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 innovation for the Game Insight Group of Tennis Australia, building first-of-a-kind metrics and real-time applications with tracking data. Stephanie is the founder of the tennis analytics blog "On the T" and tweets @StatsOnTheT.  For additional ..read more

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 ..read more

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 recently spent the Spring 2022 semester working as an instructor in the Dept of Mathematics and Statistics at Loyola. Quang previously completed his undergraduate degree in Mathematics and Data Science at Wittenberg University in Springfield, Ohio. Qu ..read more

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! Benjamin Robinson is a data scientist living in Washington, D.C. and the creator of Grinding the Mocks, where since 2018 he has used mock drafts, the wisdom of crowds, and data science to predict the NFL Draft.  He is a 2012 graduate of the Universit ..read more

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. He is broadly interested in Bayesian methods and causal inference. He is a long-suffering but unapologetic fan of America's Team. He's also a fan of the Dallas Mavericks. Kathy is the Director of Strategic Research for the Toronto Raptors. She ..read more

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 Information and Analytics in the Daniels College of Business at the University of Denver (DU). He earned his Ph.D. in statistics at Penn State University and worked as a Senior Scientist at the National Renewable Energy Laboratory prior to DU. He h ..read more

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 League, his work centers on how to use data to enhance and better understand the game of football.  For additional references mentioned in the show: NESSIS 2017 talk: https://www.youtube.com/watch?v=obb_wpn4IvE CMSAC 2017 talk: https://www.you ..read more

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 sports, as well as the importance of interpretable machine learning. Lotte Bransen (@LotteBransen) is a Lead Data Scientist at SciSports, where she leads the Data Analytics team that develops analytical tools to derive actionable insights from soccer d ..read more

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 Technology), and former lead hockey researcher for the Minnesota Wild. He received his PhD in Statistics at Harvard University. Sam Ventura is the Director of Hockey Research for the Pittsburgh Penguins, and an affiliated faculty member at Carnegi ..read more

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/beanumber/openWAR Baseball Prospectus example articles (*deserved runs created, NOT defensive runs created as incorrectly stated in the show):  https://www.baseballprospectus.com/news/article/48293/entirely-beyond-wowy-a-breakdown-of-drc/ and https ..read more