Hello,

When evaluating a set of items for their popularity, I pull popularity

metrics from two different sources, aggregating each score into an average.

While I am satisfied with this metric (Let's call it the 'popularity

score'), I'd like to create a simplified, more meaningful score.

My initial though was to create an index using the following formula:

(D3/MAX($D:$D))*100)

Where D3 is the individual popularity score and D

represents all

popularity scores.

I discovered, however, that outliers would have a disruptive effect on the

data. For instance, if there where several VERY popular items, other items

with acceptable popularity scores would appear very low in relative terms.

Any ideas on how to account for the Outliers while still retaining the

meaning in an easy to understand way?