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

]]>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

]]>]]>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 […]

]]>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. […]

]]>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 […]

]]>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 […]

]]>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 […]

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

]]>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

]]>]]>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 […]