CourtVision: NBA Visual and Spatial Analytics

NBA League-Wide Shot Attempt 2006-2011
NBA League-Wide Shot Attempt 2006-2011

Abstract

The graph depicts five years of every NBA shot attempt from 2006 to 2011, visualization work credited to Prof. Kirk Goldsberry, who teaches geography at Michigan State University. He published a paper titled “CourtVision: New Visual and Spatial Analytics for the NBA” while he was a PhD at Harvard. (Paper link: http://www.sloansportsconference.com/wp-content/uploads/2012/02/Goldsberry_Sloan_Submission.pdf) He introduces CourtVision, what he called “a new ensemble of analytical techniques designed to quantify, visualize, and communicate spatial aspects of NBA performance with unprecedented precision and clarity.” He believes that visual and spatial analyses represent vital new methodologies for NBA analysts.

Introduction

Prof. Goldsberry presents a whole new way to look at NBA data. His paper on CourtVision investigates spatial and visual analytics as means to enhance basketball expertise. Goldsberry and his research team propose a new way to quantify and visualize NBA player’s shooting ability with unprecedented precision and clarity. In the paper, an exploratory case study is introduced. Goldsberry attempts to examine spatial shooting behavior and performance for every NBA player by applying his CourtVision method. He concludes with evidence that Steve Nash and Ray Allen have the best shooting range in the NBA.

Hypotheses

Who is the best shooter in the NBA? This is the question asked by Goldsberry. Conventional evaluative approaches would probe into FA (field-goal attempted) and its derived FG% (field-goal percentage). This approach fails to provide a simple answer to this question. For example, Nene Hilario and Dwight Howard led the league in FG% in the 2010-2011 season, but neither is considered to be a great shooter. NBA reporter David Aldridge suggests that Ray Allen is the best shooter because of his ability to shoot well from many different locations on the court. Goldsberry introduces two metrics to quantify player’s shooting performance and validate reporter Aldrige’s opinion.

Visualization: Shooting Spread

Spread Visualization of Ray Allen
Visual depiction of Ray Allen’s Spread Variable

The research team compiled a spatial field goal database that included Cartesian coordinates (x,y) for every field goal attempted during 2006-2011. The shooting data is mapped to a standard NBA basketball court and the map is divided into 1,284 squares for analysis of Spread, which describes the overall size of a player’s shooting territory.

Visualization: Shooting Range 

Range
Visual depiction of Ray Allen’s Range Variable

But Spread alone is not enough. It reveals shooting tendencies but not effectiveness. Range, thereof, indicates the percentage of the scoring area in which a player averages more than 1 PPA (points per attempt). Steve Nash is ranked first, with a Range value of 406, indicating that he averages over 1 PPA from 406 unique shooting cells, or 31.6% of the scoring area. Ray Allen was ranked second (30.1%). Although the hypothesis about Ray Allen being the best shooter in the league is wrong, but Goldsberry proves that Allen is still the second best shooters in terms of the Range metric.

Effectiveness

The paper presents new spatial metrics and advanced visualizations that allow better understanding of the complex spatial dynamics of NBA players and teams. CourtVision integrates database science, spatial analysis, and visualization to demonstrate players’ or teams’ spatial shooting signatures.

Connections to Lecture/Lab

  • Visual encoding process: Record, Analyze, and Communicate
  • Linear mapping of size and value for FGA (see legend on Visualization of Spread)
  • Use of color spectrum and correlated dimensions to illustrate PPM (points per attempt)

Conclusion

CourtVision is an informative and beautiful visualization of shooting analysis dedicated to NBA fans, players, coaches, analysts, and executives. The presented spatial and visual analytics could be vital new tools for informing future game plans, practice regimens, and for scouts to find potential players.

—Charles 2/24, 2013

One Reply to “CourtVision: NBA Visual and Spatial Analytics”

  1. Thanks for posting this, Charles.
    This is very timely with regards to what we’ve talked today in class. Given what you guys learned today, how effective do you rate the color scale used? Are the two encodings (color and size) integral or separable?

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