Welcome
The ninth issue of Unexpected Points Added. This one had an unexpected (pun) shift towards R, by reviewing NFL player tracking data, a baseball Shiny tutorial, and personal run data. Also included is reviewing how to rank FBS kickers and a resource for NHL API data.
What's your favorite sports statistic? Mine? Take away all of Gretzky's goals and he's still the all-time NHL point leader.
This Week's Lineup
NFL Player Tracking Data Using R
The 2019 and 2020 Big Data Bowl winners each used Convolutional Neural Networks (CNNs) in their entries, this very detailed article digs into how it actually works. This one gets into the weeds quickly, love it!
Value-Added FBS Kicker Rankings
How can we rank kickers relative to one another, while controlling for the opportunities they get? The value-added statistic is the answer. Here's the formulat (#Makes * 3) - (#Attempts * Expected Points) = Added Value.
Resource: Publicly Accessible NHL API Portions
Via Gitlab, a resource to pull in any NHL data you can think of.
Baseball Shiny Tutorial Visualizations with R
Visualizing pitch locations by pitch type and count, this one provides additional links to a presentation and Github repo. Also included is a hitter Brushing Zone tool.
Analyzing Your Running Data with R
Continuing the R theme this week, a look at how to review your personal running data acquired in Nike Run Club for the iPhone. Step by step process is provided to summarize your best runs and other analyses from your output.
Please Forward
Was this email forwarded to you? Sign up here >>
Unexpected Points Added is curated and maintained by Patrick Hayes. If you have questions or suggestions for the newsletter, just reply to this email. I answer every single one.