Ron Yurko
Assistant Teaching Professor at Carnegie Mellon University - Statistics & Data Science
Pittsburgh, Pennsylvania, United States
Summary
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Senior🎓
Top SchoolRon Yurko is an Assistant Teaching Professor in Carnegie Mellon’s Department of Statistics & Data Science and Director of the Carnegie Mellon Sports Analytics Center, bringing nine years of applied analytics and academic experience. He develops methods at the intersection of statistical inference and machine learning, with domain applications in statistical genetics and sports analytics. Prior roles in industry—at Zelus Analytics and PNC—have grounded his research in practical quantitative and risk problems. Ron maintains active open-source work, producing reproducible NFL player-level datasets and advanced metrics (EPA, WPA, clutch EPA) used for exploratory analyses and visualization. He holds a PhD and BS in Statistics from Carnegie Mellon, combining rigorous academic training with hands-on data engineering. Colleagues often note his ability to translate complex statistical formulas into usable tools and clear teaching material for applied audiences.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at Carnegie Mellon University
Github Skills (11)
data-visualizations10
data-visualization10
data-visualisation10
statistical-models10
tidyverse10
r10
data-analysis10
ggplot8
statistics8
data-wrangling8
statistic8
Programming languages (4)
Github contributions (5)
ryurko/nflscrapR-data
Jul 2017 - Jan 2020
Data files (.csv) accessed with nflscrapR and summarized at the player-level
Role in this project:
Data Scientist
Data ScientistContributions:126 commits, 112 pushes, 1 branch in 2 years 5 months
Contributions summary:Ron created datasets and documentation, and added calculations for passing, rushing, and receiving statistics. They defined functions to generate player-level statistics at both season and game levels, including expected points added (EPA), win probability added (WPA), and clutch EPA, which required understanding and implementation of statistical formulas. The user also created example visualizations to explore relationships between these metrics, such as the relationship between success rate and clutch EPA.
csvpythonformula1csv-filesdata-files
ryurko/adaptMT
Jan 2019 - Mar 2022
Practical modifications to the adaptMT package
Contributions:2 PRs, 18 pushes, 2 branches in 3 years 2 months
modificationspractical
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