How to Web Scrape Football Statistics from VREF in Under 5 Minutes

Web scraping is a powerful technique for extracting data from websites. This guide demonstrates how to scrape football statistics tables from VREF, a website containing extensive data on various football leagues.

Getting Started with Web Scraping

Web scraping involves extracting data from websites for analysis or storage. In this case, we’ll focus on scraping two specific tables from VREF: the Score Stats table and the Open Stats table.

Tools Required

For this tutorial, we’ll be using:

• Google Colab – A collaborative notebook environment similar to Jupyter Notebook
• Pandas – A Python library for data manipulation and analysis

Step-by-Step Process

1. Import Required Libraries

First, import the pandas library:

import pandas as pd

2. Specify the URL

Define the URL of the webpage containing the tables you want to scrape:

URL = "[website URL goes here]"

3. Read Tables from the URL

Use pandas’ read_html function to extract all tables from the webpage:

tables = pd.read_html(URL)

4. Check How Many Tables Are Present

Display the number of tables found in the URL:

len(tables)

In this case, we found two tables on the page.

5. View the Tables

Display the first table (Score Stats):

print(tables[0])

And the second table (Open Stats):

print(tables[1])

6. Store Tables in DataFrames

Assign each table to a named DataFrame for easier manipulation:

df = tables[0]  # Score Stats table

df1 = tables[1]  # Open Stats table

7. View the First Few Rows

Check the first five rows of each table to confirm the data looks correct:

df.head()

df1.head()

8. Save the Data to CSV Files

Export the tables to CSV files for future use:

df.to_csv("scored_stats.csv")

df1.to_csv("open_stats.csv")

9. Download the CSV Files

Once saved, you can download the files from Google Colab by clicking on the file name in the file browser.

Conclusion

Web scraping football statistics from VREF is a straightforward process using pandas and Google Colab. This approach allows you to quickly extract valuable data for analysis without manual copying. The entire process takes less than five minutes once you understand the steps.

With these CSV files, you now have football statistics data ready for analysis, visualization, or any other data science project you may have in mind.