Hotshot Charts

Basketball Data Insights

Welcome to Hotshot Charts (HSC), Our website was developed for interactive insight into the great game of basketball. Pick a year, team, and player, to see how teammates pass amongst themselves, and where a specific player takes his best shots. It’s that simple.


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What browsers are supported?

This web app uses svg images which are supported in Firefox, Internet Explorer (IE 9+), Opera, Safari and Google Chrome.

Why are you doing this?

We thought it would be fun to showcase some of our abilities as Data Artist, Data Scientist, and Developer; what better way than an awesome webpage. Also the underlying framework is relevent to our clients looking to highlight insights buried in their data.

I’ve seen shot charts before, what is so innovative here?

Glad you asked. Instead of generating each of the players' data into a static graphic we are building the geospatial data and the heat map in real time. What you are seeing are dynamically generated shot charts based on granular data. This means if you pass us any type of combinations of players (teams, years, or home/away), we can generate the shot chart. This can be used for any type of geospatial data.

What does expected value mean?

Expected value is the average value of a shot from that point on the court. For instance, if a player’s expected value for a 2 point shot is 1.1, out of 100 shots, on average he would score 110 points or is a 55% shooter from that spot. For a 3 point shot, if the expected value is 1.2, on average he would score 120 points in 100 shots, or he is a 40% shooter from that spot.

What about defensive data, this seems very offensively focused?

We are working on that for our next release.

Where did you get your data?

We are using the APIs provided by SportsData.

How did you calculate expected value?

Expected value is calculated not only from the spot on the court where the shot occurred, but also from the closest eight points around that point which form a square. Since there is some uncertainty in location (and also since most of the NBA players feet are larger than 1 square foot), the expected value is smoothed across a larger area of data. This is called spatial smoothing, which allows us to remove uncertainty and better represent expected values across the court. More about smoothing can be found here.

What resources and tech did you use?

First off, this project was inspired by Kirk Goldsberry. For the back end this could have been built with any database but we chose to use .NET MVC, and SQL Server. The calculations and magic happen in an algorithm that leverages the best of SQL Server and the best of .NET. The front end development was built with a combination of Twitter Bootstrap for the skeleton and styling of the site and d3.js for making the data visualizations.

What is the circular visualization?

It is a chord diagram. In this case we've called it an assist chord since it is representing assists from teammates over a season.

For example, the Los Angeles Lakers shows that Kobe has the most assists than anyone else on his team and someone like Dwight Howard gives more assists to Kobe than Kobe gives to him. You can tell this because the width of the path coming out of Dwight is larger than the width of the connected path from Kobe.

What is the court visualization?

That is a heatmap. In this case the diagram shows a player's shots over the selected season. Each shot is represented on the relative position on the court, the colors indicate the expected value, and the size signifies the number of attempted shots.

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