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Basketball Data Analytics Data Science Mathematics NBA NBA Finals NBA Playoffs Sports Statistics

How to Beat the Golden State Warriors

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 […]

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Basketball Data Analytics Data Science Mathematics NBA NBA Playoffs Sports Statistics

The “Interference” of Phil Jackson

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 […]

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Basketball Data Analytics Data Science Mathematics NBA NBA Finals NBA Playoffs Sports Statistics

Basketball Machine Learning Paper Updated 

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 . 

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The Trump Rally, Really?

Today, The Dow Jones Industrial Average (DJIA) surpassed the 20,000 mark for the first time in history. At the time of the writing of this posting (12:31 PM on January 25), it is actually 20,058.29, so, I am not sure if it will close above 20,000 points, but, nevertheless, a lot of people are crediting this […]

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Basketball Data Analytics Data Science Mathematics NBA NBA Finals NBA Playoffs Sports Statistics

The Most Optimal Strategy for the Knicks

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 […]

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Basketball Data Analytics Data Science Mathematics NBA NBA Finals NBA Playoffs NCAA Statistics

Analyzing Lebron James’ Offensive Play

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 […]

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Data Analytics Data Science Mathematics Politics Science Statistics

The Relationship Between The Electoral College and Popular Vote

An interesting machine learning problem: Can one figure out the relationship between the popular vote margin, voter turnout, and the percentage of electoral college votes a candidate wins? Going back to the election of John Quincy Adams, the raw data looks like this: Electoral College Party Popular vote  Margin (%) Turnout Percentage of EC John […]

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Basketball Chicago Bulls Data Analytics Data Science Mathematics NBA NBA Finals NBA Playoffs Science Sports Statistics Toronto

Breaking Down the 2015-2016 NBA Season

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” […]

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Basketball Chicago Bulls Data Analytics Data Science Mathematics NBA NBA Finals NBA Playoffs NCAA Sports Statistics

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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Basketball Data Analytics Data Science NBA NBA Finals NBA Playoffs Sports Statistics

Basketball Paper Update

Everyone by now knows about this paper I wrote a few months ago: http://arxiv.org/abs/1604.05266 Using data science / machine learning methodologies, it basically showed that the most important factors in characterizing a team’s playoff eligibility are the opponent field goal percentage and the opponent points per game. This seems to suggest that defensive factors as […]