mercoledì 19 ottobre 2016

Ten Years of Nba Draft

Scatter Plots: a brief Introduction

When you have to deal with a dataset containing lots of rows and multiple metrics, Scatter Plots may be the solution to all your Viz problems.

With a Scatter Plot chart, you can plot all your rows/detail considering a minimum of 2 metrics (X and Y coordinates) till a maximum of 4.

You may say: what could the other 2 metrics affect?
I shoud say: Size of your "plotted" shape, and eventually its color.


For example you shuold plot multiple circle (one for each row of yours) with a specific size determined by a third metric, and a certain color coming from a fourth metric (eg: gradient from worst to best).
Alternately, you could even bind colors to a dimensions.


Analysis

Is it possible to evaluate how good (or bad) NBA teams have selected their picks in the last 10 years?

I tried to answer this question using 2 very well known stats: Avg. Minutes Played and Wins Share per Year of every player drafted in this span of time.


With this assumption, the more a team (first scatter plot) or a player (second scatter plot) is on the top-right corner the more they are considered in a good position with high value for Avg. minutes played and hig Wins Share per Year.
On the other end, being in the bottom-left corner means that a team has selected low profile/impact players; meanwhile for a player means that his careers is just sub-par.




You can interact with the dashboard both selecting a year to filter a specific draft class and clicking on teams logo to consider just those players drafted by that team.









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