Technical Manager and former Naval Officer
A progressive BJJ training application that accelerates skill acquisition through spaced repetition (SM-2 algorithm) and evidence-based mental visualization techniques. Combines physical training logs, guided 5-minute visualization sessions, smart scheduling, research protocols (nutrition, cardio, strength), and comprehensive progress tracking.
A comprehensive analytical framework for evaluating NFL quarterback performance and contract decisions through advanced statistical modeling and era-adjusted metrics. Combines systematic data collection from Pro Football Reference with ridge regression analysis to reveal organizational biases in quarterback evaluation and predict contract outcomes.
An intelligent project management automation system that integrates Jira with Claude AI to automatically analyze requirements, generate implementations, and evaluate deliverables, reducing manual effort by 70-90% while maintaining high quality and consistency.
Cross-city market analysis of short-term rental investment opportunities across 22 US cities, combining Airbnb listing data with Zillow real estate prices. Identifies profitable property segments, competitive landscape dynamics, and market entry barriers through statistical modeling and market professionalization metrics.
An AI-powered application that analyzes jiu-jitsu techniques and generates personalized training strategies, flow charts, adversary response plans, and analytical insights for practitioners of all levels.
An automated news aggregation and analysis system that uses Claude AI to generate targeted search queries, fetches articles from NewsAPI, synthesizes summaries, and delivers reports via GitHub Issues with intelligent caching.
A robust web scraper application that extracts Penn State football schedules and creates standardized calendar files, allowing fans to automatically sync game information with their personal calendars.
A Python web scraper that automatically extracts Yale football schedule information and generates an iCalendar file for easy integration with calendar applications.
Capturing moments through my lens
Insights from my class Emerging Trends in Digital Advertising, and my recent work with the Yale Center for Customer Insights
In March 2023, the New York Giants extended Daniel Jones with a 4-year, $160M contract ($40M AAV). The decision sparked immediate debate because his track record painted an ambiguous picture. Through his first four seasons, Jones had accumulated respectable volume: 14,582 passing yards, 4th highest among his 2019 draft class. But his efficiency told a different story: 5.83 career ANY/A, ranking in the bottom third of starting quarterbacks. His Year 3 performance showed 3,205 yards with 6.14 ANY/A—modest improvement, but hardly elite. Both were still well below our 4,200 era-adjusted yards and 6.5 ANY/A.
Article 1 established which metrics predict quarterback contract decisions most strongly, identifying Total Yards (era-adjusted) and ANY/A (era-adjusted)¹ as the key granular and open-source predictors which balance volume production with efficiency. However, that analysis treated all career years equally, averaging performance across Years 1-3 with uniform weights. This approach optimized overall prediction accuracy but obscured a crucial dimension of organizational evaluation: teams don’t weight all career years equally when making contract decisions.