# The Potential Assist

In a fairly comical article back in February 2018, Bleacher Report identified the League’s Least Valuable Shooters. In this article, Adam Fromal examined players around the league by extracting their field goal percentage from four particular zones on the court: 3-10 feet, 11-16 feet, 17′-3pt, and 3PA. Fromal would then calculate each player’s points per…

# A Methodology for Qualitatively Comparing Games

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…

# True Shooting Percentage Part I: Introduction and Framework for Advancement

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…

# Random Manatees: The Art of Ranking Players

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…

# Applying Role Alignment to Tracking Data

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…

# Skayton Ayton: A Look into Spacing and Putting Bigs on Skates

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…

# Modeling the Pass

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…

# Current Shooting Trends in the NBA

Earlier this fall, we introduced non-negative matrix factorization to help us analyze field goal trends within the NBA and applied it to the 2017-18 NBA season. There we compared the Boston Celtics and Houston Rockets to show the immense discipline the Rockets displayed while playing “Moreyball.” That is, a high propensity to take three point…

# The “Wisconsin Stat”

As a kid growing up in the Los Angeles area, playing in AAU, the Nike leagues, and the Marmonte; we had a team that was a little ahead of its time: take three’s, switch everything, run teams into the ground by scoring before the defense was set, and force every opponent shot in the short…