Every week, thousands of sports fans and analysts visit websites like TeamRankings.com to check the latest match results, betting spreads, and team statistics. But collecting and organizing that information manually can be a painfully slow process — copying tables, switching tabs, and double-checking data. It’s repetitive work that steals valuable time from actual analysis. That’s the problem I set out to solve.

I built a Web Scraping & Data Processing Project — an automation tool that collects match data, analyzes team rankings, and exports the results directly into Excel. With a single command, it gathers the latest sports data, organizes it, and presents it in a ready-to-use format. No more manual work, no more missed details — just clean, structured data delivered automatically.

The system works by connecting to TeamRankings.com, where it automatically navigates through the website, selects the matches you choose, and extracts important details such as team names, rankings, spreads, totals, and game statistics. Using Python libraries like Playwright, BeautifulSoup, and Pandas, the program scrapes the data, processes it into structured tables, and exports everything neatly into an Excel file. The user simply runs the script, selects match dates through a prompt, and waits for the data to be ready.

Behind the scenes, the project is made up of several interconnected scripts that work like an assembly line. The main.py script acts as the central controller, managing the workflow and saving results. The user_prompt.py file collects user input for match selection, while scraping_process.py uses Playwright and BeautifulSoup to extract the match details. The get_rank.py script retrieves team rankings, and create_excel.py handles all the data organization and file conversion, turning the raw information into a polished Excel spreadsheet. Once completed, the program automatically removes temporary files, leaving a clean workspace.

The entire system is powered by Python and relies on several key technologies: Playwright for automated browsing, BeautifulSoup for parsing web content, Pandas for data manipulation, and Requests for fetching additional information. These tools work together to transform raw web pages into structured, meaningful data outputs that anyone can analyze, even without technical knowledge.

This project is more than just a scraper — it’s an example of how automation can simplify the way we handle information. For analysts, journalists, or sports enthusiasts, this tool eliminates the repetitive work of collecting stats and lets them focus on understanding the story behind the numbers. Instead of spending hours copying and pasting data, users can now get complete match insights in just a few minutes.

In the future, I plan to extend the system with live match tracking, support for multiple sports and leagues, and real-time integration with dashboards like Google Sheets. Each addition brings the project closer to becoming a full-fledged sports data intelligence platform — one that can power predictions, insights, and analysis automatically.

I’m M. Ikhsan Arif, a data automation engineer who loves building tools that make work smarter and faster. My passion lies in designing systems that handle repetitive tasks so people can focus on what truly matters: thinking, analyzing, and creating. If your organization relies on data collection or processing, I can help you build a system that works for you — just like this one.

project's github: https://github.com/Ikhsan121/TeamRankingBasketball