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Data science, Analytics and software jobs in sports analytics. As always, the most complete site to get all the relevant opportunities in one place!
This edition I bring you recent jobs as always but also a really nice article from Jeremias Engelmann about Adjusted Plus Minus metric (in this case for the NBA) and different variants that exist and have been developed over the years. You will understand why it is useful, how to create it (with code) and what are the pros and cons. It’s super clear and if you are interested you can get into the rabbit hole following the links in the article or checking an old edition of this newsletter where we first talked about RAPM.
▫️Essentially all modern NBA player metrics depend on Adjusted Plus-Minus (APM).
▫️With more and more individual data being made available by the NBA, All-in-One metrics that incorporate that data keep gaining ground in accuracy.
Jobs for you:
These were some of the uploaded in the last two days only!
We started the year STRONG with a great list of opportunities. Let’s make 2026 a great sports analytics year.
NBA Adjusted Plus-Minus: How to Build It, plus Pro Tips and Tricks
Link to the article: https://www.roycewebb.com/p/nba-adjusted-plus-minus-how-to-build
Almost every modern NBA player metric starts from the same place: Adjusted Plus-Minus (APM). In this piece, Jeremias Engelmann, the mind behind ESPN’s Real Plus-Minus, explains how teams and analysts actually try to measure a player’s true on-court impact, beyond points, rebounds, or highlights.
The core idea is simple but powerful: evaluate how lineups perform possession by possession, and use math to separate individual impact from teammates, opponents, and randomness. Because lineup data is noisy and players often share the floor, advanced techniques are needed to make the numbers stable and meaningful.
What makes this approach especially relevant is that APM sits underneath almost every “all-in-one” metric used today, including those that later incorporate box-score stats or tracking data. If you want to understand how NBA teams think about player value , and what skills are expected in basketball analytics roles , this is essential reading.
You will be able to replicate this and create a portfolio project much more useful than plain Iris or Titanic dataset. Show what you can do, make the most of the resources we give you here!
Link to the article: https://www.roycewebb.com/p/nba-adjusted-plus-minus-how-to-build
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