I’m a Statistics & Data Science and Computer Science student at Connecticut College and a sports analytics builder focused on turning raw data into tools teams can actually use. My work combines statistical modeling, software engineering, and on-ice perspective: I’m the author/maintainer of nhlscraper, an R package for collecting, cleaning, modeling, and visualizing NHL/ESPN data; I’ve built expected-goals models, interactive dashboards, and D3 visualizations to study shot quality, player value, and tactical decision-making; and I’ve applied that toolkit professionally as a Stats R&D Intern at NHL. I’m especially interested in hockey data infrastructure, probabilistic modeling, player evaluation, and decision tools that help move analysis from “what happened?” to “what should we do next?”

I created and maintain nhlscraper, an R package that makes NHL and ESPN data more accessible by scraping, cleaning, and analyzing data from 125+ API endpoints. Since publishing it on CRAN, the package has surpassed 5,000 downloads, been added to the SportsAnalytics CRAN Task View, and appeared in academic papers and course materials. I also reverse-engineered more than 50 undocumented NHL EDGE endpoints and built native C routines that accelerate play-by-play and shift-processing workflows by up to 167× while preserving reliable R fallbacks.

I built and deployed Rento’s Rink, a Python and Streamlit NHL analytics platform used by more than 1,000 people to explore skater and goalie shot maps, compare player and team performance through xG-based rankings, evaluate free agents, and generate contract scenarios. Behind the interface, I developed multi-season R data pipelines and a leakage-controlled, six-game-state xG system that selects between XGBoost and LightGBM models, alongside contract models trained on 5,394 historical deals using 337 engineered features and achieving an average held-out error of 0.61 percentage points of the salary cap.

NBAxP is a project where I turned raw NBA shot data into an interactive “shot value map” for each team. I scraped and cleaned ~700,000 shots, built an expected-points model using shot context, and visualized results by court region in a D3-powered dashboard with interactive filters and hover tooltips for team-to-team comparisons.

I built an end-to-end R pipeline combining AHL player-tracking data with XGBoost and LightGBM models to analyze established 5-on-4 offensive-zone play. I developed Attempted Exploitable Mismatch per State (AEM/state), a coaching-focused metric that measures whether power-play units recognize and attack high-value openings. Across 32 teams, AEM/state correlated with scoring at r = 0.442 and increased team-level explanatory R² from 0.281 to 0.346 beyond xG alone, while revealing actionable puck-movement patterns associated with creating mismatches.