We are using the same true Bayesian estimate formula used by the Internet Movie Database, for calculating average ratings. ``` weighted rank (WR) = (v / (v+m)) * R + (m / (v+m)) * C where: R = average for the photo (mean) = (Rating) v = number of votes for the photo = (votes) m = minimum votes required to be listed in top 30 (currently 5) C = the mean vote across photo votes ``` This formula normalizes scores, that is it pulls a particular score (R) to the mean (C) if the number of votes is not well above m. In other words, if a particular photo has only a few votes above the minimum required votes to be listed in top 30 (m), the average score is decreased a little if it is above the mean, or increased a little if it is below the mean in accordance with the normal distribution rule of statistics. Here is an example calculation for one photo: ``` WR = (6 / 10) * 5.33 + (4 / 10) * 7.18 = 6.07 | | | | | | v v+m R m v+m C ``` The formula normalizes the average rating of a relatively low rated design from 5.33 to 6.07 since the number of votes (v=6) is only slightly above the minumum required votes (m=4) and the mean across the photos (C=7.18) is quite high. If, in the future, this particular photo gets more votes, the difference between R and C will increase as the number of votes increase. The idea is that the more the votes, the more representative the average rating is.