Welcome

Welcome to Eck Sports Lab, where cutting-edge statistics meet baseball history, culture, and creativity.

At Eck Sports Lab, our mission is to research all things sports, with a current emphasis on baseball. We study topics ranging from player evaluation metrics to comparing baseball players across eras. What unites our work is a dedication to high-quality, innovative statistical and interdisciplinary research that are presented in ways that are accessible, engaging, and often entertaining.

Our projects span rigorous methodology, applied analytics, and student-led creative initiatives, all working together to bring new perspectives to how we understand sports.

Current Projects

Comparing Baseball Players Across Eras

Project page: Era Adjusted Baseball Stats v2.1
Full write‑up: Stats and Case Studies v2.1

This is an ongoing project devoted to the development of statistical tools which can era-adjust performance metrics. The impetus for this project was the initial discovery that the current consensus of baseball ranking methods were biased towards the performances of pre-integration players. You can explore these origins in our initial Shiny app on baseball nostalgia. We have since made an advance towards the creation of era-adjusted statistics with the development of what we call Full House Modeling. Full House Models era-adjust statistics through a principled balancing of how players performed “vs. their peers” and the quality of the talent pool of players’ contemporaries. These models are a crystallization of conceptual logic articulated by Stephen Jay Gould, as explained in this short video.

Here is a snapshot of our current results. Below is the top 10 list according to era-adjusted baseball reference wins above replacement (ebWAR) and era-adjusted fangraphs wins above replacement (efWAR):

rank	name	ebWAR	name	efWAR
1	Barry Bonds	154.71	Barry Bonds	145.57
2	Willie Mays	145.30	Roger Clemens	140.75
3	Roger Clemens	144.38	Willie Mays	135.78
4	Babe Ruth	138.64	Henry Aaron	127.96
5	Henry Aaron	135.67	Greg Maddux	120.91
6	Alex Rodriguez	120.64	Babe Ruth	120.6
7	Stan Musial	119.37	Stan Musial	112.79
8	Ty Cobb	115	Alex Rodriguez	110.52
9	Greg Maddux	113.55	Randy Johnson	109.78
10	Albert Pujols	111.95	Ty Cobb	108.95

The list above combines Babe Ruth’s batting and pitching WAR

This is Version 2.1 of the project. For earlier iterations and the development history, see our project history on GitHub.

SEAM method for Better Batted-Ball Prediction

Web application landing page: SEAM: Synthetic Estimated Average Matchup

We developed SEAM (synthetic estimated average matchup) methodology for describing batter versus pitcher matchups in baseball. The SEAM method provides confidence regions that reflect where baseballs that are put into play are expected to land. Our method is more accurate than similar methods constructed from individual batter spray charts or an individual pitcher’s spray chart allowed. We estimate that the implementation of SEAM can yield an additional 40 outs over conventional spray charts throughout the course of an MLB season. Colin Alberts Master’s thesis explores fielder placement optimization approaches that are downstream of SEAM.

Check out Julia Wapner’s presentation of the SEAM method at the 2022 SABR Analytics Conference:

Teaching Resources

Eck Sports Lab develops open-access materials that bridge foundational concepts in statistics with contemporary research problems in sports analytics. These resources are designed for instructors to integrate directly into their own courses and for students to explore methods through tangible, data-driven, and modern examples.

Teaching vignette pages:

Binomial Distribution: Comparing Baseball Players Across Eras (Introductory)

A teaching vignette that introduces the Binomial distribution using baseball data and connects introductory probability to real-world modeling with era-adjusted WAR. This resource includes reproducible R code, data from baseball reference and the fullhouse R package, and visualizations illustrating how simple probability models can reveal systematic bias in historical rankings.

Probability Integral Transformation, Order Statistics, and Full House Modeling (Intermediate / Advanced)

A teaching vignette that introduces the core ideas behind Full House Modeling (FHM) and its applications to era-adjusted WAR. This resource builds intuition for the probability integral transform and the distribution of order statistics, showing how these concepts form the mathematical foundation of FHM. It includes reproducible R code, visual examples, and data from the Lahman R package.

Featured Student Content

At Eck Sports Lab, our students are creating innovative, public-facing projects that bring our research to wider audiences. From newsletters to YouTube channels to interactive games, here are some highlights:

The Era Curveball

A Substack newsletter by Idrees Muhammad Kudaimi featuring player profiles through the lens of era-adjusted baseball statistics obtained via Full House Modeling. It blends rigorous analysis with sharp, entertaining commentary. Check it out

DataDugout YouTube Channel

A YouTube channel by Christopher Ye that covers baseball broadly, with a current focus on player profiles and storytelling powered by era-adjusted baseball statistics. Check it out

2v2 Baseball Death Match

An R Shiny–based retro arcade simulation video game by Logan Blancett, originally inspired by a STAT 430 group project led by Aidan Glickman. The game simulates 7-inning matchups where two teams of a batter and pitcher face off, with simulator logic using Statcast data and similarity scores derived from our SEAM project. Check it out (click game artwork below to go to game):

Historical Milestones

08/12/25: Our era‑adjusted baseball research was featured in New York Times Science and was on the front page of the 08/13/25 print edition!
03/13/23: Our era-adjusted methodology and interdisciplinary collaboration was featured by the College of Liberal Arts and Sciences at University of Illinois Urbana-Champaign.
The Eck Sports Lab is collaborating with the Chicago Cubs on a joint undergraduate research mentorship program.

People

Daniel J. Eck is a Statistics professor at the University of Illinois Urbana-Champaign. He is an active researcher in baseball analytics and has recently developed a topics course devoted to Baseball Analytics.

Adrian Burgos Jr. is a History professor at the University of Illinois Urbana-Champaign. He has written numerous books and articles and has taught numerous classes devoted to baseball history. Recently, Adrian served on Hall of Fame Committees which enshrined Bud Fowler, Gil Hodges, Jim Kaat, Minnie Minoso, Tony Oliva, and Buck O’Neil.

Shen Yan is currently a postdoc with Professor Bo Li in the Department of Statistics at the University of Illinois Urbana-Champaign. He successfully defended his PhD dissertation on Full House Methodology, a framework for fair cross-era comparisons in baseball and beyond. Shen plays an active role in research and advising students.

David Dalpiaz is a Computer Science professor at the University of Illinois Urbana-Champaign. He is an active researcher in baseball analytics.

Christopher Kinson is a Statistics professor at the University of Illinois Urbana-Champaign. He is an active data science educator.

Ryan To is a Computer Science student at the University of Illinois Urbana-Champaign. He is working on a baseball game simulator with the Chicago Cubs, helps facilitate lab research projects, and mentors student-led content creation initiatives within the lab.

Idrees Muhammad Kudaimi is a Chemistry student at the University of Illinois Urbana-Champaign. He is contributing to content development for the Full House Modeling project. He launched the Era Curveball Substack newsletter.

Rohan Nakra is an Engineering student at the University of Illinois Urbana-Champaign. He is currently working to update the SEAM project with up-to-date batted ball distributions for specific batter-pitcher matchups.

Christopher Ye is a Statistics student at the University of Illinois Urbana-Champaign. He is contributing to content development for the Full House Modeling project. Check out his DataDugout YouTube channel.

Logan Blancett is a Statistics student at the University of Illinois Urbana-Champaign. He is developing a Shiny-based baseball simulation game, 2v2 Baseball Death Match, and also mentors student-led content creation projects within the lab.

Alumni Working in Baseball

Jack C. Banks (2023) is currently working as a Performance Science Analyst for the New York Yankees. He worked on a baseball season simulator with the Chicago Cubs. Check out his website.

Julia Wapner (2022) is currently working as a Junior Data Scientist for the Baltimore Orioles. She helped develop the second version (current version) of the SEAM application.

Charles Young (2020) is currently working as a Senior Software Engineer with the Pittsburgh Pirates. He helped develop the first version of the SEAM application. He created the Illini Analytics group at University of Illinois Urbana-Champaign. His collaborations with physicist and baseball expert Alan Nathan and the UIUC baseball team were made into a documentary.

Alumni

Ashrith Anumala (2025) is a Computer Science and Statistics student at the University of Illinois Urbana-Champaign. He is working on a baseball game simulator with the Chicago Cubs.

Colin Doherty (2025) is a Statistics student at the University of Illinois Urbana-Champaign. He is working on a baseball game simulator with the Chicago Cubs.

Ava (Zheer) Wang (2025) is a Computer Science and Statistics student at the University of Illinois Urbana-Champaign. She developed public-facing content for the Full House Modeling project.

Mohit Singh (2025) is a Computer Science and Statistics student at the University of Illinois Urbana-Champaign. He is developing public-facing content for the Full House Modeling project.

Colin Alberts (2024) is a data scientist at CISCO. He was an Applied Mathematics MS student at the University of Illinois Urbana-Champaign. He completed a Master's thesis on working on fielder placement optimization. See his GitHub repo here.

Jamin Kim (2024) is a Statistics graduate from the University of Illinois Urbana-Champaign. He worked on a baseball game simulator with the Chicago Cubs.

Aidan Glickman (2023) is a Computer Science graduate from the University of Illinois Urbana-Champaign. He led a STAT 430 group that developed the initial proof of concept for our baseball video game simulator.

Michael Escobedo (2023) is a Statistics graduate from the University of Illinois Urbana-Champaign. He worked on a baseball season simulator with the Chicago Cubs.

Christian Chase Jr. (2022) worked as a Player Development Intern with the Chicago White Sox. He wrote his University of Florida honors thesis on "Predicting situation-specific OPS in MLB", and is currently a J.D. Candidate at Vanderbilt University Law School.

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