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 […]

# Category Archives: Uncategorized

## Where do assists come from? (Part 1)

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 […]

## Elam Ending Analytics

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 […]

## Clustering NBA Shot Charts (Part 2)

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 […]

## Clustering NBA Shot Charts (Part 1)

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 […]

## How useful (or useless) are preseason statistics for rookies?

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 […]

## A Quick 3 or a Quick 2?

Nearly every day in the NBA (playoffs included), there are close games that come down to the wire. We see teams with 3, 4, or 5-point deficits with only a shot-clock remaining quite often, and one of the questions commentators always ask during this situation is: Do you go for the quick 2 and intentionally […]

## Playmaking in the Playoffs vs. the Regular Season

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 […]

## Usage Rate – Regular Season vs. Playoffs

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 […]

## Player Trajectories

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 […]