Measuring Attack Vectors of Ball-Handlers — Squared Statistics: Understanding Basketball Analytics

As a point guard growing up, I found that driving with my dominant shooting hand would typically put my shooting hand away from the basket. And being undersized at the position (5’4″, 95 pound Sophomore) made life more difficult to shoot off the dribble. Instead, I developed my non-dominant hand, which gave me two options […]

via Measuring Attack Vectors of Ball-Handlers — Squared Statistics: Understanding Basketball Analytics

Deep Dive with Python: Offensive Ratings — Squared Statistics: Understanding Basketball Analytics

The calculation for Offensive Rating, another fruitful Dean Oliver metric, is simple: compute the number of points produced when a player is in the game per 100 possessions that the player is in the game. The computation is performed at a “per possession” rate and scaled out to 100. The challenge lies at being restricted […]

via Deep Dive with Python: Offensive Ratings — Squared Statistics: Understanding Basketball Analytics

Evaluating Assists with Python: Community Detection and the Brooklyn Nets — Squared Statistics: Understanding Basketball Analytics

A common question about identifying player tendencies on offense is to ask “how likely is this player to receive the ball during a possession?” This methodology can be aided by the quantity touches. However, a player can touch the ball with what I like to term as an empty touch. These are touches that have […]

via Evaluating Assists with Python: Community Detection and the Brooklyn Nets — Squared Statistics: Understanding Basketball Analytics

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