I build the pipelines I used to wait on.
I take on data problems that are worth solving and interesting to sit with. Florida born with sights set on Michigan, I work from pandas to PostGIS — writing SQL and Python, and building the pipelines that feed the analysis. Below is a curated body of work. If something here is useful to you, get in touch.
I came to analytics through digital marketing, which taught me early that a chart nobody acts on is just decoration. Today I build reports for managers and C-suite executives who don’t need the model; they need the decision it points to. My job is getting them there: rigorous analysis, told clearly enough to act on. Most of my time goes to financial, location-based, and sports data.
Which position players returned the most value above market rate? Merged Baseball Reference WAR against contract salary at $8M/WAR to isolate where clubs got production they didn't pay for.
Pre-arbitration players dominate the top 20 — and one team finished negative. View on GitHubMLB's proposed 2027 cap and floor, measured against real team payrolls. An initial pass using cash payroll was rebuilt on CBT-consistent figures after a framework mismatch surfaced — which reversed the headline.
Cap burden is concentrated and transactional. Floor burden is broad and structural. View on GitHubDo teams that spend more actually win more? Wins per dollar across all 30 clubs, with efficiency tiers and a correlation test on the spend-to-win relationship.
r = 0.521 — real, but far from deterministic. View on GitHubHave a question about anything here, or a data problem worth talking through? Reach out. I’m always up for a good conversation about data.