As usual, here is the post-game breakdown of Game 2 of the NBA Finals between Cleveland and Golden State. Using my live-tracking app to track the relevant factors (as explained in previous posts) here are the live-captured time series: Computing the correlations between each time series above and the Golden State Warriors point difference, we […]
Tag: Statistics
Using my live tracking app combined with the relevant factors based on this previous work, here is my breakdown of what contributed to the Warriors win in Game 1 of the NBA Finals. First, here is the time series graph of several predictor variables: Breaking this down a bit further, we have: Computing the correlations, […]
Here is the collection of time series of relevant predictor variables captured live during Game 7 of the Western Conference Finals between The Oklahoma City Thunder and The Golden State Warriors: Another video animation: https://twitter.com/dr_ikjyotsinghk/status/737694089437716480 Many commentators are making a point to mention how many three point shots The Warriors made, suggesting that that was […]
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 […]
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 […]
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 […]
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!
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 […]
As usual, Phil Jackson made another interesting tweet today: Never seen anything like SCurry? Remind you of Chris Jackson/ Mahmoud Abdul-Rauf, who had a short but brilliant run in NBA? — Phil Jackson (@PhilJackson11) February 28, 2016 And, as usual received many criticisms from “Experts”, who just looked at the raw numbers from each players, and […]
Our new paper was accepted for publication in Physical Review D. The goal of the paper was to calculate the probability that a multiverse could emerge from a more general background spacetime, in this case, Bianchi Type I coupled to a chaotic inflaton potential. Basically, we found that a multiverse being generated from such a […]
Relativity Digest 