Welcome
127th issue.
The Eagles' struggles against weaker opponents are examined, Datagolf's efficient tech stack for golf analytics is highlighted, an in-depth review of the 2023 MLB end-of-season stats is presented, and a machine learning approach is explored for predicting NBA players' positions in the '22-23 season, shedding light on the dynamics of positionless basketball.
Cheers!
Self Promotion
This Week's Lineup
Where Did It All Go Wrong For The Eagles?
This kind of sums it up: The Eagles played worse against worse opponents down the stretch. More insight inside.
How Datagolf.com Scores Hole-in-One Analytics with Its Tech Stack?
Datagolf built an analytics pipeline leveraging Ruby on Rails, Google BigQuery, and pre-trained ML to deliver premium golf data experiences without overextending its early-stage resources.
2023 MLB End of Season Stats
A little late, sure, but still worth a review! Walks through how he arrives at the year-long stats and provides links to a bunch of spreadsheets.
Predicting NBA Players Positions with Machine Learning
Employs machine learning models trained on historical NBA offensive data to predict players' positions in the '22-23 season, delving into the nuances of positionless basketball.
Introducing The CBBData API
R package designed for anyone passionate about college basketball statistics. It simplifies the process of accessing and analyzing a wealth of college basketball data, making it more efficient and user-friendly than ever before.
Please Forward
Was this email forwarded to you? Sign up here >>
Unexpected Points Added is curated and maintained by Patrick Hayes.
I have completed a Master of Applied Data Science from the University of Michigan; let's connect.
Send me an email with your feedback or questions. I respond to every single one.