Data Analytics · Toronto, ON
Data analyst working across R, Python, SQL, Tableau, and Excel — with a focus on sports analytics projects built around questions coaches and decision-makers actually want answered.
Despite turning the ball over four times, Seattle was a field goal away from winning. Quantified how turnovers shifted expected points and win probability across the game.
Quantifying the production departing free agents take with them — by position, by team, by metric. EPA/target, pressure rate, coverage snaps. Who lost the most?
When should a manager make a change? A win-probability and Statcast breakdown of every pitching change in a World Series Game 7, and the measurable impact each decision had on the outcome.
Exploratory analysis of statistics attributed to winning NFL games — identifying which metrics most reliably separate winners from losers across a season.
Exploration of the global COVID-19 dataset using SQL Server — joins, CTEs, window functions, and aggregate analysis across cases, deaths, and vaccination rates.
Cleaned and standardized a real-world housing dataset in SQL Server: handling nulls, standardizing formats, removing duplicates, and splitting address columns.
Explored which variables — budget, genre, runtime, studio — most strongly correlate with high box office revenue, using correlation matrices and scatter analysis.
A collection of interactive dashboards and data visualizations across various domains, built with a focus on clarity and accessibility for non-technical audiences.
Analyzed survey data from data professionals to surface insights on salary ranges, tool preferences, job satisfaction, and career entry difficulty by role.
Analyzed customer data to identify demographic patterns in bike purchases, segmenting by income, commute distance, age, and region using pivot tables and charts.
I'm a data analyst based in Toronto with a broad toolkit — R, Python, SQL, Tableau, Power BI, and Excel — and a particular passion for sports analytics. My sports projects are built around a simple principle: frame every finding as something a coach or decision-maker would actually want to know.
On the NFL side, I work with play-by-play data via nflfastR and FTN charting, using EPA, win probability, and coverage metrics to understand how games are won and lost. I'm also expanding into MLB analysis using Statcast pitch-level data through baseballr.
Projects are published as R Markdown reports and deployed here on GitHub Pages — built to be readable by anyone, not just data people.