🏀 CourtViz
Welcome to my full-stack sports analytics site. Built with Next.js, Supabase, and Fly.io.
📊 About CourtViz
CourtViz was created as a sports analytics project to deliver powerful insights across all levels of sports and competition. The first step in building this platform was launching a full-stack website to showcase my ability to own the entire analytics pipeline — from back-end data collection and storage to front-end interactive visuals.
This site highlights features like dynamic player dashboards and a Player Finder tool that helps identify undervalued players, as well as uncover potential strengths and weaknesses based on data-driven metrics. As CourtViz continues to grow, the vision is to expand its scope across multiple sports while refining tools that support performance analysis, roster construction, and scouting.
👋 About Me
My name is Brian Papiernik — a sports data scientist with experience across baseball, basketball, and multi-sport performance analysis. I’ve worked as a Baseball Technology Operator for the Tampa Bay Rays, a Baseball Student Manager with Notre Dame Baseball, and hold a Master’s degree in Sports Analytics from Notre Dame. I specialize in building end-to-end analytics pipelines, predictive models, and interactive tools for evaluating players and strategies. CourtViz is where I bring together my passion for sports, data, and clean design.
🛠 Former Portfolio Projects
- March Madness Simulation Model — Predicted tournament outcomes using ShotQuality, Torvik, and Elo-based features.
- MLB Pitch Clustering Project — Used k-means to group pitch shapes and identify optimal pitch sequencing strategies.
- NBA Injury Prevention Model — Leveraged tracking data and play context to detect movement patterns that increase injury risk.
- College Basketball Game Outcome Probabilities — Modeled game win probabilities using Torvik and ShotQuality metrics, while accounting for transfer portal impact and team-level volatility.
- Heliocentric Snapshot Model — Evaluated offensive decision-making and shot distribution by comparing missed shot outcomes with catch-and-shoot opportunities for teammates based on tracking data.