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My apologies if this is a basic question because I am no mathematician. Struck on my work on this, so came here to get some help.

I am working on this bayesian estimate explained here.
This is a bayesian estimator used to find out the Top 250 list of all the movies in Imdb.com with the following factors.
enter image description here

where,
W= weighted rating
R= average for the movie as a number from 0 to 10 (mean) = (Rating)
v= number of votes for the movie = (votes)
m= minimum votes required to be listed in the Top 250 (currently 25000)
C= the mean vote across the whole report (currently 7.1)

Here what I am trying to do is to find the required number of votes and rating for a normal movies to get into the Top 250 list.
This is how I do it:

  1. Find the Weighted Rating(W) of 250th movie in the list.
  2. subtract 0.0001 to the W to get newW.
  3. With the new weighted rating I have to calculate the required number of votes and the rating.

This 3rd step is where I got struck. Can you simplify me an expression to calculate
1. required number of votes
2. Average Rating


Also the new required number votes should exclude the existing votes.

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    It seems that by "WR" you don't mean W times R as you had introduced the variables; you introduced a second name "WR" for the variable you had previously named "W"? That's rather confusing.2012-08-22
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    @joriki sorry.. made the correction now.2012-08-22

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