In a previous post, I showed how given random positions of 5 players on the court that they could “fill” the triangle. The main geometric constraint is that 5 players can form 3 triangles on the court, and that due to spacing requirements, these triangles are “optimal” if they are equilateral triangles. Given that we […]
Category: NBA Finals
As I write this, Cleveland just won the series 4-3. What was behind each team’s wins and losses in this series? First, Golden State: A correlation plot of their per game predictor variables versus the binary win/loss outcome is as follows: The key information is in the last column of this matrix: Evidently, the most […]
I’ve been fascinated by the triangle offense for a long time. I think it is a beautiful way to play basketball, and the right way to play basketball, in the half-court, a “system-based” way to play. For those of you that are interested, I highly recommend Tex Winter’s classic book on the topic. There is […]
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 […]
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 […]
I’ve been ranting a lot about the so-called “value” of the three-point shot in “modern-day” basketball. I know! But, here is yet one more entry. The common consensus is that teams are shooting more three point shots as discussed in the articles below: http://www.businessinsider.com/nba-three-point-shooting-2016-3 http://www.nba.com/2014/news/features/john_schuhmann/11/07/history-of-the-three-point-shot/ http://nyloncalculus.com/2016/03/08/three-pointers-and-skill-displacement/ There are several more where these have come from. […]
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 […]
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 […]
Relativity Digest 