# LeTrot Scraper (`lexis-solutions/letrot-com-scraper`) Actor
Scrape trotting racing data from letrot.com - including race schedules, horse profiles, odds, results, and track details. Ideal for betting analysis, performance tracking, and market research. Fast, structured, and customizable extraction from France’s official harness racing site.
- **URL**: https://apify.com/lexis-solutions/letrot-com-scraper.md
- **Developed by:** [Lexis Solutions](https://apify.com/lexis-solutions) (community)
- **Categories:** AI, Automation, Developer tools
- **Stats:** 36 total users, 4 monthly users, 100.0% runs succeeded, 2 bookmarks
- **User rating**: No ratings yet
## Pricing
$28.00/month + usage
To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors
## 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
### 🎯 What does LeTrot Scraper do
LeTrot Scraper helps you extract horse racing data and results from **LeTrot.com**, France's premier online platform for trotting horse racing information and results.
You can extract detailed race information including race titles, times, status, and complete race results with horse positions, times, and other key details from the website. LeTrot Scraper supports REST API which gives you access to the extracted dataset, enables you to download it in various formats and use it in other applications.
### 📊 What data can I extract from LeTrot with a web scraper
With this web scraping tool, you can extract the following data from LeTrot:
| 🏇 Race title |
⏰ Race time |
| 📊 Race status |
🏆 Race results |
| 🐎 Horse positions |
⏱️ Finish times |
| 🔗 Race URL |
📅 Race date |
| 📋 Complete race tables |
🏁 Race details |
### 🚀 Why scrape LeTrot.com
LeTrot.com is France's leading platform for trotting horse racing information, providing comprehensive data on races, results, and statistics. Scraping LeTrot.com can help you:
- Track race results and performance data
- Monitor horse racing statistics and trends
- Analyze betting patterns and odds
- Research historical race data
- Make informed betting decisions
- Monitor race schedules and upcoming events
- Understand racing performance metrics
- Track jockey and trainer statistics
### 🛠️ How to use LeTrot Scraper
LeTrot Scraper is designed for easy and fast start even if you've never extracted data from websites before. Here's how you can extract data from LeTrot:
1. Create a free Apify account using your email
2. Open LeTrot Scraper on the Apify platform
3. Click on the **Try for free** button
4. Enter your search parameters or race date
5. Click on the **Start** button and wait for the data to be extracted
6. Download your data in JSON, XML, CSV, Excel, or HTML
### ⚙️ Input
The actor accepts the following input parameters:
- `raceByDate` (string) - Date to scrape races from (default: today)
- `maxItems` (integer) - Maximum number of races to scrape (default: 10)
Supported date formats:
- `"today"` - Scrape today's races
- `"yesterday"` - Scrape yesterday's races
- `"tomorrow"` - Scrape tomorrow's races
- `"2025-01-15"` - Scrape races for a specific date (YYYY-MM-DD format)
Example:
```json
{
"raceByDate": "today",
"maxItems": 20
}
````
### 📤 Output
The scraped data will be saved as a dataset. Each item will represent a race with its results. You can download your data in various formats: JSON, JSONL, HTML table, CSV, Excel spreadsheet, or NDJSON.
Example of the output format:
```json
{
"url": "https://www.letrot.com/courses/2025-01-15/race-1",
"title": "Prix de la Ville de Paris",
"hour": "14:30",
"status": "Terminée",
"results": [
{
"Rang": "1",
"N°": "1",
"ChevalCrack series au Partant": "JUNKY DE CHENU3188",
"SA": "H6",
"DriverEntraîneur": "E. RAFFIN P. BOUTIN",
"PropriétaireÉleveur": "P. BOUTINF. PELLEROT",
"Distance": "2 500",
"Temps": "3'03\"1",
"Red. Km": "1'13\"2",
"Allocation €": "9 000 €",
"Rap. probables": "2"
},
{
"Rang": "2",
"N°": "8",
"ChevalCrack series au Partant": "JUST WINNER ROC4830",
"SA": "H6",
"DriverEntraîneur": "B. LE BELLER B. LE BELLER",
"PropriétaireÉleveur": "O. SIMONETTIC. ROSZAK",
"Distance": "2 500",
"Temps": "3'03\"2",
"Red. Km": "1'13\"3",
"Allocation €": "5 000 €",
"Rap. probables": "47"
}
]
}
```
### ⚠️ Notes and Limitations
- The actor relies on the structure of the LeTrot website. If the website structure changes, the actor may need to be updated
- Ensure you comply with the terms of service of LeTrot.com when using this actor
- For large-scale scraping, use Apify's proxy services to avoid IP blocking
- The scraper uses residential proxies from France to ensure reliable access
- Respect rate limits to avoid overwhelming the website
- The scraper is optimized for French trotting races and may not work for other racing types
### 🔗 Need to scrape other racing platforms
Check out our other marketplace scrapers:
- [2dehands Scraper](https://apify.com/lexis-solutions/2dehands-be-scraper)
- [BizBuySell Scraper](https://apify.com/lexis-solutions/bizubuysell)
### 🛠️ Want something custom-built
This LeTrot Scraper doesn't exactly do what you need? You can always build your own! Lexis Solutions is a [certified Apify Partner](https://apify.com/partners/find). We can help you with custom solutions or data extraction projects.
Contact us over [Email](mailto:scraping@lexis.solutions) or [LinkedIn](https://www.linkedin.com/company/lexis-solutions)
### Support Our Work 💝
If you're happy with our work and scrapers, you're welcome to leave us a company review [here](https://apify.com/partners/find/lexis-solutions/review) and leave a review for the scrapers you're subscribed to. It will take you less than a minute but it will mean a lot to us!
# Actor input Schema
## `raceByDate` (type: `string`):
The date of the race to crawl.
## `maxItems` (type: `integer`):
The maximum number items to crawl.
## Actor input object example
```json
{
"raceByDate": "today",
"maxItems": 5
}
```
# 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 = {
"raceByDate": "today",
"maxItems": 5
};
// Run the Actor and wait for it to finish
const run = await client.actor("lexis-solutions/letrot-com-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 = {
"raceByDate": "today",
"maxItems": 5,
}
# Run the Actor and wait for it to finish
run = client.actor("lexis-solutions/letrot-com-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 '{
"raceByDate": "today",
"maxItems": 5
}' |
apify call lexis-solutions/letrot-com-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=lexis-solutions/letrot-com-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "LeTrot Scraper",
"description": "Scrape trotting racing data from letrot.com - including race schedules, horse profiles, odds, results, and track details. Ideal for betting analysis, performance tracking, and market research. Fast, structured, and customizable extraction from France’s official harness racing site.",
"version": "0.0",
"x-build-id": "b3tkSeBNiTDxpAkbW"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/lexis-solutions~letrot-com-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-lexis-solutions-letrot-com-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": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/lexis-solutions~letrot-com-scraper/runs": {
"post": {
"operationId": "runs-sync-lexis-solutions-letrot-com-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/lexis-solutions~letrot-com-scraper/run-sync": {
"post": {
"operationId": "run-sync-lexis-solutions-letrot-com-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$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": [
"raceByDate"
],
"properties": {
"raceByDate": {
"title": "Race by date",
"type": "string",
"description": "The date of the race to crawl."
},
"maxItems": {
"title": "Max items",
"type": "integer",
"description": "The maximum number items to crawl."
}
}
},
"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
}
}
}
}
}
}
}
}
}
}
```