Here’s a hill I will die on: The primary goal of spacing in basketball is to manipulate a defense into a providing a high quality shot for an offense. To me, as a former player/coach/scout/analyst/front office specialist, this is intuitive. However, as one Eastern Conference representative responded to me: I’m not sure about that. Spacing…
As a kid growing up in the Los Angeles area, playing in AAU, the Nike leagues, and the Marmonte; we had a team that was a little ahead of its time: take three’s, switch everything, run teams into the ground by scoring before the defense was set, and force every opponent shot in the short…
Back in 2012, on the heels of my PhD in nonparametric Bayesian density estimation, I developed a methodology that constructs a nonparametric Bayesian density and teases out a nice spatio-temporal decomposition of movement on a space of interest. Pointing this at NBA SportVu data, the decomposition kindly identified player tendency in specific types of offenses…
A common methodology for NBA analysts to develop a metric that quantifies scoring ability of a player through the position a player is put in when taking a shot, and the association of a closest defender when the shot is taken. Some such metrics are kernel density plots of field goal percentages on the court, assist adjacency matrices, measure…
In a previous post, we took a look into some spatio-temporal data obtained through SportVU technology for the NBA and identified how to use that data to perform basic tasks such as building convex hulls to illustrate offense and defense coverage on a court; as well as provide basic Python code for the reader to…
With the NBA Playoffs set to get underway, we take a quick look at the probabilities for each team becoming the NBA Champions. Common consensus would place any combination of the five strongest teams: Golden State – Houston – San Antonio versus Cleveland – Boston. But the question is how likely? To answer that, we…
Squared Statistics: Understanding Basketball Analytics 