# Understanding Trends in the NBA: How NNMF Works

Over the past decade a true emphasis on three point shooting has emerged. This can be credited to the understanding of effective field goal percentage being a better underlying parameter for points per field goal attempt than raw field goal percentage. And it’s become an emphasis for some top-tier teams such as the Houston Rockets.…

# Second Chances and the Rebounding Specialist

A tried-but-true strategy in pick-up (and grade school) basketball is the “dump-and-chase” offense. This offense takes as many jumpers as possible, regardless of capability, fully knowing that the team can either out-hustle their opponent for rebounds; or they have a behemoth of a rebounder on their team. As players become older, or more technically sound…

# Stochastic Tracking II: Next Gen Solutions and Player Performance

In our previous post on Stochastic Tracking, we took a look at motivating a Hierarchical Bayesian process in filtering tracking data and producing more robust estimators for the velocity. During the discussion, we limited the data sampled to be of two-dimensions only and had to assume that acceleration was constant between sampled points. Due to…

# Computing in the Stream: Schedule Compactification

When we learn about computational mathematics, software engineering, or data science in general, one of the most important questions we have to answer is how many operations are required. The answer isn’t to show the prowess of our algorithm, but rather identify how quickly the algorithm can perform. We need to know if the answer…

# Making Blocks Count

When we measure the defensive impact of a player, typically the first arguments we make are the number of blocks and steals that player has obtained. We celebrate players like Dikembe Mutombo and Maurice Cheeks for their prowess in obtaining blocks (2nd all time) and steals (5th all time), respectively. In the latter case, a…

# Usage and Efficiency

The usage of an NBA player consists of the number of chances a player takes out of the possible chances a team has when that player is on the court. A chance being the number of possessions that can result in a scoring possession. The higher the usage for a particular player, the more likely…

# Offensive and Defensive Ratings

The terms offensive rating and defensive rating refers to the amount of points scored by a team and by the team’s opponents, respectively, over 100 possessions. The normalization to 100 possessions allows for the ability to compare teams as pace of games can skew points per game comparisons. Both ratings are still considered box score…

# Minnesota Timberwolves Offense: Stability, Screens, and Mid-range Game

As of the morning of November 19th, 2017, the Minnesota Timberwolves have found themselves at 10-5, atop the Northwest Division and third on the Western Conference behind the Golden State Warriors (12-4) and the Houston Rockets (13-4). In a recent match-up, the Timberwolves dispatched the San Antonio Spurs 98 – 86 in Minnesota. With the…

# Bradley-Terry Rankings: Introduction to Logistic Regression

In a recent post, we identified the Colley Matrix methodology for ranking NBA teams. The methodology provided insight but abused the originating statistical construct in an effort to enforce a correlated, solvable, set of equations to identify a “probability” of winning. Unfortunately, we witnessed that not only were those statistical assumptions violated, but the resulting…

# Defensive Ratings: Estimation vs. Counting

Defensive rating, a box score calculation, is an estimation procedure that attempts to identify the points per 100 possessions that an NBA player yields in a game. In this calculation, a player’s defensive rating is effectively eighty percent of their team’s defensive rating plus twenty percent of defensive points per scoring possessions when on the…