Basketball on avyay's blog

Symbiosis in the NBA

Tue, 22 Mar 2022 00:00:00 +0000

It’s been a while since I’ve done an NBA analytics project, but I’ve recently been intrigued by interactions between players 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 will present a simple, quantitative way of discovering favorable/unfavorable duos in the NBA (in addition to investigating specific duos).

What Affects NBA Shot Probability?

Sun, 25 Oct 2020 00:00:00 +0000

I presented this project as a poster at the CMU Sports Analytics Conference. The full research and associated code and conference poster are available online.

Overview

Since the advent of basketball analytics, a metric that can accurately determine the relative value of a player’s defense has been widely sought after. It is widely regarded that features like shot defense are key to a player’s defensive identity, but regularized on-off metrics like RAPM are unable to take this into account. Using player-tracking data, we are able to extract information about shot defense.

Where Do Assists Come From?

Sun, 05 Jul 2020 00:00:00 +0000

I used archived 2015-16 SportVU player-tracking data to look at where on the court assists come from. Along with league-wide trends, I looked at assists off misses and compared guards, forwards, centers, and individual players.

Why?

I found this problem interesting because of its potential implications.

Players in the NBA—and basketball more broadly—have inherent biases toward where they prefer to shoot. For instance, if a player like Ben Simmons were standing at the three-point line, you wouldn’t guard him as tightly as you would Stephen Curry. Analysis of the specific locations from which players prefer to pass and shoot could help players anticipate what might happen next in a possession and improve defensive strategy.

Playing With Win Probability Models

Sat, 20 Jun 2020 00:00:00 +0000

I recently developed a win probability model for the awesome py_ball package.

In this blog post, I’ll describe the methods I used to develop the model.

Methods

The model estimates win probability using the amount of time remaining, the point differential, and which team has possession. Home-court advantage is the only pregame bias, because every input is represented from the home team’s perspective. We hope to add betting odds to produce more accurate pregame probabilities.

Elam Ending Analytics

Sun, 15 Mar 2020 00:00:00 +0000

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 more deeply at whether 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 the All-Star Game in 2020, the Elam Ending gives each team a target score at the start of a period rather than having them fight against the clock. Rather than having a five-minute overtime or a 12-minute fourth quarter, each team would have to score a certain number of points, based on the higher score in the game.

Clustering NBA Shot Charts

Tue, 24 Dec 2019 00:00:00 +0000

I used k-means clustering to group similar NBA shot charts and identify common shooting tendencies.

Methodology

My approach was to bin groups of shots, much like we sometimes do for visualization. This let me represent the data as a vector showing the frequency of shots from individual locations.

Binned FGM/FGA shot chart for James Harden

I separated shots into 14 locations provided by the stats.nba.com API and created a 14-dimensional vector for each player in each season. Each value represents the player’s shot frequency from one location on the court. I did not include field goal percentage because I wanted to highlight shooting tendencies rather than shooting ability.

Playmaking in the Playoffs vs. the Regular Season

Mon, 20 May 2019 00:00:00 +0000

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 clearly decided to have Giannis drive into the paint more and pass out less. This is because Giannis’s attempts in the paint generate more points per shot (field goal percentage multiplied by two) than a typical three-point shooter does.