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…
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…
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…
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…
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…
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,…
On December 31st, James Harden dropped yet another 40+ points triple double on the Memphis Grizzlies in a 113 – 101 victory in Houston. It wasn’t the 43 points, 10 rebounds, and 13 assists that was the most impressive stat of the night, but rather the fact that Harden scored 43 points to spite making…
Over the previous couple seasons, I have written extensively about how Regularized Adjusted Plus-Minus (RAPM) is constructed, what the assumptions really mean, and how we interpret the results. If you’re curious for a refresher, feel free to remind yourself here. There’s an example in there that clearly breaks down how various forms of adjusted plus-minus…
Over the course of the years, NBA Stats has released a variety of information about passing in games. A few years ago, we could directly query the “single-hop” passing network between players. It allowed me to then perform a topical analysis such as Kobe Bryant’s role within the Los Angeles Lakers’ offense. Unfortunately, little information…
As of the morning of November 20th, the Four Factors line for the 7-9 Dallas Mavericks read off as follows: Effective Field Goal Percentage: 0.527 (8th in the league) Turnover Percentage: 14.4 (28th in the league) Offensive Rebound Percentage: 22.6 (18th in the league) Free Throw Rate: 0.22 (7th in the league) Opponent eFG%: 0.527…
Squared Statistics: Understanding Basketball Analytics 