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/

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