Artifacts
I built NBA Stat Auction, an interactive web app that challenges users to build the strongest possible NBA lineup while staying within a $150 budget. Players are priced based on their combined points, rebounds, and assists per game, so putting together a winning roster means weighing performance against cost rather than just picking star names. I designed the core rules around real roster construction: each team needs 2 guards, 2 forwards, and 1 center, and I built out team rating calculations that score lineups on scoring, playmaking, rebounding, defense, and overall balance. To keep the game fresh, I added multiple modes — Classic, Historic, Unlimited, and Daily — including a Historic mode that pulls in players from past NBA seasons alongside current stars, so users can build dream teams across eras. Beyond single-lineup building, I added features that let users compare their team against other possible combinations to find the optimal lineup, and a leaderboard system so people can compete on daily scores against each other. I also implemented Google sign-in so users can save their lineups and results and come back to previous teams later. The result is a full game that combines basketball analytics with strategic decision-making, turning stat-based player evaluation into a competitive, replayable experience with daily challenges and historical team building.
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/
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