Yet another post on visualising and analysing NBA shot location data using location averaging methods.
Previously I have shown averages by team for all shots taken. What about shots taken by zone? Consider the following two charts:
Now we can see a level of detail that we couldn’t in the average of all shots.
- Previously we saw that the GSW were the longest shooters in the league and indeed they were also long shooters in the paint and at midrange. It is not simply that the GSW take a lot of 3s.
- Other teams like the NYK take shots from close up in both the paint and at midrange. They were generally longer shooters in the previous analysis, suggesting that they balance those close 2-pts with many 3-pt attempts
- Previously we saw DEN as the closest shooters overall, and indeed they appear to be close shooters in the paint and moderately close shooters at midrange.
On average, players in the NBA take shots 6.5% further from the basket in the 4th period than in the 1st. This is a subtle, but consistent trend across all periods. 1.2% further in the 2nd than the 1st, 2.4% further in the 3rd than the 2nd, and 2.7% further in the 4th than the 3rd.
Most teams show the same trend, consider the graphic Average Distance of Shots by Period below:
Nearly all teams show an increasing distance by period, but there are some notable exceptions. A few teams like NYK, UTA, ATL, and ORL show an opposite trend.
It’s not clear at this point what is leading this. Greater defense in later periods forces further shots? A greater need for 3-pts pushes shots away from the basket? A greater need for points in less time forces less ideal shots from further out? What about the exceptional teams where they get closer? What are these teams doing in the 4th quarter? Leaning more heavily on particular strengths or players? Tired players not driving to the net?
What is true at the global level and largely true at the team level is again reflected in the players. Here are the average distance for the top 100 scorers:
More noise, but the trend is still visible!
When it comes to data visualisation design, it’s always important to consider your purpose and your audience. Are you trying to convince your audience of a particular point of view? Are you giving your audience an platform from which to explore and find their own insights? In my latest piece I take a step down a less discussed path.
I have created an interactive tool using D3.js that gives the user a chance to see and interact with the typical k-means clustering algorithm from data mining/machine learning. It is my hope, that it will enable students to develop an intuition for how the algorithm works, and a better appreciation of its shortcomings.
You can learn more about k-means clustering here.