NBA analytics: Going data pro
For the NBA, like every other sports league, awards are important. They can generate attention, spur debate, make money, and involve fans, players, and experts, among others. Is there data science and analytics behind them — can there or should there be? We picked the NBA Most Improved Player award as an example to analyze some aspects of data-driven culture.
The NBA is announcing its yearly awards today. This is a much-anticipated event that has been talked about and analyzed extensively on sports media and beyond. Predictions and arguments on who should be nominated and who should win each award have been going on almost since the beginning of the season.
Keeping fans engaged is good, but there are more aspects to awards like these: They can give the media something to talk about, boost player and team statuses, and anyone can bet on the results.
Being part of pop culture, and having the potential to make or break careers and fortunes means there’s more to the NBA Awards that meets the eye. Let’s try and peek behind the looking glass and use data science and analytics to answer a question on many NBA fans minds: Who was the most improved player (MIP) in the NBA this season?
To begin with, who gets to define improved, and how? As one NBA writer once put it: “There are few things more frustrating than trying to determine what it means to be the MIP“. On the other hand, that makes it interesting and open to interpretation. Since the NBA does not say much about its criteria and evaluation method, others have tried to come up with their own.