Below, we demonstrate why trying to identify a single player as the “Greatest of all time” is a fallacy.
Tag: Analytics
Using a lot of data wrangling and NLP, I created a movie and tv show recommender: Basically, the user selects from the drop-down list their movie/tv show of interest, and the algorithm will recommend a combination of movies and tv shows that the user is most likely to be interested in. I tried to include […]
Many textbooks when introducing ordinary least squares as a method to do regression (usually via introducing linear regression) don’t explain / derive the coefficients. Here, I explain how to do it using tensor/index notation.
I wrote an extensive application using NLP and TensorFlow/Keras in Python that looks at all of the current and upcoming Hollywood releases for 2020 and tracks the online Twitter sentiment for each of them. The model output was then displayed in a PowerBI dashboard. In essence, we are predicting the classification probability . You can […]
Abstract: It is shown that the standard/common definition of team offensive rating/offensive efficiency implies that a team’s offensive rating increases as its opponent’s offensive rebounds increase, which, in principle, should not be the case. Over the past number of years, the advanced metric known as Offensive Rating has become the standard way of measuring a basketball […]
Here is an embedded dashboard that shows a number of statistical insights for NBA teams, their opponents, and individual players as well. You can compare multiple teams and players. Navigate through the different pages by clicking through the scrolling arrow below. (The data is based on the most recent season “per-game” numbers.) (If you cannot […]
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 […]
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 […]
Relativity Digest 