How The NBA Uses Data & Analytics

randerson112358
Aug 16, 2018 · 6 min read

In my opinion, basketball is a wonderful sport, you can tell a lot about a person from the way s/he played basketball, things like did s/he hog the ball ? Did s/he show off on the court ? Was the person afraid to shoot and miss ? Did the person lie about being fouled? On top of that it’s just fun to play and great exercise !

Also in my opinion Data Analytics/Science is an amazing popular and growing field, so much so that it was named “the sexiest job of the 21st century”. Data science is a mixture of statistics, data analysis, machine learning, computer science, and knowledge of the data / business that aims to provide insights and understanding from data. So, when you put these two fields together, you get an article written by yours truly.

Data Science and Data Analytics have completely changed the NBA landscape, so much so that the league now runs a yearly Hackathon, this allows them to get some new great ideas and find new data analyst with talent.

The Golden state warriors even credit their success to their analyst. You may have seen the coach “Steve Kerr” rest some of his players, well this was due to the fact that the data showed his players needed rest, and this will not only prevent injury, but also allow for their players to be able to play longer, and that’s good both for the Golden State Warriors and the NBA, more winning games usually means more money.

In 2009 the league began using a state of the art video system to track the players movement on the court as well as the ball. Having this new video system allowed the NBA to collect new data which in term allowed data scientists to use machine learning and cartography ( the science or practice of drawing maps) to better assess which players helped their team to win.

The NBA has been using statistics so heavily, that they might surpass that of Major League Baseball (MLB) in using data, which was one of the first US sports league to use data to find hidden insights and patterns to benefit their team. Have you ever heard of “Money Ball” , both the movie and book give a good depiction of the events and use of data that led to a under dog team (Oakland Athletics) winning many games with players who were obviously under valued.

I think it’s interesting that the baseball team that first used the data was Oakland Athletics, and maybe it’s not such a coincidence that the NBA Golden State Warriors a team that seems unbeatable in the NBA the past few years, also uses data to make them successful, oh and did I mention they are also currently located in Oakland.

Most NBA teams have data analysts as staff on their team. These data analyst work with coaches and players to maximize the talents of the players and of course like “Money Ball”, identify under valued players. NBA players use wearable technologies to track their health, avoid injury, and track their fatigue levels. This is both good and bad as this data can help the player, but also hurt the player when it comes to negotiations.

One of the biggest and noticeable changes to the NBA caused by data and analytics is the increase of the three-point shots per game, which was a result of some simple math. The average three-point shot taken in 2012 was about 18.4 three-point shots per game, and in 2017 the average team took about 27 shots per game, that’s a little over 46% more shots!

The NBA Data Scientist

The analysis showed that a three point shot that had a 35 percent chance of going in, on average, led to more points than a two-point shot. So NBA teams and coaches strongly encourage players who are good at shooting 3’s to take as many three-point shots as often as possible. Players like Kevin Durante, Klay Thompson, and Stephen Curry do this all of the time.

The NBA has also used Bayesian statistics to figure out how much better a teams overall defense is when a given player is on the court, as well as evaluating the teams overall defense. This data is good to see which players discourage the most efficient types of shots a.k.a three-pointers and dunks.

Thanks to all of this data old players who were once valued are now near extinction and teams are no longer interested in players who take a lot of inefficient two-point shots and don’t have a strong defense (makes me wonder about James Harden). Every player now is expected to be good passers as well.

In conclusion the NBA’s data revolution has created rosters with more skilled, more well-rounded players that are better rested, and increased the profitability of the NBA. If you would like to learn more about the NBA and data analytics I suggest you read “Basketball on Paper: Rules & Tools for Performance Analysis” , this book is considered to be the Money Ball book of basketball!

https://www.amazon.com/gp/product/1574886886/ref=as_li_tl?ie=UTF8&tag=medium074-20&camp=1789&creative=9325&linkCode=as2&creativeASIN=1574886886&linkId=d04f0224401e7fcd98c8609cb767069e

If you are interested in data science and data analytics after reading this article you should check out the website FiveThirtyEight.com they have many interesting articles and statistics on politics, sports, science, health, economics and culture, you can also check out my blog for more information on free tutorials and books on data analytics/ science. Thanks for reading this article, I hope you found it very helpful and enjoyable as I enjoyed writing it! Happy learning and I will see you all in my next article. Please leave many claps on here and share it, thanks for taking the time out of your busy day to read this article !

Also if you are interested on the data from the SportVU camera that is installed in every arena that hosts the NBA basketball you can get the SportVU Data here, and advanced statistics link here.

Check Out the following for content / videos on Computer Science, Algorithm Analysis, Programming and Logic:

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randerson112358: https://www.youtube.com/channel/UCaV_0qp2NZd319K4_K8Z5SQ

compsci112358:
https://www.youtube.com/channel/UCbmb5IoBtHZTpYZCDBOC1CA

Website:
http://everythingcomputerscience.com/

Video Tutorials on Recurrence Relation:
https://www.udemy.com/recurrence-relation-made-easy/

Video Tutorial on Algorithm Analysis:
https://www.udemy.com/algorithm-analysis/

Twitter:
https://twitter.com/CsEverything

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randerson112358

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A programmer that loves Computer Science, programming, basketball, and machine learning. YouTube Channel: https://www.youtube.com/user/randerson112358

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