Categories and Correlations - Part 2

...Continuing from Categories and Correlations - Part 1 (Please read that post first)

First, a necessary note on turnovers: From now on turnovers will refer to the opposite of turnovers. In the last post we saw that points and turnovers had a very strong correlation. if we use the opposite of turnovers, then points and turnovers become strongly negatively correlated. For the purposes of determining which categories to go for, strong correlations will now always be a good thing and negative correlations will now always be a bad thing.

There are 9 categories and there are 36 unique pairs of categories. We have 36 category to category correlations to analyze. The 2 scatter plots in the previous post show the category to category relationships for 2 of these 36 combinations. I won't show any more scatter plots, but here are the correlation coefficients for all 36 possible pairs of categories:

The negative relationships are in red and the positive correlations are in black. The 5 highest correlations are (These aren't the strongest correlations, just the strongest positive ones):

• Steals vs. Assists - .70
• Blocks vs. Rebounds - .67
• Steals vs. Points - .64
• Points vs. Asssits - .59
• Points vs. 3 Pointers Made - .53

You might be thinking, "Great let's use those 5 to pick which categories to go after". The above 5 strong correlations involve 6 categories (everything except FG%, FT%, and TO) and as a group of 6, there are many negative correlations between them. (Blocks vs. Assists, Rebounds vs. 3 Pointers, Etc.)

So back to the question of determining which 5 categories to go after.. We can't just look at the correlations between pairs of categories. We need to see how 5 categories can work together.

There are 126 ways to pick 5 categories from the 9. We need to pick the best group of 5 categories from the 126 different possibilities.

To be Continued...