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Answer 1

+2 A:

you would want to calculate the percentile that each user belongs to. in mysql, you can do it like this:

http://forums.mysql.com/read.php?20,105223,105278#msg-105278

rank / total

where rank is the number of users with lower karma.

jspcal 2010-01-05 06:14:46

Answer 2

+7 A:

 min_karma = min(karmas)
 max_karma = max(karmas)
 normalized = (karma - min_karma) / (max_karma - min_karma)

This has the property that the user(s) with karma = min_karma get a normalised karma of 0, and users with karma = max_karma get 1. Others are linearly distributed in between. You will have to handle separately the special case that all users have the same karma.

If you want a non-linear distribution you could use a logarithmic function:

 normalized = (log(karma) - log(min_karma)) / (log(max_karma) - log(min_karma))

It is important in this case that the karma can never fall below 1, as this could skew the results.

Mark Byers 2010-01-05 06:15:18

I'm not sure if this is the way to go. Consider a situation where 100 users have a low karma distributed around 10, and only 1 user has a high karma of 100. I would expect the normalized values to take into account the fact that only 1 user has a high karma, and therefore give users with for example 15 karma a better than linear advantage. Does that make sense?

EdanB 2010-01-05 06:31:28

Also, there is no max karma, as karma gets added for certain actions that can be done indefinately (such as getting high ratings for a review you posted)

EdanB 2010-01-05 06:35:12

You could take the logarithm of the score if you want a non-linear distribution.

Mark Byers 2010-01-05 06:35:50

@EdanB: The max_karma refers to the *current* max, which changes as people get more karma.

Mark Byers 2010-01-05 06:36:30

I've updated my post with these clarifications.

Mark Byers 2010-01-05 06:40:29

This is an awesome solution!

jspcal 2010-01-06 01:56:45

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Calculating normalized karma

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