If you are an NBA fan, you are also likely a stat junkie. PPG, FG%, AST, REB, BLK, STL, etc. – everything is calculated, every single player, every single NBA game.
You can then analyze those stats and say, “Oh – Kobe Bryant is not playing well in the past 10 days” or “Kyle Korver is better at his 30’s than he was at 20’s in his three point shooting percentage.”
But one NBA player – now retired – takes those stats deeper, analyzing the NBA big data. His name is Shane Battier.
Shane was a two-time NBA Champs with the Miami Heat in 2012 and 2013. He retired in 2014, leaving a legacy as one of the toughest defenders of the game. He’s known for taking the role in guarding NBA’s most prolific players, which include Kobe Bryant.
But how he manages to defend those guys, while others seem reluctant to take the role? Mindset and practices make the difference, but there’s one more thing that gives Shane the advantage: Big data analytics.
Shane uses the Los Angeles Lakers and Kobe Bryant as examples. Shane shared that analyzing the big data reveals that the Lakers score 0.98 point per possession. Digging deeper reveals that Kobe Bryant, although difficult to defend, has a ‘skill gap’ when he’s driving or shooting from his left side. The analytics reveal that Kobe just made 0.88 point per possession – below the Lakers’ average. That 0.1 difference can impact the outcome of a competitive sport like basketball.
To take action on the analysis, Shane – when guarding Kobe Bryant – will try to push Kobe to move to his left side, to reduce the likelihood of him scoring.
Let’s hear Shane’s explanation here:
This is one good example of the application of big data analysis in sports. Analyzing the big data will give you the insights you can’t see with ‘bare eyes.’
So, what do you think of big data analytics? Do they matter more than most people think?
