Analytics Art: NBA passing

By Andrew Bergmann, for NBA.com

Click to expand

Here’s a look at how starters on all 30 NBA teams share the basketball.

(Click graphic to expand)

The thickness of the gray lines on the accompanying chart represents the average number of passes per game between two players.

A very clear picture emerges on which teams distribute the ball more evenly between players, such as the Nets, Bulls and Cavaliers. On the flip side, Chris Paul and Blake Griffin dominate passing for the Clippers, and likewise for Kevin Love and Ricky Rubio of the Timberwolves.

This data would have been virtually impossible to collect in the past. However, all NBA arenas are now equipped with SportVU technology. Several cameras are installed overhead which are used to track every move of the game.

Click on the chart for a bigger version.

The top level center, point guard, shooting guard, small forward and power forward were selected from all team depth charts.

The average number of passes were calculated from data from this season only (through Tuesday), taking into account only those games where all five players were active and saw playing time.

In-bounds passes were excluded.

Line thickness represents combined passing in both directions between two players.

Special thanks to Matt Scott and Ryan Warkins.

Andrew Bergmann’s data driven design work can be found on CNN, NBA Digital, Sports Illustrated, FastCompany and Visual.ly. See more on www.dubly.com and twitter.com/dubly

4 Comments

  1. mikeromor says:

    Test

  2. chrissng0 says:

    I don’t follow NBA and the players, but does the layout suggest that the placement of the nodes consistent in terms of court position?

    You could do this with a PivotGraph, player names vs court position, and further understand how one position share the ball with another position on the court.

  3. chrissng0 says:

    I don’t follow NBA and the players, but does the layout suggest that the placement of the nodes consistent in terms of court position?

    You could do this with a PivotGraph, player names vs court position, and further understand how one position interact with another position on the court.

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