Build a Football Dataset With Web Scraping

Anyone who has an interest in football and data knows that there is a lot of football data on the web. But what if you want to aggregate those data and build your own football dataset? If so, web scraping may be the solution for you.

Web scraping can help you obtain various data about football matches and football players, thereby allowing you to understand the sport better. 

In this article, we will share some valuable tips on how to build a football dataset with web scraping. So, without further ado, let’s get started.

1. Get All the Necessary Tools for Web Scraping

Web scraping can be tedious and time-consuming, but with the right tools, you can automate many repetitive aspects. Additionally, you’ll have more time to focus on collecting the data you need. 

So, here are some essential tools you need to access on your computer to get started with web scraping:

  • A Web Browser: This is where you’ll do most of your scraping. Firefox and Chrome are excellent options as they are the fastest browsers available. 
  • An HTTP Client: This will help you make requests to websites and get responses back. The Urllib3 is a popular option.
  • An HTTP Proxy: It will hide your IP address when performing web scraping on any website. This is a secure method to ensure the URL you operate on doesn’t block or ban your IP. You will find several risk-free free proxies available on the internet.
  • A Web Scraping Tool: This will make the entire scraping process easier. There are several web scraping tools under Python web drivers, such as Selenium, BeautifulSoup, Scrapy, etc. You can use these tools to run the scraping process much more efficiently.

2. Establish a Plan for the Data You Want to Extract

Before beginning to scrape data from the web, it is crucial to establish a plan for the data that you want to extract.

And to ensure that you extract the most relevant data, consider what you want to use the dataset for. For example, are you looking to track player performance? Analyze game strategies or predict outcomes?

Once you have a clear idea of the purpose of the dataset, you can then determine the specific data points you need to extract.

Without a plan, it will be challenging to ensure that you collect the data you need, and you may end up with a dataset that does not lend itself well to your ultimate aims. 

3. Understand the Website’s Structure

Before beginning to scrape a website for data, it is essential first to understand the structure of the website. By doing so, you’ll be able to locate and obtain the required data.

For example, if you want to build a dataset of football statistics, you would need to know where the data is located on the website and how it is organized.

The website’s structure can be analyzed by inspecting the source code and determining the organization of the data.

Once you understand the website’s structure, you can begin writing the code to scrape the data.

4. Choose Your Target Website Carefully

It is a crucial step to follow when you want to build a football dataset with web scraping. That’s because you’ll be relying on the website structure you’re scraping. If the website decides to change its form, your scraper will likely break.

For example, let’s say you’re scraping data from a website that lists football fixtures. If the website changes how it displays fixtures (e.g., from a list to a calendar), your scraper will no longer work.

This is why it’s crucial to choose a target website that is unlikely to change its structure. An excellent way to do this is to select a website maintained by an organization (i.e., a league or channel), as they are less likely to make significant changes to their websites.

5. Write Clean and Readable Code

You need to input clean and readable code to web scrape effectively into the web scraping tool. Otherwise, the scraper will be unable to deliver the desired results you want for your football dataset.

Furthermore, it will make maintaining and updating the dataset easier as new data becomes available.

Therefore, the following considerations should be made when writing code for web scraping:

  • The code ought to be clear and well-organized.
  • The code ought to be modular, so it can be reused for other projects.
  • The code should be efficient to run quickly and without errors.

By adhering to these principles, you can ensure that your code is clear, readable, and reusable.

6. Handle Errors Gracefully

It is essential to handle errors gracefully to ensure that the data collected is accurate and reliable.

There are several ways to do this, but some standard methods include using try-except statements and logging errors.

Try except statements allow you to catch and handle errors during web scraping while logging mistakes ensures that you can track and debug any issues.

7. Be Prepared for Rate Limits

Rate limits are a way for websites to limit the amount of data a single user can access in a given period. You may be blocked from accessing the data if you exceed the rate limit.

One way to circumvent rate limits is using a web scraping platform to distribute the scraping load across multiple machines. This will allow you to obtain the needed data without being rate-limited promptly.

You can also use free proxy services to change your IP address. 

8. Don’t Forget to Check for Updates

When building a football dataset with web scraping, it is essential to remember to check for updates. This is because data on the web can change frequently, and you want to ensure that your dataset is up to date.

To do this, you can either manually check for updates or set up a script to automatically check for updates.

9. Make Sure Your Data is Complete

Ensure that your data is complete. This means you should scrape data from as many sources as possible, including traditional and non-traditional sources.

For example, you should scrape data from official NFL and fan websites. 

Furthermore, you must scrape other relevant data to obtain all necessary information. This includes basic information like player names and statistics and more detailed information like game logs and player contracts.

You can build a more accurate and comprehensive dataset by ensuring that your data is complete.

10. Save Data in JSON or CSV Format

JSON and CSV are standard data storage formats that are easy to work with various programming languages. It also contributes to the data’s usability and ease of manipulation for analysis.

Therefore, try to save data in one of these formats when building a football dataset with web scraping.

Importance of Creating Football Dataset with Web Scraping 

As the popularity of football has increased, so has the need for accurate and up-to-date football data, especially for those with a keen financial or professional interest in the sport. 

With the right data, analysts can accurately predict player and team performance, identify trends, and make recommendations for game strategy.

Understanding these things allows teams to adjust their strategy and tactics to increase their chances of winning.


The internet has a wealth of data waiting to be scraped and used for analysis. For football fans, this data can be used to build a dataset of player statistics, team standings, and more.

So, whether you’re a diehard football fan or just a casual observer, use these valuable data and build a football dataset with web scraping.

Throughout this article, we have already elaborated on how to collect data and build a football dataset with web scraping.

If it still seems challenging, there are plenty of easy-to-run web scrapers for you to try.


  • Can you scrape data from ESPN?

Yes, you can scrape data from ESPN. The process is not complex, and the benefits are numerous. If you are looking for a way to get detailed information on your favorite sports teams and players, scraping ESPN is a great option.

  • How do you web scrape sports stats?

To scrape the sports stats, below are the steps that one may follow,

Step 1: Get the desired website URL pasted on one of the web scrapper tools that can collect HTML data.

Step 2: Scrape statistics from every page, so no data gets missed.

Step 3: Configure Google Sheets to use the ParseHub API. It will store all the collected data in one place.

  • Is sports web scraping copyright infringement?

Scraping data from sports websites may seem like a harmless way to get information, but it can be considered copyright infringement.

Although not all sports website scraping is considered infringement, only a few sports websites have strict policies against scraping. And if you violate these policies, it can lead to legal action.

Thus, if you’re considering scraping data from a sports website, check the website policy on scraping or have permission from the site first.


Sumit is a Tech and Gadget freak and loves writing about Android and iOS, his favourite past time is playing video games.

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