This project aims to elucidate the difference in performance of NBA players in contract years vs non-contract years. Additionally, they seek to create a quantification of the "contract year effect" for each player using what they call a "slippery index." They have season statistics for 100 players over 989 total seasons at their disposal to accomplish this goal.
Things I liked
The justification for leaving out certain features on the basis of the correlation matrix and information embedded in other features was very nicely done.
The realization that you could be introducing age bias by just comparing contract vs non-contract years was smart, and I like the way you overcame the issue by only comparing non-consecutive contract year vs post-contract year.
I liked all the visualizations of the data. They did a great job of exploring the dataset and drew solid conclusions from it.
Areas for improvement
Many of the statistical differences are very small. It would be nice if you only highlighted values that were significantly different because it's hard to understand the differences the way it is presented. I see this will be in your future work which is good.
I would have liked a more in-depth explanation of what the slippery index is, and how you will attempt to construct it.
It would be nice to have a discussion on over/underfitting.
This project aims to elucidate the difference in performance of NBA players in contract years vs non-contract years. Additionally, they seek to create a quantification of the "contract year effect" for each player using what they call a "slippery index." They have season statistics for 100 players over 989 total seasons at their disposal to accomplish this goal.
Things I liked
Areas for improvement