In this episode we talk to Stephanie Kovalchik about her paper 'A Statistical Model of Serve Return Impact Patterns in Professional Tennis' (co-authored with Jim Albert). Stephanie is a Staff Data Scientist at Zelus Analytics, where she works on advanced performance valuation for multiple pro sports. Before joining Zelus, Stephanie led data science…

We discuss True Shot Charts with Syracuse University Professors Justin Ehrlich and Shane Sanders. For references mentioned in the show: BigDataBall StatMuse Positive Residual - True Shooting Charts

We discuss An Examination of Olympic Sport Climbing Competition Format and Scoring System with Quang Nguyen (@qntkhvn). This paper won the Carnegie Mellon Sports Analytics Conference Reproducible Research Competition in November 2021. Quang Nguyen completed his Master of Science in Applied Statistics at Loyola University Chicago in 2021. He recentl…

We discuss Grinding the Bayes: A Hierarchical Modeling Approach to Predicting the NFL Draft with Benjamin Robinson (@benj_robinson). This paper was a finalist in the Carnegie Mellon Sports Analytics Conference Reproducible Research Competition in October 2020. You can submit an abstract to enter the 2021 Reproducible Research Competition now! Benja…

We discuss a previous Big Data Bowl finalist paper `Expected Hypothetical Completion Probability` (https://arxiv.org/abs/1910.12337) with authors Sameer Deshpande (@skdeshpande91) and Kathy Evans (@CausalKathy). Sameer is a postdoctoral associate at MIT. Prior to that, he completed his Ph.D. at the Wharton School of the University of Pennsylvania. …

We discuss Bang the Can Slowly: An Investigation into the 2017 Houston Astros with Ryan Elmore (@rtelmore) and Gregory J. Matthews (@StatsInTheWild). This paper was the winner of the Carnegie Mellon Sports Analytics Conference Reproducible Research Competition in October 2020. Ryan Elmore is an Assistant Professor in the Department of Business Info…

We discuss 'How often does the best team win? A unified approach to understanding randomness in North American sport' with Michael Lopez. Michael Lopez (@StatsbyLopez) is the Director of Football Data and Analytics at the National Football League and a Lecturer of Statistics and Research Associate at Skidmore College. At the National Football Leagu…

We discuss 'Player Chemistry: Striving for a Perfectly Balanced Soccer Team' with Lotte Bransen. This paper builds on the VAEP framework previously introduced Lotte and her colleagues, in order to quantify player chemistry. Our discussion covers details of the paper along with general challenges of estimating player chemistry in soccer and other sp…

In the third episode of the show we discuss 'Competing process hazard function models for player ratings in ice hockey' with two guests, Andrew Thomas and Sam Ventura. The discussion ranges from paper details to thoughts on modeling in hockey and sports in general. Andrew Thomas (@acthomasca) is the Director of Data Science for SMT (SportsMEDIA Tec…

In the second episode we discuss two papers by our guest Daniel Daly-Grafstein and Luke Bornn: Rao-Blackwellizing field goal percentage (published in JQAS and available at: http://www.lukebornn.com/papers/dalygrafstein_jqas_2019.pdf) and Using In-Game Shot Trajectories to Better Understand Defensive Impact in the NBA (available at: https://arxiv.or…

In the first official Open Source Sports podcast episode, we discuss openWAR (available on arXiv https://arxiv.org/abs/1312.7158 and JQAS ) with author Gregory J. Matthews (@StatsInTheWild), Associate Professor of Statistics at Loyola University Chicago. Additional resources mentioned in the show: openWAR code repository: https://github.com/beanumb…

Ron Yurko and Kostas Pelechrinis host the 'Open Source Sports' podcast to serve as a public reading group for discussing the latest research from sports analytics and statistics in sports. This teaser episode introduces the hosts and discusses the podcast format. Each episode will focus on a single paper featuring authors as guests, with discussion…