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## So, What’s Wrong with the Knicks?

By: Dr. Ikjyot Singh Kohli As I write this post, the Knicks are currently 12th in the Eastern conference with a record of 22-32. A plethora of people are offering the opinions on what is wrong with the Knicks, and of course, most of it being from ESPN and the New York media, most of […]

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## Optimal Positions for NBA Players

I was thinking about how one can use the NBA’s new SportVU system to figure out optimal positions for players on the court. One of the interesting things about the SportVU system is that it tracks player coordinates on the court. Presumably, it also keeps track of whether or not a player located at makes […]

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## The Mathematics of “Filling the Triangle”

I’ve been fascinated by the triangle offense for a long time. I think it is a beautiful way to play basketball, and the right way to play basketball, in the half-court, a “system-based” way to play. For those of you that are interested, I highly recommend Tex Winter’s classic book on the topic. There is […]

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## Metrics for GSW vs. OKC Game 6 Second Half

Continuing with the live metrics employed yesterday, here is an analysis of the second half of the Warriors-Thunder Game 6.  Here is a plot of the various time series of relevant statistical variables:  One can see from this plot for example, the exact point in time when OKC loses control of the game.  Further, here […]

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## Live Metrics for NBA Games

Yesterday for the first time, I took the playoff game between Cleveland and Toronto as an opportunity to test out a script I wrote in R that keeps track of key statistics during a game in real time (well, every 30 seconds). Based on previous work, it is evident that championship-calibre teams are the ones […]

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A few weeks ago, I published a paper that used data science / machine learning to detect commonalities between NBA playoff teams. I have now updated and extended it to detect commonalities between NBA championship teams using artificial neural networks, which is a field of deep learning. The paper can be accessed by clicking on the […]

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## New Paper on Machine Learning and Basketball

A new and formal paper of mine describing how one can use machine learn methodologies to help determine which NBA teams will make the playoffs is now online:  arXiv link SSRN link Have a look!

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## How close were The Knicks to making the Playoffs?

It is another New York Knicks season where fans have to wait until next year to see if the Knicks will make the playoffs or not. Yesterday, there was a lot buzz around the idea that Phil Jackson may want to keep Kurt Rambis on as head coach, and as usual, there were numerous people […]

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## What Do NBA Playoff Teams Have in Common?

I’ve been interested for some time on figuring out an analytical way to determine what characterizes an NBA team as a playoff team. Looking at the previous six seasons, I pulled together almost 65 different statistics that characterize how a team plays, and then performed a classification tree analysis. I found the following result:    […]

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## The “Evolution” of the 3-Point Shot in The NBA

The purpose of this post is to determine whether basketball teams who choose to employ an offensive strategy that involves predominantly shooting three point shots is stable and optimal. We employ a game-theoretical approach using techniques from dynamical systems theory to show that taking more three point shots to a point where an offensive strategy […]