Ron Yurko

@stat-ron.bsky.social

Assistant Teaching Professor, Department of Statistics & Data Science, Carnegie Mellon University, Director of Carnegie Mellon #SportsAnalytics Center (CMSAC) https://stat.cmu.edu/cmsac

Does anyone (buzzing in the same circles as me) want to team up for a stats project? Just trying to break out of a funk, maybe try something new, nothing ambitious...

Ron Yurko@stat-ron.bsky.social · 3w ago

Enter this year's Carnegie Mellon #SportsAnalytics Conference (Oct 23-24) #ReproducibleResearch Competition! forms.gle/GUXbxGbkkoiq... NEW: Winning authors receive an exclusive invitation to submit to the Journal of Statistics and Data Science in Sports jsds-sports.github.io

It's a privilege and honor to be able to work w/ #Heisman - I consider the #HeismanTrophy to be the most prestigious award in American sports, and I'm beyond excited for #CMSAC to play a role in its rich history! #CollegeFootball #sportsanalytics

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Carnegie Mellon University@cmu.edu · 4w ago

CMU x #Heisman 🏈📊🏆 @heismantrophy.bsky.social has partnered with Carnegie Mellon and the Sports Analytics Center to develop metrics and tools to make it easier for fans to access and evaluate performance data for all positions.

Yo this is the last week (last days!) for abstracts! I just submitted mine today :) All baseball teams are represented at this conference. Most (all?) doing informational interviews for job seekers or the curious. May 15 is the deadline for abstracts. See y’all there in a few months

Saberseminar@saberseminar.bsky.social · 4mo ago

Reminder: abstract submissions are open for Saberseminar 2026. We always aim for a mix of talks on a wide variety of subjects, from established professionals to hobbyists to students looking to get a foot in the door. Submit your abstract ASAP! docs.google.com/forms/d/e/1F...

CMU's sports analytics education produces big league talent. Shravan Ramamurthy and Toby Junker both have professions in research and development for #NFL teams. Today's sports analysts have access to more data than ever. CMU equips students with the skills to turn data into actionable insights. 📊

Data is powering the future of sports. 📊 “Every tenth of a second, the NFL’s Next Gen data chips provide information for where every single player is positioned on the field — the direction they’re moving, the speed they’re moving." CMU researchers are tackling how to use the data at our disposal.

Sports Generate More Data Than Ever. CMU's Sports Analytics Center Asks What It Means

Carnegie Mellon University experts are turning that data into insight, using statistics and data science to help professional teams gain a competitive edge.

cmu.edu

Carnegie Mellon research is helping #NFL teams see what game film can’t. Smarter #data means smarter draft picks. By measuring traits that aren't always obvious, from shiftiness to arm angle, we can study their impact on the game. Analyzing this data is shaping the way teams draft. @cbsnews.com

Carnegie Mellon analytics helping shape NFL draft decisions

With technology used in just about every facet of everyday life, it's also changing how NFL teams look at athletes.

cbsnews.com

This framework enables comparison of observed movement against a distribution of hypotheticals at each frame within a play We focus on evaluating RB movement after handoff in run plays, and present examples of play valuation breakdown & player performance metrics

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Simulation details: Given an observed path, at each frame along this path, generate a local distribution of hypothetical next steps (only simulate the immediate next movement, 1 frame ahead) (work in progress: full path sim by repeatedly drawing posterior predictive steps & turns)

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Full framework: 1. Movement characterization with tracking data features (step length & turn angle, inspired by animal movement literature) 2. Bayesian multilevel step-and-turn models 3. Posterior predictive simulation of player movement 4. Ghosting/hypothetical evaluation

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🔔 New preprint w/ Catalina Medina ❓ We ran a quasi-experiment in an introductory stats course to see if giving students a choice of real data context on homework actually has impact 📄 Engaging students with statistics through choice of real data context on homework 🔗 arxiv.org/abs/2603.04541

Engaging students with statistics through choice of real data context on homework

Statistics educators recommend teaching with real data with relevant contexts, but defining relevancy is challenging and varies by student. We investigated whether providing student choice of data con...

arxiv.org

👍 Our recs based on our findings are 1️⃣ use real data with authentic contexts, 2️⃣ select contexts students care about 3️⃣ incorporate variety across data contexts 4️⃣ consider choice as a pedagogical tool.