A more granular view of star-rankings

One of the main problems with the star-categorization is that, like most ordinal data, it doesn’t provide an idea as to the degree of separation between intra-star gradings. Generally speaking, we can use the individual ratings to find that, which usually works well. But that still doesn’t let us know exactly where within a particular star category a player currently sits. Taking the range of ratings (composite) for each star category, I added a normalized score at each possible level (at 4 digits). Applying that to Florida’s current commits and leans (per crystal ball) to get an idea of where they stand provides some insight:

Tyler Booker is practically a 5-star (4.98) and Julian Humphrey is a very high 4-star. This is pretty straightforward. Booker is in the 98th percentile of the 4-star range, so he is a 4.98 star.

Class-wise, Florida has some ground to make up, as they are currently 15th using this metric (and controlling for number of commits/leans). Fortunately they have time:

One thought on “A more granular view of star-rankings

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s