": "Race Results Base URL", "type": "string", "description": "Base URL for the mikatiming race results site (e.g. https://results.baa.org/2024/ for Boston 2024)" }, "eventId": { "title": "Event ID", "type": "string", "description": "Mikatiming event identifier (e.g. 2024_boston). Found in the URL: ?event=" }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of finisher records to scrape (0 = unlimited)", "default": 10 } } }, "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 } } } } } } } } } } ```