Redefining NBA Divisions By Clustering

With the recent announcement that the NBA is changing the format of playoffs by seeding the playoffs based solely on records, we take a look at the complexity and reasoning for dividing out the 30 NBA teams into 6 divisions. For Major League Baseball and the National Football League, divisions serve a major role in scheduling. An…

Proximity of NBA Teams

Many fans of the NBA select their team based on one of four main criteria: they may have grown up in a household that was once within proximity of an NBA team; their favorite player has moved to a particular team; the team is has a history of being a winner; or they have some odd…

Spatio-Temporal Data In the NBA

In our last post we saw that we can use a simple nonparametric statistical method, called the kernel density estimator, to build nice looking scoring charts. As we saw, these charts give a better idea of the distribution of the scorer when compared to the basic shooting chart used for several decades past. In this post,…

NBA Shot Charts via Kernel Density Estimation

Shot charts have been used for decades, used to identify locations where players make field goals and to identify locations where players give up field goals. Decades ago, each player has their own chart with four quarters. Typically if a field goal is made, the player has a ‘o’ is placed on the relative location on…

Score Flows of NBA Teams

Almost every NBA game is played as a game of runs, with teams either piecing together five to ten point runs or going scoreless for a couple minutes at a time. To identify the scoring pace of a team, we can take a look at a simple measurement: the Score Flow. Score flows are popular…