How to Build a Football Dataset with Web Scraping

How to Build a Football Dataset with Web Scraping

Using Selenium to scrape JavaScript rendered content. This article will cover the scraping of JavaScript rendered content with Selenium using the Premier League website as an example and scraping the stats of every match in the 2019/20 season.

When scraping a website with Python using libraries such as BeautifulSouprequests, or _urllib _it’s common to have some trouble accessing some parts of the website. That's because these parts are generated on the client-side, using JavaScript, which these libraries can’t handle.

To deal with this problem, using Selenium can be an interesting option. Selenium works by opening an automated browser and then it’s capable of accessing the entire content and of interacting with the page.

This article will cover the scraping of JavaScript rendered content with Selenium using the Premier League website as an example and scraping the stats of every match in the 2019/20 season.

Understanding the Website

The Premier League website makes the scraping of multiples matches pretty simple with its very straight forward URLs. The URL for a match consists basically of “https://www.premierleague.com/match/” followed by a unique match ID.

Each ID consists of a number and the IDs for all matches of each season are sequenced. For instance, the entire 2019/20 season goes from 46605 to 46984. All we need to do then is to loop through this interval and collect the data from each match.

We’ll use Liverpool 5 to 3 win over Chelsea as an example in this article. This game ID is 46968. You can type this ID after “premierleague.com/match/” to go to the page so you can follow along with the scraping process that will be described in the article. Refer back to the page always that necessary.

python selenium data web-scraping dataset data-science

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