Blogs
Clustering teams by their defense p2
This is the second part of my cluster analysis of NBA teams. In this paper I am looking for groups of teams based on their opponents percentages from certain areas on the court. I use slightly different methods than in previous analysis, not only just to try it out, but I found out that every method result in divergent clusters and in this case following algorithm suit me the best.
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Clustering teams by their defense p1
In the first part I want to simply divide NBA teams by zones from which their opponents are shooting more frequent than from the others. These are not complex clusters that would provide me a little bit of insight if there is any difference between teams. My overall assumption is that better teams would allow less shots from “danger zones” which are Restricted Area and Three Point Line.
There are of course only 30 observations, one for each team, and five variables describing frequency for each zone (Restrcited Area, Paint, Mid-Range, Long-Range, 3Point Line).
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Good Shots Index
Good Shots Index
Good Shots Index is my first approach to describe efficiency of NBA players. I get that its name is a bit lame, but its acronim is GSI which sounds way more geeky, which is good.Good shot is a shot that reasonably thinking NBA player would like to take in any play in any moment. He shoots it frequently and makes them on above league average efficiency.
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Object Oriented Programming for Machine Learning with Python
I will remember 2019 as a year in which I fully embraced Python as my go-to machine learning tool. (I am still going for R when I need to do exploratory data analysis, because tidyverse is just easier to use in that case than pandas). Scikit-learn universe with unified API’s, database connections and proper programming toolset is simply too good to not use it for complex machine learning projects.
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