It’s been a while since I’ve done an NBA analytics project, but I’ve recently been intrigued by player-player interactions within teams. Oftentimes, fans have a hunch that two players “mesh” well together or two players’ playstyles do not complement one another. However, for the most part, this is a qualitative observation. In this article, I … Continue reading Symbiosis in the NBA
This is a project that I am presenting as a poster at the CMU Sports Analytics Conference. A full version of this research (and associated code) is here: https://github.com/avyayv/CMSACRepo. You may also view the poster I created at: http://www.stat.cmu.edu/cmsac/poster2020/posters/Varadarajan-NBAShotProb.pdf Overview Since the advent of basketball analytics, a metric that is accurately able to determine the … Continue reading Finding Determinants of NBA Shot Probability using Interpretable Machine Learning Methods
A few weeks ago, I did some analysis with archived SportVU Player Tracking data (2015-16), looking at where on the court assists come from. You can read about that analysis at these links: Blog post: https://analyzeball.com/2020/06/02/where-do-assists-come-from/, Specific players: https://twitter.com/avyvar/status/1267189790388056064 League wide trends: https://twitter.com/avyvar/status/1270892658437705733). Here, I’m a deeper dive on this data, looking at assists off … Continue reading Where do assists come from? (Part 2)
I recently developed a win probability model for the awesome py_ball package in Python. The package itself makes NBA/WNBA data accessible to a wide audience. If you haven’t seen it, you should definitely check it out. The link is https://github.com/basketballrelativity/py_ball. In this blog post, I’ll describe the methods I used to develop the model. Methods … Continue reading Playing With Win Probability Models
I recently tweeted some assist heat maps that were generated using 2015-16 SportVU data here. Although the individual player heat maps are interesting, I wanted to look at more league-wide trends. I also wanted to explain my methods a little bit more. Why? The reason why I found this specific problem interesting was because of … Continue reading Where do assists come from? (Part 1)
With the NBA season being postponed, there has been a lack of basketball in the world. As a result, I thought it would be interesting to look into depth about how the Elam Ending has a place in the current NBA and how it would work. What is the Elam Ending? If you didn’t watch … Continue reading Elam Ending Analytics
My previous blog post showed how cluster-able NBA shot charts were. I recently made a few improvements to the model and looked into things that I didn’t look into in the previous article. A quick summary of that article is that I generated a 14 dimensional vector with shot frequencies for different locations on the … Continue reading Clustering NBA Shot Charts (Part 2)
Methodology In the NBA, we often assign labels to players, not really looking in depth on what constitutes these labels. Something that we can do to figure out the “definition” of these labels and see whether these labels actually exist is to use an algorithm known as k-means-clustering to cluster shot charts (to find similar … Continue reading Clustering NBA Shot Charts (Part 1)
Zion Williamson has been phenomenal this preseason for the New Orleans Pelicans. This has led to various opinions in the basketball world on how Zion will perform in the regular season. Some say that Zion is going to be an All-Star in his rookie season. In fact, Stephen A. Smith made the bold claim that … Continue reading How useful (or useless) are preseason statistics for rookies?
The 2019 NBA Playoffs have been excellent, with teams playing at their absolute best. We’ve seen teams like the Warriors and the Bucks absolutely dominate, but how have these teams, along with other teams, changed their playmaking strategies? For instance, if we look at the Bucks in the Playoffs, they have obviously decided to make … Continue reading Playmaking in the Playoffs vs. the Regular Season
When we look at games in the playoffs, we see completely different strategies employed by teams. Star players seem to be more relied on than they would in the regular season, while players with smaller roles seem to be less useful than in the regular season. This ‘hunch’ can be represented with a graph of … Continue reading Usage Rate – Regular Season vs. Playoffs
Using the stats.nba.com API data and matplotlib, I recently developed a pretty cool visual to see how players’ points (adjusted by usage rate and minute) changed over time. This means that an excess of usage leading to extra points will not affect the quantified improvement over time. The graph above shows the average player who … Continue reading Player Trajectories
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