Data Science & Analytics in Sports
Ayokunmi Lawal
Toronto, ON
Toronto, ON
Hi, I'm Ayokunmi — a data science student at York University focused on bringing rigorous analysis to football: recruitment models, undervalued player profiles, and the tactical trends that make certain players work in certain systems.
I work across Python, SQL, R, and dbt to build data pipelines, statistical models, and forecasts, then turn that work into things people can actually use — dashboards, maps, and reports, published in public and dated so the work can be checked directly.
Some of that work outside football:
Across all of it, you'll find:
Closing out an automated Premier League prediction pipeline — free-tier data sources, a transparent weighted-factor scoring model, and a live dashboard that refreshes on a schedule.
GitHub — finishing touchesA model estimating transfer likelihood and probable destination clubs from performance trends, contract situation, and market-value signals.
In progressStatistical similarity scoring across FBref and StatsBomb metrics to surface undervalued players who match a target role profile.
PlannedUsing Python, SQL, and R to build game databases and performance reports for the varsity coaching staff.
Automated recurring financial reporting with SQL and Power BI, cutting manual prep time by roughly 40% per cycle.
Coursework in statistics, machine learning and data engineering, applied directly to football through independent scouting and analytics projects outside the classroom.
Working toward a recruitment or analytics role inside a club — starting with value-first outreach to local semi-pro clubs and academies, using club-specific analysis as the introduction.