# Boston vs. The Field: Defensive 3PT%

An annual discussion that takes place roughly around the start of every NBA season is whether teams are “good” at perimeter defenders. This discussion arises due to spurious, early returns on defensive three point percentages. This year is no different as the Twitter feed becomes log-jammed with discussion about whether there is a “leave the…

# Extending Possessions: Geometric Distribution

In a recent post, we took a brief glimpse at offensive rebounding and discussed some pro’s and con’s about crashing for rebounds and provided an illustration about where rebounds go after missed attempts. In one such instance, you would have seen this plot: For the sake of argument, let’s suppose that every single red circle…

# Story Underneath Usage: Incompleteness

In the era of possession-based statistics, we often look at items such as per-possession or per-100 possessions. This type of parsing makes sense as a possession is deemed to be the period of time a team “controls” the basketball. The technical definition of a possession defines the end of the “control” period as the point of…

# Manifold Nonparametrics: Which Way Do Passers Pass?

As a player traverses across the court, they break down and process every event that they see: the assertion of defensive players, the alignment of their teammates, and current state of the game. The ability to perceive all three components simultaneously is what I call court vision. It is one of the primary instigators that…

# Exercising Error: Quantifying Statistical Tests Under RAPM (Part IV)

In the Regularized Adjusted Plus-Minus (RAPM) model, one of the perceived challenges is understanding the error associated with the resulting posterior RAPM value a player receives. In a previous post, we noted that RAPM is a Bayesian model in which we assume that “player contribution” can be estimated through weighted offensive ratings conditioned on the…

# Considering Consistency of an Analytic

Warning: This is a lecture from one of previous statistics courses taught over the years. It will be theoretically heavy, but offers insight on some of the research process required for developing analytics. (End Disclaimer)   Thought Exercise: Perimeter Defense Whenever we develop an analytic to help describe the game, we typically have to ask three things.…

# The Randomness of Ratings

Fresh off a Boston Celtics sweep of the Indiana Pacers, across Twitter phrases of “Remember that the Boston starters were -3.5 Net Rating for the series” piped up. The implication was that the Boston starters should have lost the series, and the most common rumblings came about the depth of Indiana’s bench costing them the…

# From the Ground Up: Points Scored

The end goal for all basketball analytics is to gain wins. In order to gain wins, a team must score more points than their opponent. It may seem like a completely obvious and yet, vague statement; but this is the reality: how does this “newfound” intelligence gain wins? In sports, if this intelligence is proprietary,…

# An Example in Kullback-Leibler Divergence

Consider the following table: This is Kevin Durant‘s percentage of field goal attempts, aggregated by specific distance for the first two seasons of his career. This table gives some information, indeed, however does it really paint the picture of where Durant takes his shots? More importantly, are we able to make proper decisions about the…

# Stop Rate

In a recent game between the Milwaukee Bucks and the Detroit Pistons, the Bucks employed their typical switch, slip, and show match-up zone defense to clog the paint and swat shooters off the perimeter en route to a 115-105 victory. The methodology is fairly straight forward: On the perimeter, try to slips guards and drop…