Industry: Sports Analytics / Betting & Fantasy Markets
Developed for sports data providers, fantasy platforms, and scouting agencies requiring high-velocity aggregation of player metrics across the NFL, NHL, NBA, and international leagues.
Problem
Fragmented athletic data across disparate league sites and third-party APIs makes real-time analysis nearly impossible.
Data Silos: Each league uses proprietary card layouts and data structures, requiring bespoke extraction logic.
The Velocity Gap: Player stats, injury statuses, and roster moves change by the minute, outfacing manual database updates.
Parsing Fragility: Standard scrapers break when league sites update their DOM or implement anti-bot measures.
Analytical Overhead: Raw HTML is useless for decision-making; it requires structured normalization before it can feed a model.
Solution
A high-throughput Python ecosystem that transforms unstructured player "cards" into a query-ready PostgreSQL intelligence layer.
Recursive Roster Crawling: Engineered a systematic crawler that traverses league hierarchies—from conference to team to individual player cards—ensuring zero data leakage.
Schema Normalization: Built a robust parsing engine using BeautifulSoup to map heterogeneous data points (e.g., NBA "Rebounds" vs. NHL "Save %") into a unified relational database.
Automated Sync Pipeline: Implemented a cron-driven update cycle that detects roster changes in real-time, keeping the database in sync with live league movements.
Relational Scalability: Optimized a PostgreSQL backend to handle complex multi-league queries, supporting simultaneous access for analytics, journalism, and fan-facing apps.