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.youtube.com/watch?v=owOpU_diCVI


• 'teamcolors' package by Ben Baumer (@BaumerBen) and Gregory J. Matthews (@StatsInTheWild)


• Mike's tutorial posts on the paper's modeling framework: https://statsbylopez.netlify.app/post/a-state-space-model-to-evaluate-sports-teams/


• Follow Tom Bliss (@DataWithBliss) and check out his presentation at UCSAS20


• Dan Cervone's archived 'Win Probability Probabilities' post: http://web.archive.org/web/20200808064442/http://xyresearch.com/posts/win-probability-probabilities with code available here https://github.com/dcervone/winProb


• Big Data Bowl 2021: https://www.kaggle.com/c/nfl-big-data-bowl-2021

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