Open Source Sports Ron Yurko

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 references mentioned in the show:


ATP Tour Second Screen
Stephanie's article in Harvard Data Science Review: Why Tennis Is Still Not Ready to Play Moneyball
Grand Slam R package: courtvisionr
Stephanie's GitHub with various resources for accessing tennis data: https://github.com/skoval
Stan tutorials: https://mc-stan.org/users/documentation/tutorials
Register now for the Carnegie Mellon Sports Analytics Conference: https://www.stat.cmu.edu/cmsac/conference/2022/
Check out the Big Data Derby now on Kaggle


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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


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit opensourcesports.substack.com

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. Quang's current interests include statistics in sports, data science, statistics and data science education, and reproducibility. He is a die-hard supporter of Manchester United F.C. of the English Premier League. And last but not least, Quang is excited to join the Dept of Statistics and Data Science at CMU as a first-year PhD student this coming Fall 2022.

For additional references mentioned in the show:


Quang's blog posts: https://qntkhvn.netlify.app/blog.html
Code for paper: https://github.com/qntkhvn/climbing
Inducing Any Feasible Level of Correlation to Bivariate Data With Any Marginals
R copula package: https://cran.r-project.org/web/packages/copula/index.html and book: http://copula.r-forge.r-project.org/book/
UConn Sports Analytics Symposium (UCSAS) 
CRAN Task View for Sports Analytics: https://cran.r-project.org/web/views/SportsAnalytics.html


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit opensourcesports.substack.com

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 University of Pittsburgh with degrees in Economics and Urban Studies and earned a Master of Public Policy degree from the University of Southern California in 2014.  You can follow him on Twitter @benj_robinson and find the Grinding the Mocks project at grindingthemocks.com and @GrindingMocks.

For additional references mentioned in the show:


Ben's bitbucket repository of data: https://bitbucket.org/benjamin_robinson/grindingthebayes/
Bayesian modeling in R with the brms package: https://paul-buerkner.github.io/brms/
CMSAC Reproducible Competition abstract submission: http://stat.cmu.edu/cmsac/conference/2021/#mu-research
Saiem Gilani's (@SaiemGilani) collection of software: https://sportsdataverse.org/


This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit opensourcesports.substack.com

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 completed her Ph.D. in Biostatistics at Harvard University. She doesn't have an opinion on Frequentist vs Bayesian or R vs Python, but will get very upset if Rise of Skywalker is your favorite Star Wars movie.

For additional references mentioned in the show:


Big Data Bowl notebooks: https://www.kaggle.com/c/nfl-big-data-bowl-2021/notebooks
BART: https://arxiv.org/abs/0806.3286
XBART: Accelerated Bayesian Additive Regression Trees https://jingyuhe.com/xbart.html
Matthew Reyers (@Stats_By_Matt) thesis: https://twitter.com/Stats_By_Matt/status/1296570171687989249?s=20 


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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 has over 20 peer reviewed publications in outlets such as Journal of the American Statistical Association, Biometrika, The American Statistician, Big Data, Journal of Applied Statistics, Journal of Sports Economics, among others. He is currently an Associate Editor for the Journal of Quantitative Analysis in Sports and recently organized the conference “Rocky Mountain Symposium on Analytics in Sports” hosted at DU.

Gregory Matthews completed his Ph.D. In statistics at the University of Connecticut in 2011.  From 2011-2014, he was a post-doc in the School of Public Health at the University of Massachusetts-Amherst.  Since 2014, he has been a professor of statistics at Loyola University Chicago.  He was recently promoted to Associate professor with tenure in March 2020.

For additional references mentioned in the show:


Tony Adams' (@adams_at) Houston Astros trash can banging data website: http://signstealingscandal.com/
Ryan and Greg's GitHub repository with code and data: https://github.com/gjm112/Astros_sign_stealing
The causal effect of a timeout at stopping an opposing run in the NBA by Connor Gibbs (@cgibbs_10), Ryan Elmore, and Bailey Fosdick (@baileyfosdick)




This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit opensourcesports.substack.com