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Analyzing NBA skill gaps in python (danielforsyth.me)
7 points by danielforsyth 3659 days ago | 5 comments


4 points by jcbozonier 3658 days ago | link

Whoa wait. His rationale for 5 teams being outliers seems pretty weak. If I'm reading this right the outliers make up around 17% his data points as well. Seems like a lot of data to disregard as "outliers".

This seems more like data shaping than noise reduction.

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1 point by roycoding 3658 days ago | link

Seems like a fine line. A more robust inspection of distribution of stars (and anti-stars?) on the teams would make the argument more convincing.

This definitely falls in the "fun, back-of-the envelope" category versus "serious" academic research. Both are worthwhile.

I definitely enjoyed reading this analysis. Always nice to see the code and reasoning.

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2 points by jcbozonier 3658 days ago | link

I agree! It was super easy to follow. I agree, rather than just throwing out the outliers, it'd be great to dive into why they might be different.

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1 point by nickflees 3656 days ago | link

This is cool. I wish you would have used the html for the IPython notebook instead of screenshots, but still very cool. Can you post the CSV files (or link them)?

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1 point by danielforsyth 3656 days ago | link

I'm still trying to figure out how to post HTML Ipython cells separately in the ghost blogging platform but all of the code is on my github. Thanks for reading.

https://github.com/danielforsyth

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