I wanted a way to search sports stats that went beyond basic box scores, so I built Sports Stat Finder, a full-stack website for exploring historical and current data across the NBA, NFL, and WNBA. The core idea was a customizable search engine that lets users find individual game performances matching whatever statistical criteria they define, instead of combing through static tables or multiple sites.
I built the backend around a PostgreSQL database covering NBA, NFL, and WNBA seasons, and wrote Python pipelines to collect, clean, validate, and update the stats automatically. On top of that data I added filtering by season, team, opponent, player position, home/away, win/loss, game date, and playoff round, plus separate search modes for game, season, and career stats so users could analyze players and teams across different time windows. For NFL specifically, I added customizable fantasy football scoring with PPR, Half-PPR, Standard, and adjustable passing touchdown values.
On the frontend, I used React, TypeScript, and Tailwind CSS to build a responsive, mobile-friendly interface, and added features like search history and saved statistical combinations so users could quickly return to past queries. I deployed the full application on Railway, with PostgreSQL handling storage and backend queries.
The result is a working, deployed analytics tool spanning three major sports leagues, with a search system flexible enough to answer specific statistical questions that most mainstream sports sites don't support.
Here is the link: https://statfinder-nine.vercel.app/
