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Basketball – A Game of Geometry

In a previous post, I described the most optimal offensive strategy for the Knicks based on developing relevant joint probability density functions. In this post, I attempt a solution to the following problem: Given 5 players on the court, how can one determine (x,y) coordinates for each player such that the spacing / distance between each […]

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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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Analyzing Lebron James’ Offensive Play

Where is Lebron James most effective on the court? Based on 2015-2016 data, we obtained from the following data which tracks Lebron’s FG% based on defender distance: From, 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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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 […]