By: Dr. Ikjyot Singh Kohli It seems that one cannot turn on ESPN or any YouTube channel nowadays without the ongoing debate of whether Michael Jordan is better than Lebron, what would happen if Michael Jordan played in today’s NBA, etc… However, I have not seen a single scientific approach to this question. Albeit, it […]

# Tag: Data Science

By: Dr. Ikjyot Singh Kohli The Golden State Warriors have posed quite the conundrum for opposing teams. They are quick, have a spectacular ability to move the ball, and play suffocating defense. Given their play in the playoffs thus far, all of these points have been exemplified even more to the point where it seems […]

By: Dr. Ikjyot Singh Kohli So, I came across this article today by Matt Moore on CBSSports, who basically once again has taken to the web to bash the Triangle Offense. Of course, much of what he claims (like much of the Knicks media) is flat-out wrong based on very primitive and simplistic analysis, and […]

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 […]

I have now made a significant update to my applied machine learning paper on predicting patterns among NBA playoff and championship teams, which can be accessed here: arXiv Link .

In a previous article, I showed how one could use data in combination with advanced probability techniques to determine the optimal shot / court positions for LeBron James. I decided to use this algorithm on the Knicks’ starting 5, and obtained the following joint probability density contour plots: One sees that the Knicks offensive strategy […]

Where is Lebron James most effective on the court? Based on 2015-2016 data, we obtained from NBA.com the following data which tracks Lebron’s FG% based on defender distance: From Basketball-Reference.com, we then obtained data of Lebron’s FG% based on his shot distance from the basket: Based on this data, we generated tens of thousands of […]

In this article, I will use Data Science / Machine Learning methodologies to break down the real factors separating the playoff from non-playoff teams. In particular, I used the data from Basketball-Reference.com to associate 44 predictor variables which each team: “FG” “FGA” “FG.” “X3P” “X3PA” “X3P.” “X2P” “X2PA” “X2P.” “FT” “FTA” “FT.” “ORB” “DRB” “TRB” […]

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 […]

Consider modelling the Clinton/Trump debate via a static game in which each candidate can choose between two strategies: , where denotes predominantly “attacking” the other candidate, while denotes predominantly discussing policy positions. Further, let us consider the mixed strategies for Clinton, and for Trump. That is, Clinton predominantly attacks Trump with probability , and Trump predominantly […]