Python Scraping Footballer.com.tw Data for Automated Football Gear Monitoring

In the football industry, professional gear plays a crucial role in enhancing a player's performance. Footballer.com.tw is a well-known Taiwanese retailer specializing in football gear, offering products from major brands like Desporte, Nike, Adidas, and Mizuno. However, popular items often sell out quickly due to high demand.

To automate stock monitoring, track price trends, and conduct competitive analysis, we can leverage Python along with LuckData Sneaker API to efficiently scrape Footballer.com.tw data. This article presents a comprehensive solution along with implementation details.

1. Why Scrape Footballer.com.tw Data?

Footballer.com.tw primarily sells football boots, protective gear, and accessories from top brands. For football enthusiasts, staying ahead of stock availability and price changes is crucial. Through automated data monitoring, we can:

✅ Track price fluctuations — Detect discounts and promotions.

✅ Monitor stock status — Get notified when products are restocked.

✅ Build a historical price database — Analyze long-term pricing trends.

✅ Compare prices across different platforms — Ensure the best possible deal.

2. Understanding LuckData Sneaker API

LuckData Sneaker API is a powerful tool designed for retrieving sneaker and sports gear data from multiple e-commerce platforms, including Footballer.com.tw, DreamSport, Footlocker, and Musinsa. By simply providing a product URL, we can retrieve comprehensive product details such as name, price, stock status, and images.

LuckData API offers different subscription plans to suit various needs:

How to Get Started:

1. Register an account on LuckData's website.

2. Access the API Key from the dashboard.

3. Choose a subscription plan.

3. Making an API Request to Scrape Footballer.com.tw Data

The basic API request format is as follows:

https://luckdata.io/api/sneaker-API/get_4ce2?url=<Product_URL>

To fetch data, we send a GET request with the API Key in the request headers.

Python Implementation

Below is an example script to scrape the Desporte Sao Luis SI II DS-1436 football boots from Footballer.com.tw:

import requests
# API Key
API_KEY = "your_api_key"
# Target product URL
TARGET_URL = "https://footballer.com.tw/collections/desporte/products/desporte-sao-luis-si-Ⅱ-ds-1436"
# API request URL
API_ENDPOINT = f"https://luckdata.io/api/sneaker-API/get_4ce2?url={TARGET_URL}"
# Request headers
headers = {
'X-Luckdata-Api-Key': API_KEY
}
# Sending request
response = requests.get(API_ENDPOINT, headers=headers)
# Handling response
if response.status_code == 200:
data = response.json()
print(data)  # Process the retrieved data
else:
print(f"Request failed, status code: {response.status_code}")

4. Parsing the API Response

The API returns product details in JSON format, similar to the example below:

{
"name": "Desporte Sao Luis SI II DS-1436",
"price": "$2,880",
"stock": "In Stock",
"image": "https://footballer.com.tw/path/to/image.jpg",
"url": "https://footballer.com.tw/collections/desporte/products/desporte-sao-luis-si-Ⅱ-ds-1436"
}

We can extract key details in Python:

data = response.json()
product_name = data.get("name")
product_price = data.get("price")
product_stock = data.get("stock")
product_image = data.get("image")
print(f"Product Name: {product_name}")
print(f"Price: {product_price}")
print(f"Stock Status: {product_stock}")
print(f"Image URL: {product_image}")

5. Advanced Applications: Bulk Scraping & Data Storage

1. Scraping Multiple Products at Once

We can create a product list and iterate through it to scrape multiple items:

product_urls = [
"https://footballer.com.tw/collections/desporte/products/product1",
"https://footballer.com.tw/collections/adidas/products/product2",
"https://footballer.com.tw/collections/nike/products/product3"
]
for url in product_urls:
API_ENDPOINT = f"https://luckdata.io/api/sneaker-API/get_4ce2?url={url}"
response = requests.get(API_ENDPOINT, headers=headers)
if response.status_code == 200:
print(response.json())  # Process data

2. Storing Data in CSV for Analysis

We can use pandas to store scraped data in a CSV file for further analysis:

import pandas as pd
data_list = [
{"name": "Desporte Sao Luis SI II", "price": "$2,880", "stock": "In Stock"},
{"name": "Nike Mercurial Vapor 15", "price": "$5,200", "stock": "Out of Stock"}
]
df = pd.DataFrame(data_list)
df.to_csv("footballer_data.csv", index=False)

This allows us to analyze data in Excel or visualize trends using data analysis tools.

6. Best Practices for Web Scraping

When scraping data, it is important to follow ethical guidelines to ensure responsible usage:

✅ Avoid excessive requests — Implement time.sleep(1) between requests to prevent getting blocked.

✅ Check website policies — Review Footballer.com.tw's robots.txt file to ensure compliance.

✅ Handle API errors properly — Log errors and retry failed requests if necessary.

7. Conclusion

By leveraging LuckData API, we can efficiently scrape product data from Footballer.com.tw, enabling price monitoring, stock tracking, data storage, and competitive analysis. This not only helps consumers make better purchasing decisions but also provides valuable market insights for businesses.