Likeability score

Create an experimental engagement score for sorting your library

This optional calculated column compares kudos with hits, then adjusts the result slightly for word and chapter count.

What the score does

The template starts with the fic’s kudos-to-hits relationship, then adjusts the result slightly for its length and chapter count. It produces a number that you can display or sort in Calibre. A higher number only means “stronger by this particular formula”; it is not a rating from AO3.

Create the source columns

The template needs these four Calibre columns. You may already have Words and Chapters from Add AO3 metadata.

For each missing column:

1. Open Calibre → Preferences → Add your own columns.
2. Select the green + button.
3. Enter the heading and lookup name from the table. Calibre displays a # before custom lookup names, so kudos becomes #kudos.
4. Choose Integers as the column type, then save. Restart Calibre if asked.
5. Open FanFicFare → Configure FanFicFare → Custom Columns and map the column to the FanFicFare value shown in the table. Leave New Only unchecked so updates can refresh the number.

Create the calculated score column

1. Return to Preferences → Add your own columns and select the green + button.
2. Enter Likeability as the heading and likeability as the lookup name.
3. Choose Column built from other columns as the column type.
4. Open the Likeability template in a plain-text editor.
5. Copy everything from program: to the end into the new column’s Template box.
6. Save and restart Calibre if asked. Add the Likeability column to your library view if you want to see or sort by it.

The calculated column reads #kudos, #hits, #words, and #chapters automatically. Do not map #likeability in FanFicFare: Calibre calculates it from the other four values. It also does not need to appear on a cover unless you specifically want it there.

Limitations

This is a personal sorting aid, not an objective quality measurement. Newer fics and works in small fandoms naturally have fewer readers, and reader behavior varies by pairing, rating, date, and audience size. Missing or zero hits cannot produce a useful score. Compare results only within similar groups and adjust or ignore the formula if it is not useful to you.