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…
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…
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,…
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…
Suppose a game completes and three players post the following stat lines: Player A: 31 points, 13 rebounds, 3 assists Player B: 20 points, 11 rebounds, 9 assists Player C: 20 points, 21 rebounds, 0 assists Frequently, we ask who was the better player or which player contributed the most to the game. Unfortunately, most…
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…
Squared Statistics: Understanding Basketball Analytics 