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

Distributional Analysis of Free Throws and the Denver Nuggets — Squared Statistics: Understanding Basketball Analytics

In possession models and analytics such as RAPM, the ability to count free throws is crucial. Any miscalculation in computing free throws can result in an unintended dire consequence. In the case of a possession, a team’s possession may be calculated with bias and therefore comparing two teams using per possession stats becomes a flawed […]

via Distributional Analysis of Free Throws and the Denver Nuggets — Squared Statistics: Understanding Basketball Analytics

Developing a Cross-Product Analytic: Kidd Score — Squared Statistics: Understanding Basketball Analytics

In a recent podcast by Sixers Science, an analytic called the Kidd Score was unveiled. The goal of the analytic is to identify players who are great at two ancillary tasks: assists and rebounds. These two components are part of the big three statistical categories that make up the traditional triple double: points, rebounds, assists. […]

via Developing a Cross-Product Analytic: Kidd Score — Squared Statistics: Understanding Basketball Analytics

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

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

Deep Dive on Regularized Adjusted Plus-Minus I: Introductory Example — Squared Statistics: Understanding Basketball Analytics

Let’s start with a simple exercise. Suppose we have a three-on-three game, where there are five players on each team. If the game results in Team A defeating Team B by a score of 54 – 53; how can we determine each player’s contribution? We will identify the players as A1, A2, A3, A4, and […]

via Deep Dive on Regularized Adjusted Plus-Minus I: Introductory Example — Squared Statistics: Understanding Basketball Analytics