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## What are the factors behind Golden State’s and Cleveland’s Wins in The 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 […]

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## Game 2 of CLE vs GSW Breakdown

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 […]

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## Game 1 of CLE vs GSW Breakdown

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, […]

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## Breakdown of Game 7 between OKC and GSW

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 […]

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## Metrics for GSW vs. OKC Game 6 Second Half

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 […]

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## Live Metrics for NBA Games

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 […]

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## Basketball Paper Update

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 […]

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## New Paper on Machine Learning and Basketball

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!

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## How close were The Knicks to making the Playoffs?

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 […]

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## 2016 Michigan Primary Predictions

Using the Monte Carlo techniques I have described in earlier posts, I ran several simulations today to try to predict who will win the 2016 Michigan primaries. Here is what I found: For the Republican primaries, I predict: Trump: 89.64% chance of winning Cruz: 5.01% chance of winning Kasich: 3.29% chance of winning Rubio: 2.06% […]