Basketball Isn’t a Sport. It’s a Statistical Network

[Love Pulse]

A basketball fanatic and a math whiz want to do for basketball what Bill James and sabermetrics did for baseball, and their innovative way of parsing data could revolutionize game analysis, providing coaches with new insights while making the game more fun to watch.

Sabermetrics, for those who haven’t seen Moneyball, is the objective analysis of baseball using game stats. Billy Beane used it to revolutionize the Oakland A’s. Compared to baseball, though, basketball is much more dynamic, and ball movement becomes a key variable in success. Passing is one of the fundamentals of hoops, and in the upper ranks of the sport, turnovers - often the result of wayward passes - contribute to ticks in the win-loss column. Fast, agile passing can make or break a team.
That’s why sabermetrics might not tell the entire story about what happens on the court. Researchers at Arizona State University, led by life science professor and basketball fan Jennifer Fewell and math professor Dieter Armbruster found an ideal model to explain the results of the 2010 NBA playoffs by simply keeping their eye on the ball. Their work opens the door to an entirely new line of sports analysis, from game-tape breakdown to highlight reels and augmented-reality visualizations.

To analyze basketball plays, Fewell and Armbruster used a technique called network analysis, which turns teammates into nodes and exchanges - passes - into paths. From there, they created a flowchart of sorts that showed ball movement, mapping game progression pass by pass: Every time one player sent the ball to another, the flowchart lines accumulated, creating larger and larger and arrows.

Using data from the 2010 playoffs, Fewell and Armbruster’s team mapped the ball movement of every play. Using the most frequent transactions - the inbound pass to shot-on-basket - they analyzed the typical paths the ball took around the court.