I build the decision systems
behind the numbers.
15 years running professional sports organizations as President, GM, and VP. M.S. Data Analytics, 4.0 GPA. I turn raw data into the answer to the question someone is actually trying to ask.
v1.0
I drove 158,940 miles and found a story in the data.
A forensic reconciliation of three years of my own gig-work earnings — six data exports, four platforms, 415,000+ rows. The kind of messy, ground-truth analytics work most candidates never get to demonstrate.
1,040 active days
across all platforms
from 6 exports
overstates real earnings
The number Uber shows you in the app is a fiction. So is the figure on your 1099. Both are technically true, but neither answers the question that matters: per hour of your life, what does this work pay?
The honest answer required reconciling three different ledgers, none of which agreed with each other. The 2024 Uber data showed a $1,920 day — except only four rides happened that day. The Lyft rides file had 1,484 trips, but payments showed earnings during years where no rides existed in the export.
"Drivers don't need a pay cut to lose money. They just need the platform to take a bigger slice of the same pie."
This is the kind of analytics work the corporate world rarely lets you show: sourcing the truth from disagreeing systems, identifying anomalies that warrant investigation, and computing the right unit economic instead of the obvious one.
UVI — a pitch-by-pitch MLB performance metric.
A full-scale public sabermetric platform processing every pitch of the 2025 MLB season, weighted by leverage and validated against the most-cited industry benchmarks.
30 teams · full season
(starting pitchers)
Public baseball analytics give you outcome stats (wOBA, FIP, WAR) or process stats (xBA, xwOBA), but almost nothing weights performance by the leverage of the moment. Internal team systems do — but they're proprietary. I built one in public.
UVI scores every pitch of every plate appearance across all 30 MLB teams, weighted by game state, score, inning, baserunners, and count, then aggregated to player-level metrics. Deployed as a live Streamlit application with automated dashboards, real-time leaderboards, and player-level audit pages.
Three more analyses, three different problems.
MLB Win Total Predictor
A Random Forest model trained on 19 offensive and pitching features — ERA, OBP, slugging, run production — to forecast season win totals. Deployed as an interactive R Shiny application.
Weighted Delta Aging Curve
Sabermetric methodology applied to player aging. Built a regression-based aging curve weighted by playing time, addressing selection bias inherent in raw year-over-year comparisons.
Cyclistic Bike Share
Google Data Analytics capstone. Compared member vs. casual ridership patterns to identify conversion opportunities. Full Tableau dashboard with weekday/weekend and trip-duration analysis.
An unusual path to analytics.
"I've sat in the seat of the person who has to use the dashboard. I know what gets opened twice a week and what gets forgotten. I build accordingly."
I spent 15 years running professional sports organizations — as President of the Texas Revolution, VP of Sales for the Lancaster Barnstormers, General Manager of the Richmond Revolution, and Director of Team Services for the Indoor Football League. I made staffing decisions, set pricing, ran P&Ls, negotiated contracts, and answered to ownership groups across six seasons of professional football, baseball, and league-level consulting.
The whole time, I was building spreadsheets and dashboards to figure out what was actually working. In 2023 I went back to school to formalize what I'd been doing instinctively for over a decade. I earned my M.S. in Data Analytics from Robert Morris University in December 2024 with a 4.0 GPA.
The result is a rare combination: domain experience from inside the businesses I now build tools for, paired with the statistical and engineering training to do it rigorously.
Two careers, one throughline.
Formal training meets field experience.
Three ways to work together.
Have data and need someone who can turn it into something useful?
Book a 15-minute call. We'll figure out together whether this is a fit, and if it isn't, I'll tell you.