Unleashing the Power of Sports Data: Use the right analytical tools for the right job

In the world of sports data analysis, using the right tools for the right job can transform raw information into valuable, actionable insights. Here’s a glimpse into the workflow and tools that I commonly use for collecting, automating, storing, and presenting data. In this example, the final aim is to present data on sports-related companies worldwide for sports investment analysis.

1. Python for Web Scraping

The journey begins with data collection, where Python plays a crucial role. Python modules allow us to (responsibly) scrape sports investment data from various websites, gathering details about companies, their backgrounds, and essential investor information such as financial analysis, updates, and valuations. Although R packages can also be used for web scraping, I prefer the Python modules (I find the coding easier). Depending on the specific requirements of each website, a combination of Selenium, Selectolax, MechanicalSoup, and BeautifulSoup can be employed. For extensive scraping tasks, asynchronous or concurrent programming can dramatically increase efficiency and speed up the process – I like to use Asyncio or concurrent.futures modules for this purpose.

Specific to the example in this article, I also used the geopy package to obtain latitudes and longitudes for the PowerBI mapping visual in the final stage of the workflow

2. GitHub Actions for Automation

Once the Python code is written and tested, maintaining up-to-date data becomes a priority. For some websites, data scraping can occur every one or two weeks. However, data extraction can happen daily for news updates or newsletters. GitHub Actions, with its generous free allocation of 2000 minutes per month, is my go-to tool for automating these updates. It ensures that data remains current without manual intervention, streamlining the workflow.

3. MySQL for tabular Data Storage

With data continuously updated, the next step is efficient storage. Data can often be stored in a MySQL database, with Aiven’s MySQL being my preferred free option for tabular data. Storing data in a structured format not only ensures its integrity but also facilitates easy access for analysis.

4. Power BI for Visualisation

Finally, the most exciting part is reached – visualizing data to extract meaningful insights. Power BI is an exceptional tool for this purpose, allowing seamless connection to CSV files stored in the GitHub repository or directly to the MySQL database. Power BI’s Power Query feature provides an intuitive GUI to clean and transform data, making it even easier to prepare the data for analysis. And who wants to do more coding anyway !!!! ?? While many of these tasks can be performed in Python, Power Query’s user-friendly interface can speed up the process.

Conclusion

In summary, this approach combines various tools to deliver valuable data insights:

1. Python for web scraping – Efficiently gather the sports investment data.

2. GitHub Actions for automation – Keep data up-to-date with minimal effort.

3. MySQL database for storage – Maintain data integrity and accessibility.

4. Power BI for visualization – Easily transform data into actionable insights.

Many tools are available for each job, and a sports data analyst must have a range of "tools" in their locker for different tasks. Different skills are needed for each step, such as knowledge of coding, web scraping, SQL, and data visualization. By harnessing the power of these tools, informed decisions can be made, helping savvy sports investors stay ahead in the competitive world of sports investments.

Stay tuned! Free sports-related datasets will soon be shared on a GitHub repository along with FREE Power BI tutorials on a new website. Keep an eye out for updates! ??

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