Earlier this fall, we introduced non-negative matrix factorization to help us analyze field goal trends within the NBA and applied it to the 2017-18 NBA season. There we compared the Boston Celtics and Houston Rockets to show the immense discipline the Rockets displayed while playing “Moreyball.” That is, a high propensity to take three point…
Over the day following a Derrick Rose 50-point performance, Jalen Rose commented that Derrick Rose took 653 dribbles over the course of the game; contrasting to a 52-point performance from Klay Thompson that only took 56 dribbles. The aim of the comment was to identify the difference between the players’ role within the offense. Thompson,…
In the era of tracking data, a need for a new style of analysis has emerged. Long gone are the regularized regression models and the simple counting techniques. Instead, we require leveraging shot-noise distributed systems such as Dan Cervone’s competing risks model, or Matthias Kempe’s self-organizing maps, or Peter Carr’s Imitation Learning. The list is…
Recently, we posted about a way to redefine gravity in terms of a joint model that can be used to quantify defensive interaction, offensive spacing, and spatio-temporal motion of a possession. The result yields a sparse N x 2P x Q x R x S x Z tensor that characterizes the interactions of players, locations on the court,…
Back in 2014, STATS Inc. introduced the notion of gravity, which was explained by Kevin Pelton as a series of two measures: gravity score and distraction score. In Pelton’s explanation, gravity score measures the relative distance a primary defender strays from their offensive counterpart and distraction score quantifies the amount of attention that same defender…
In a recent 2017 paper posted by Andrew Miller (Harvard University / Philadelphia 76ers) and Luke Bornn (Simon Fraser University / Sacramento Kings) titled “Possession Sketches: Mapping NBA Strategies,” the duo takes a well-known manifold learning technique called trajectory analysis and develops a methodology of classifying NBA actions through the use of functional mapping of…
In a recent post, we took a look at identifying how a team distributes the ball on offense with a deep dive look at the Brooklyn Nets. In that article we identified how to construct a community; the sets of likely passes for scores between players. This also included two-pass assists (hockey assists) where it…
As a point guard growing up, I found that driving with my dominant shooting hand would typically put my shooting hand away from the basket. And being undersized at the position (5’4″, 95 pound Sophomore) made life more difficult to shoot off the dribble. Instead, I developed my non-dominant hand, which gave me two options…
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…
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…
Squared Statistics: Understanding Basketball Analytics 