I looked at three categories: college three point percentage, college free throw percentage, and NBA three point percentage. For each of the eligible players, I calculated what percentile they fell into when stacked up against the other 42. This is because because simply using free throw percentage and three point percentage would have produced confounding results; there is a much greater variance in free throw percentage than in three point percentage, therefore a comparative ranking would work better.

The spreadsheet found four percentiles: percentile that the player fell into for three point percentage in college, percentile the player fell into for free throw percentage in college, percentile the player fell into for three point percentage in the NBA, and, finally, the average of the college free throw percentile and the college three point percentile.

There are more players in the spreadsheet than this. This was just to make the numbers readable.

I tested each of the college percentiles against NBA three point percentiles to see which was the best indicator of future success. These were the graphs:

Using a trendline for the most accurate data set

Finally, I calculated r for each of the graphs. (r finds how correlated a data set is. 1 is 100% positive correlation, 0.7 is strongly positively correlated, with anything above 0.8 being considered very strongly positively correlated. 0 is no correlation.)

College 3pt vs NBA 3pt percentile: r=.6756

College ft vs NBA 3pt percentile: r=.7945

Average of college 3pt and NBA 3pt: r=.7961