Relationship Between TS% and eFG% by Justin Jacobs

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

The Euroleague 2017/18 mega preview by BallinEurope

Our long nightmare is finally over, Euroleague is back. Grab your bag of cans, light up whatever you’re lighting up, and strap yourself in for 30 rounds of basketball that matters. Emmet Ryan breaks down the big storylines to watch and makes his predictions for the Final Four, the eventual champions, along with the major…

via The Euroleague 2017/18 mega preview — BallinEurope

How NBA Draft Lottery Probabilities Are Constructed by Justin Jacobs of Squared Statistics

On September 28th, the NBA Board of Governors approved changes to the NBA draft lottery system. These changes were construed in an attempt to help avoid tanking in the league in an effort to maximize a respective team’s probability of obtaining a high draft pick. In part, this is not a bad effort as we […]

via How NBA Draft Lottery Probabilities Are Constructed — Squared Statistics: Understanding Basketball Analytics

Using Random Forests to Forecast NBA Careers by Justin Jacobs of Squared Statistics

A great bit of insight on career progression in basketball and especially in the NBA. A clear performance based outlook and how player careers can look like!

We need to admire the maths involved and try to implement this across international basketball.

Ermay Duran

Advance Pro Basketball

 

Consider, for a moment, being a General Manager for an NBA team that is faced with determining the number of years for a player contract. The problem seems simple: a team requires a certain skill set that a player possesses and they would like to know for how long a player would be able to […]

via Using Random Forests to Forecast NBA Careers — Squared Statistics: Understanding Basketball Analytics

The projected Top 30 centers for 2017-18 by HoopsHype

Our series projecting the Top 30 players at each position comes to an end with the best centers in the NBA, and in case you missed them you can check out the previous projections for point guards, shooting guards, small forwards and power forwards. Among centers, there are interesting and distinct player types, and often…

via We have projected the Top 30 centers for 2017-18 — HoopsHype

The Art of the Time Out by Euroleageeks

To measure the effectiveness of a TO we must first understand how TOs are used. TOs are called for primarily two reasons: To plan a quick play To put a stop in the momentum of the opposing team and convey some quick useful tips As it is impossible for us to measure the success of a quick TO play, our analysis will focus on the latter objective.

via The Art of the Time Out — Euroleageeks

Building a Simple Spatial Analytic: Passing Lane Coverage by Justin Jacobs (Squared Statistics)

I know it might seem like we have been re-sharing a lot by Justin these days however given the amount of research and clients we deal with, Justin does tremendous research and as the premium public basketball analytics website of Europe it falls on us to re-share his amazing work.

Thank you again Justin, keep it up!

In a recent blog post on defending the Hammer Offense, I showed that the quantification of distance to passing lane helps identify the coverage a defender has on an opposing player.In that very post, I showed only a graphic and did not give insight into how to compute this quantity. Today, we will walk through […]

via Building a Simple Spatial Analytic: Passing Lane Coverage — Squared Statistics: Understanding Basketball Analytics

Game Score: Focus on Scoring by Justin Jacobs

As an avid user of the Game Score metric within European Basketball here is a great read by Justin once again with regards to the metric that John Hollinger created.

Enjoy!

While I’m on a flight between Albuquerque to Oakland, let’s take a quick glance at another advanced analytic: Game Score. Game score is a metric that was developed by John Hollinger (one of the Godfathers of basketball analytics) to quickly give a rough estimate of a player’s contribution to a game. If a player scores […]

via Game Score: Focus on Scoring — Squared Statistics: Understanding Basketball Analytics

Deep Dive on Regularized Adjusted Plus Minus II: Basic Application to 2017 NBA Data with R by Justin Jacobs

In our previous post, we introduced the theory associated with Regularized Adjusted Plus Minus (RAPM) through an illustrative example. In this post, we walk through a vanilla-flavored methodology for building a RAPM model for NBA data. In this article, we focus on the data necessary, the required data manipulation process, and methodology for determining required […]

via Deep Dive on Regularized Adjusted Plus Minus II: Basic Application to 2017 NBA Data with R — Squared Statistics: Understanding Basketball Analytics

Deep Dive on Regularized Adjusted Plus-Minus I: Introductory Example by Justin Jacobs

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