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…

Approximating Curves I: Mechanical Process

Now that the 2019-2020 season has ended, let’s take a quick look at something almost every data scientist knows: polynomial projection. Now, if you’re a data scientist and find yourself mumbling, “I’ve never heard of that,” don’t worry: You have. Over the next few posts, we are going to discuss a larger problem of approximating…

Offensive Crashing

Back in high school, it behooved our team to “keep one man back” on offense. The thought process was simple, if the defense were able to get out into transition, our team would at least impede their progress towards the basket with the hopes of them settling into the half-court offense. Sometimes it worked. In…

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…

Warping Play Registration

Synergy: Breaking Down Field Goal Types With the creation of Synergy, the basketball world gained valuable access to previously hard-to-obtain data on all field goal events in the league. One of the biggest introductions was the “primary defender” tag on field goal events. With play-by-play data, when a player drives to the basket or attempts…