Scraping Sports Data 2022: Why & How

Scraping sports data has become significant since sports is all about statistics and data itself. From team analysis to each athlete’s performance, there is data on everything. The Internet is full of every kind of data be it a sports website or social media. Sports data changes almost every day with new match results and player statistics.

Imagine if you are asked to collect all of the sports data manually. That would be so time-consuming and error-prone. You might also have to limit the amount of data to be gathered. That’s where web scraping tools and techniques are helpful. Web scraping is the methodology of extracting data from websites. 

Why Scrape Sports Data?

Managers, Coaches, and marketers can benefit from scraped sports data in many ways. It will facilitate a good strategy, winning team composition, marketing and positive social impact. It might also be beneficial for those in sports betting.

The recipe for winning in sports is hard work, competent players, good strategy and some good luck. But how do you decide which player would be better for the team? Which strategy would make your team win?

You should start strategizing the game long before. Decision-based on the data is the key to success. It goes for decisions of green field sports too. Coaches and managers need to have data in hand to analyze. Which player would complement the other player? Which players might prove to be a winning combination?

Instead of picking players on the basis of gut feeling and star factor. Making a team based on data can make work easier for national selectors. They can also choose backup players on the basis of data for different pitches and opponents.

Why is web scraping the easiest way to collect sports data?

Web scraping uses bots to extract, gather, and organize big data from websites. For anyone who wants to analyze sports information. Sports websites are a great source of data. Web scraping i.e. automated collection of data is a great option. Since it is an efficient way of gathering data from various sources. It will reduce the pressure of manual work. You will get your data fast too.

By using scraping tools, sports information can be organized according to the season, team, league or even players. Professional sports managers now have departments dedicated to statistical analysis. The more the data, the better will be the analysis. This data is great for making databases, improving performances and analyzing trends.

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What sports data can you scrape?

You can scrape sports data like 

For Cricket

• Number of wins
• Wickets taken
• Batting average
• Bowler type
• Catches by the wicket-keeper
• The average speed of balls

For Football

• Number of goals scored
• Goals saved
• Percentage of Ball possession
• Interceptions
• The ratio of shots etc

Based on the sports, the team manager can study different metrics of data to make a winning team.

Where to find sports data?

Firstly you need to know the purpose of scraping sports data. Is it performance analysis or market value analysis? 

You can scrape sports data from websites like NFL.com, and FIFA.com. From credible Sports news websites like BBC sports or Sky Sports. You can also get sports-related data from social media platforms and players’ websites.

All of these sources contain valuable information about sports. 

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Fantasy League

Today, we live in a world of fantasy leagues. Betting started as a hobby for sports fans. Fanatics would choose to create a dream team with the hope of a good game. These picks were usually based on instincts and have often failed. 

In this realm, sports data plays an important role. Especially if you are in a place where sports betting is legal. Using a web scraping tool can be really helpful. You will be able to gather and organize stats faster. Since betting is not just about gambling anymore. These stats will help you study and bet on the strongest fantasy team. 

Best Website Scraping Tools

Every website has a different format, design and structure. This means that every website would need a different functionality for scraping data. This is why there are automated and specialized tools available on the internet for use. They all come with free plans and paid plans offering discounts on annual subscriptions. 

These tools have made web scraping very easy and convenient. These web scrapers facilitate you with a set of tools that let you create different web crawlers. All these web crawlers have pre-defined instructions for different types of websites.

There are two types of scraping tools available.  

• Scraping libraries or framework
• Pre-built software 

This pre-built software also includes web browser extensions for Google Chrome or Firefox. This pre-built software or extension is very user-friendly. These tools do not require any coding knowledge. You can directly download them from the websites and install them in your systems. 

Some of the scraping software and extensions are

• Web Scraper (Chrome extension)
• Data Scraper (Chrome extension)
• Octoparse
• Import.io
• Parsehub

However, these easy-to-use tools might have some limitations.

Scraping libraries and frameworks are capable of performing advanced scraping. They would require intermediate or advanced programmers. These scrapings are based on programming languages. Scraping frameworks like BeautifulSoup, Scrapy or Selenium can be easily installed. They are mostly based on Python, as it is considered to be one of the easiest coding languages.

Some examples of open-source scraping frameworks are:

• BeautifulSoup
• Selenium
• Scrapy

Conclusion

Web scraping is a legal practice if it is performed ethically and responsibly. Automated scraping tools can be a powerful support for people who want to collect sports data. It is an easy, efficient and cost-effective way. Sports data scraped from credible websites can be used for building databases, creating news aggregators or statistical analysis. 

Since there are many sports websites on the internet. Popular sports websites usually have the best data. So it is better to choose them for sports data scraping.