Scraping for NIL Opportunities
Description on how Python web scraper was built to find name, image, and likeness opportunities to be verified and displayed to the dashboard.
To find name, image, and likeness opportunities, on3 (https://www.on3.com/nil/deals/), a reference for recently completed NIL deals was scraped. This script uses the following Python libraries to scrape specified HTML tags revealing companies that offer engagements to athletes:
requests - allows a user to input an HTTP request and receive an HTML file
Beautiful Soup - Python library that utilizes an HTML parser to read and return tags.
json - to export important tags to JSON, then verify and append to front-end/
Web Scraping Algorithm for NIL Opportunities
After searching for company information using chrome DevTools, use https://www.on3.com/nil/deals/ to return all <h5> tags from the page containing company information.
Request URL, then Beautiful Soup & the associated HTML parser return all <h5> tags: the companies that have recently committed to name, image, and likeness engagements.
Save the company names from the <h5> tags to JSON formatting.
Verify the associated company is still offering NIL opportunities
Render to front-end!
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