QSAO\’s Analytics Mythbusters: Breaking down positional nuances (Part Two)

As we progress through the seasons of various professional sports, we start to notice trends worth looking into. As I have ben to expand my analytical prowess, I have learned to develop my own questions, and look to answer them through the published work of others – something that QSAO does for the sports community (Look out for all-new content next semester! – shameless plug). But I digress, in Part Two of the latest edition of QSAO’s Analytics Mythbusters, we look into load management in the NBA and how the Houston Astro’s sign-stealing scandal affected team performance.  

The Positionless NBA: Grouping players using performance stats

Nikola Jokic is one of the most unique players in the NBA today. He is 7 feet tall but can pass like a point guard, shoot the three and has arguably the best vision among all players regardless of position, however, he is listed as a centre purely based on his height. He is redefining how the league classifies players because as a centre he averaged 7.3 assists per game and brings up the ball regularly for the Nuggets. Basketball is becoming more positionless with each season, so it is a wonder why the NBA continues to segment players with the outdated 5 positions. Instead of putting players into positions based on height and traditional labels, it would be more beneficial to sort the players based on their stats. In this QSAO article, we analyse and group NBA players using important statistical indicators.

Big Baller Data: A Basketball Analytics Guide

By James Acres, Josh Antonucci, Michael Blumel, Cameron Raymond, Cody Smith, and Hunter Smeaton All current stats used are from at time of article\’s publication. As NBA fans, we are constantly bombarded with different statistics. Every evening you look at your phone to see notifications from various apps; triple double for Lebron, 50 pts 10 …

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