NBA

Shots, Touches and Scoring: PPG Forecasts for 5 NBA Unicorns

In today’s modern NBA, being a “unicorn” is all the rage. According to superstar forward Kevin Durant, he classifies Knicks’ forward Kristaps Porzingis as a unicorn because,
“He can shoot, he can make the right plays, he can defend, and he’s a 7-footer that can shoot all the way out to the three-point line. That’s rare. And block shots – that’s like a unicorn in this league.”
Ironically, it is Durant saying this about another player when he himself is perhaps the ultimate “unicorn.” The most recent Finals MVP-winner showed fans why NBA front offices are searching long and far for the next prospect to fit this mold. When one player can be used on offense as a Swiss army knife, seamlessly fitting into any scheme, while also playing world-class defense – you can contend for championships. Durant just won his first NBA ring. Fellow “unicorn-lite” teammate Draymond Green fits the label to a degree, given his unique versatility. Yet the Warriors are not the only team to possess these types of players. There is a group of rising NBA second-year and third-year studs who fit the mold of “positionless versatile modern big man” that teams and fans crave for. Leading the class are five promising young bigs:
  • C Joel Embiid (PHI)
  • C Karl-Anthony Towns (MIN)
  • PF Kristaps Porzingis (NYK)
  • C Myles Turner (IND)
  • C Nikola Jokic (DEN)
What follows is an analysis of my defined player group. I researched each individual’s tracking stats, to learn how they score points in comparison to one another. Each player is exciting in their own way, and I hoped to find data that could substantiate this. Fortunately, NBA.com’s statistics database contained what I was seeking. The league owns the right to SportVU’s incredible player tracking data, and they release some of it to fans for consumption. Read Nylon Calculus’ article for a brief introduction. I found very insightful information as to how the five players above score points during a game. Although I was initially content to merely observe the data, I quickly descended into a statistical rabbit hole. The future was calling, as I wanted to know what would happen in 2017-18! What began as a curiosity-driven information dive developed into something quite surprising, and useful: a regression model to project scoring stats. What follows are: 1) the results gleaned from the SportVU data about each player, 2) a summary of the “unicorn” points per game model, and 3) forecasting points per game each of these players in 2017-18.

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