Keynote Sessions

Nathan Chen, Statistics and Data Science Student, Yale University; Olympic, World, and US Champion in Figure Skating

Title: Designing the Optimal Figure Skating Program: Leveraging Data for a Competitive Edge

Abstract: As figure skating continues to evolve into an increasingly complex and competitive sport, athletes and coaches face the challenging task of designing optimal, high-scoring program layouts that will offer a competitive edge. This complexity is driven by the need to balance technical difficulty with artistic expression, all while adhering to strict judging criteria. To demystify this process and aid in strategic planning, we embarked on an analysis of figure skating performances and elements, including jumps, spins, and step sequences. Our analysis involves a dataset comprising over 12,000 elements from various elite international competitions, collected from official scoresheets. Employing regression and Bayesian methods to evaluate jump success probabilities, as well as mixed-effects regression models to predict score outcomes, we evaluated variables encompassing jump types, sequences, scores, and performance contexts. We were able to identify important predictors of jump success, as well as predict scores to develop a model capable of determining the most advantageous jump layouts and element sequences, providing actionable insights for skaters to plan their programs.

Nathan Chen Nathan Chen is a senior at Yale University, majoring in Statistics and Data Science. He is the 2022 Olympic champion, 2018 Olympic bronze medalist, three-time World champion, and six-time U.S. national champion in men’s figure skating. He has been recognized on the Time100 and in the Forbes 30 Under 30 list for his skating achievements. At Yale, he works at the Cardiovascular Research Center, analyzing genomic data to better understand the impact of variants of known and uncertain significance on cardiovascular outcomes. In his free time, he enjoys spending time with family and friends, exploring new food and drink spots, and relaxing anywhere with a nice view.

Kristin Morgan, Assistant Professor of Biomedical Engineering, Univeristy of Connecticut

Interdisciplinary Data-Driven Approach to Improve Player Recovery and Performance

Abstract: The integration of statistics, engineering, and biomechanics has fostered substantial contributions and advancements in sports science. The strategic incorporation of these disciplines has yielded novel data collection designs, launched new athletic performance metrics, and identified new methods for assessing and visualizing mechanistic changes in motor control during sport specific tasks. This talk will explore how this interdisciplinary approach has contributed to the development of innovative treatment protocols, injury risk assessment practices, and training programs to enhance and improve sports performance. The state-of-the-art technology used to conduct sports-based studies will be highlighted to further illustrate how data analytics and musculoskeletal modeling can help drive player recovery and on-field performance.

Kristin Morgan Kristin Morgan is an Assistant Professor of Biomedical Engineering at the University of Connecticut. Kristin’s work focuses on implementing innovative gait protocols and musculoskeletal modeling to accelerate individuals’ rehabilitation progression and improve their long-term joint health. Notably, she has utilized statistical techniques to establish universal ranges of healthy dynamics to help characterize the restoration of healthy biomechanics. Her work has been published in high-impact journals and has been supported by the Office of Naval Research, National Science Foundation, General Dynamics Electric Boat, and the National Institutes of Health.

Esteban Navarro Garaiz, Technical Product Manager, Zelus Analytics

Baseball Analytics: Past, Present, and Beyond

Abstract: Nearly 50 years after Bill James published his first Baseball Abstract, statistics has penetrated every corner of America's favorite pastime, impacting everything from media coverage to team roster construction through data-driven decision-making. Join me as we dive into the captivating history and evolution of Baseball Analytics, the technological changes that took us from box scores and radar guns to ball tracking and player motion capture. We will explore the current state-of-the-art, both in the public sphere and behind the scenes. Drawing inspiration from Keith Woolner's exploration of baseball's open problems in 2000, we'll also discuss some of the most intriguing challenges and unanswered questions facing the industry in the years ahead.

Esteban Navarro Garaiz Esteban Navarro Garaiz is a Technical Product Manager for the baseball team at Zelus Analytics, a role in which he collaborates closely with a team of 20 data scientists and engineers overseeing the development and implementation of the team's roadmap, supporting client integration, and mentoring junior team members. Before joining Zelus Analytics, Esteban spent two years as a Quantitative Analyst with the Los Angeles Dodgers, winning the World Series in 2020. He graduated with a Master’s degree in Data Science from New York University, where he was a DeepMind fellow and a Fulbright-García Robles Scholarship recipient.

Panel Discussion on Sports Analytics for Life: Many Different Paths  

Brian Macdonald Brian Macdonald (moderator) is a senior lecturer and research scientist in the Department of Statistics and Data Science at Yale University, where he focuses on statistics and data science education, sports analytics, and environmental data science. He was previously the Director of Sports Analytics at ESPN and Director of Hockey Analytics with the Florida Panthers Hockey Club, and held faculty positions at West Point, Carnegie Mellon University, University of Miami, and Florida Atlantic University. He received a Bachelor of Science in Electrical Engineering from Lafayette College, Easton, PA, and a Master of Arts and a Ph.D. in Mathematics from Johns Hopkins University, Baltimore, MD.

Sean Ahmed Sean Ahmed is the Director of Research & Development with the Pittsburgh Pirates, leading the relaunch of the team’s data science, quantitative analysis, and engineering efforts. Prior to joining the Pirates as a senior analyst in 2020, he was part of the Chicago Cubs’ first R&D team, spending six years with primary focuses on building models and integrating analysis for the amateur draft, player development, and defensive evaluation and positioning. Sean holds a bachelor’s degree in Economics and a minor in Latin American Studies from the University of Chicago.

Luke Benz Luke Benz is a Biostatistics PhD student at the Harvard T. H. Chan School of Public Health, where his research focuses on missing data problems when conducting electronic health records based observational studies. He graduated with a degree in Applied Mathematics from Yale University where he was president of the Yale Undergraduate Sports Analytics Group. He has written several research articles about sports analytics and is particularly interested in problems relating to home field advantage. Luke has consulted on several sports analytics projects for the Ivy League relating to tie breaking procedures and scheduling.

Sean Fischer Sean Fischer is the manager of the Cincinnati Reds baseball analytics department. In his time with the club, he has contributed data science solutions for advance scouting, pro player evaluation, and amateur and international scouting. Sean holds a PhD from the Annenberg School for Communication at the University of Pennsylvania.

Paul Sabin Paul Sabin is a seasoned sports analytics professional with a proven track record of leveraging data-driven insights to drive strategic decisions in the sports industry. With previous roles at ESPN, SumerSports, and his current position at Wharton, Paul brings extensive experience in analyzing sports data to optimize performance and enhance fan engagement. At ESPN, Paul played a pivotal role in developing innovative analytics solutions, including the creation of the Basketball Power Index (BPI), the Allstate Playoff Predictor, and other key metrics. These tools have become integral to sports analysis, providing valuable insights for teams, fans, and broadcasters alike. Transitioning to VP of Football Analytics at SumerSports, he started and grew the data science team while furthering his expertise in athlete performance analysis and team strategy optimization. Currently at Wharton, Paul is a Lecturer in Statistics & Data Science and a Senior Fellow at the Sports Analytics & Business Initiative, where he continues to push the boundaries of sports analytics research and application.

Emily Wright Emily Wright is the Data Scientist for Volleyball Canada’s Beach National Teams. Her primary focus involves employing data analytics and modeling techniques to gain deeper insights into the dynamics of beach volleyball, aiming to elevate the sport through enhanced performance and strategy. Emily is also a part of the Canadian Olympic Committee Emerging Leaders Program, designed to develop talent in the Canadian sport system. She holds a master’s degree in Statistics and Actuarial Mathematics from Concordia University and a bachelor’s degree in Mathematics from Mount Saint Vincent University. Outside of volleyball, she enjoys adventures with her dog and talking about Atlantic Canada with anyone who will listen.