[Fastbreak Data] Using Machine Learning to Find the 8 Types of Players in the NBA

Alex Cheng is Founder of Fastbreak Data and a guest contributor for Ball and One. Follow his work at fastbreakdata.com.

Introduction

Using machine learning, my goal is to uncover the positions that are intrinsic to today’s NBA players and classify players with a position that best encapsulates their skill sets.

Inspired by the topological data analysis of Muthu Alagappan in “From 5 to 13: Redefining the Positions in Basketball” and the “Periodic Table of NBA Elements” by Stephen Shea in Basketball Analytics: Spatial Tracking, this study proposes an alternative method in which to classify players in today’s NBA….

Click the image below to read the full article. Or you can click here to read it on my blog, Fastbreak Data.

Click on the image above to view the full article at fastbreakdata.com

Why the Golden State Warriors are Absolutely Fine

Let me just say that all this fanfare about the Warriors being in trouble is way overblown.

A team on pace to win 64 games hits a rough patch, and all of a sudden it seems like the entire sports media world piles on with completely baised and random stats to prove how much the team is in trouble.

Only a half game ahead of the Spurs! Stephen Curry 0-5 with under 10 seconds remaining! Warriors playing worse without KD (who saw that coming?)! Nevermind that the Warriors have played 8 games in 13 days, all in different cities, and 17 of the last 24 games on the road. At the end of the day, after the trade deadline, and even after KD’s sprain, the Warriors are still favored to take the championship this year – and for good reason. Let’s dive into the numbers.

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