With the new year coming up, we will be posting our NCAA Rankings based on single-season, non-prior induced, metrics for predicting who should make the NCAA tournament. Every year, we typically score between 65 and 68 teams correct; last year being a bust with 64 teams. As a side note, three of the four missed…
Once of the core applications for tracking data is the ability to apply machine learning to gain insight into player tendencies. Unfortunately, due to small samples, we cannot simply measure a particular player’s track paths and say “this player tends to do x.” Instead, we must adopt methods that lift information off a player and…
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…
How many five-man rotations have been employed in the league this season? How often do players play together on the court? What are the offensive and defensive ratings associated with every five-man unit this season? Does one particular line-up outscore opponents? Some of these questions can be answered with the following data below. Each link…
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…
Squared Statistics: Understanding Basketball Analytics 