Corona Capture: Why Most Purchases Never Enter the Marketing Funnel, and What That Means For Individual Agency

Insights from my class Emerging Trends in Digital Advertising, and my recent work with the Yale Center for Customer Insights

Quantifying QB Success: Archetypal Trajectories and Current Pending Decisions (Part 3)

Opening: The Daniel Jones Problem

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.

Quantifying QB Success: Temporal Weighting Patterns in Contract Decisions, Part 2

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.

Quantifying QB Success: A 25-Year Analysis of First-Round Performance, Part 1

The current state of discourse regarding professional American football is poor, suffering from an overabundance of meaningless data, without any effective filtering mechanism for the public to understand what drives quarterback performance and retention decisions. Advanced proprietary metrics dominate front office decision-making, but their data is not accessible to the general fan to contextualize the performance of their favorite players. Public analysis remains constrained to basic box score statistics and subjective evaluation frameworks. This saturated landscape results in confusion about which statistics are actually meaningful, and which are just noise. There is always a stat available to suit any subjective argument being put forward, and the current fan has no way to determine which statistics matter, and which ones do not.

Welcome to My Website

Welcome to my new website! This is the first post in what I hope will be a series of updates about my professional work and projects.