Welcome
73rd issue.
Skipped last week to hang out with family. Hope you did too!
World Cup is nearing the beginning of the knockout stage, so I've included two related to soccer. One is a fun quiz with good data-based visuals and another for predicting outcomes. Parity and predictability across American sports are next, followed by two college football pieces. The first a Python guide to setting up an analysis, and the second is for the perfect tailgate.
I'm interested in sports data science opportunities next year, have an employer you recommend? Let's chat!
This Week's Lineup
Which World Cup Player Should You Root For?
A brilliant quiz to find the best World Cup player to root for in 2022.
Predicting the World Cup with R and Stan
A fairly simple bayesian model. Had Germany pegged as 3rd finisher in their group, which turned out to be accurate!
Examining Parity and Predictability Across Sports
Thought-provoking read. The higher-scoring sports - basketball and football - are the ones that are the most predictable based on previous results when season length is controlled for. Lower-scoring sports generally seem more chaotic and less predictable based just on score lines.
Guide to Setting up Python for College Football Analysis
An in-depth tutorial on how to use Python to analyze College Football data. Good stuff!
The Perfect College Football Tailgate
My stomach growls as I write this. Shows differences in regions and what ingredients constitute the perfect tailgate.
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Unexpected Points Added is curated and maintained by Patrick Hayes.
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