Offensive and Defensive Efficiency in Basketball Champions League taken from The Athlytics Blog

A recent article described how the volume of three-point shots taken in the Basketball Champions League (BCL) has shown an increasing trend – in alignment with the trend in the NBA that has been observed for several years now and has coincided with the ascent of the analytics movement in the sport. The analytical explanation of […]

via Offensive and Defensive Efficiency in Basketball Champions League — The Athlytics Blog

Usage and Efficiency by Justin Jacobs of Squared Statistics

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 […]

via Usage and Efficiency — Squared Statistics: Understanding Basketball Analytics

Defensive Ratings: Estimation vs. Counting by Squared Statistics: Understanding Basketball Analytics

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 […]

via Defensive Ratings: Estimation vs. Counting — Squared Statistics: Understanding Basketball Analytics

Kinematics of Player Motion by Squared Statistics

After a couple special topics posts in Sketching and Voronoi Tessellation, we take a step back and look at the basic mechanics of player motion: position, velocity, and acceleration. Understanding computation and estimation of such quantities allow us to perform more important calculations such as trajectories, coverage, and crashing. The easiest way to capture these […]

via Kinematics of Player Motion — Squared Statistics: Understanding Basketball Analytics

The Art of Sketching: Trajectory Analysis by Squared Statistics

warriorsTrimmed

No Description

In a recent 2017 paper posted by Andrew Miller (Harvard University / Philadelphia 76ers) and Luke Bornn (Simon Fraser University / Sacramento Kings) titled “Possession Sketches: Mapping NBA Strategies,” the duo takes a well-known manifold learning technique called trajectory analysis and develops a methodology of classifying NBA actions through the use of functional mapping of […]

via The Art of Sketching: Trajectory Analysis — Squared Statistics: Understanding Basketball Analytics

Voronoi Tesselation and Rebounding Position: Defining Distance by Seconds by Squared Statistics

How likely is a player able to rebound a basketball? If you ask Second Spectrum, you will get a function that considers positioning, hustle, and conversion. The argument makes sense: First, a player needs to be in a position to have a chance at obtaining a rebound. Second, the player needs to be able to […]

via Voronoi Tesselation and Rebounding Position: Defining Distance by Seconds — Squared Statistics: Understanding Basketball Analytics

Testing the Quality of a Binary Classifier: ROC Curves by Squared Statistics

Let’s suppose that we have a methodology for classifying players into Hall-of-Fame status. This methodology can be of any type: it can be a random forest that uses proximity matrices or it can be a simple measure that uses a threshold, such as Kidd Score. Either way, the result is the same: a certain number […]

via Testing the Quality of a Binary Classifier: ROC Curves — Squared Statistics: Understanding Basketball Analytics

Understanding the Spatial Tendencies of Assists, the K(t) Test, and the Orlando Magic by Squared Statistics

In a recent post, we took a look at identifying how a team distributes the ball on offense with a deep dive look at the Brooklyn Nets. In that article we identified how to construct a community; the sets of likely passes for scores between players. This also included two-pass assists (hockey assists) where it […]

via Understanding the Spatial Tendencies of Assists, the K(t) Test, and the Orlando Magic — Squared Statistics: Understanding Basketball Analytics

Why use Alley-Oop Consulting for Video Analytics? by Alley-Oop Consulting

 

I have to say that its rare these days that video Analytics is becoming a mainstream solution. With that said it only becomes clearer to us that deal with it that its a need as well as a must…

 

With this post we refer to Alleyoop Consulting’s latest post and how Video Analytics is seen…

I get the coach.ca newsletter and this month they had an awesome article from Coaches Plan Magazine titled 5 Reasons Coaches Should Use Video (https://www.coach.ca/coaches-plan-s16544). Craig Johnson who wrote the article touched on some awesome points some of which I had mentioned in a past post in August. His 5 main points are: 1. DEVELOP […]

via Why use Alley-Oop Consulting for Video Analytics? — Alley-Oop Consulting

Relationship Between TS% and eFG% — Squared Statistics: Understanding Basketball Analytics

Here is a real great insight by Justin, cant appreciate enough on how much maths he spills out these days…

Great to know him and be able to feature him on Advance Pro Basketball.

 

 

In an effort to understand shooting efficiency, terms such as points-per-possession, effective field goal percentage, and true shooting percentage have come about as methods to quantify scoring efficiency. In fact, during my coaching days in Baltimore City (2013 – 2016), I developed a metric called points responsible for (PRF) that focused on distributing points to […]

via Relationship Between TS% and eFG% — Squared Statistics: Understanding Basketball Analytics