One data point is the netflix rating set from their contest. The system is 1 to 5 stars. The average movie rating was 3.4 in 1998 climbing to 3.8 in 2005.
Back to the original point -- if you used a formula with a default rating of 3 it would underestimate low-frequency movies. People don't view movies at random. They try to pick things they will like, which should be reflected in the statistical model.
It's very nice to see a mathematically motivated piece on how to sort based on users ratings. But there are two problems with the solution presented, the first minor, and the second more serious.
Minor: Distributions of ratings are not usually normal about the average possible rating -- they are usually skewed to the positive end, because people are pretty good at selecting what they read or view. The solution presented will tend to skew low-frequency ratings too far toward the average. It should be easy to calculate an expected average for your site, and use this instead of 1/2 for the formula given in the post.
Major: What you really need is a missing data point: the number of readers who haven't made any rating at all. It's pretty clear if you think of two examples:
Clearly, readers feel pretty blah about Item 1. You're much better off displaying Item 2, other things being equal, but the formula doesn't distinguish the two cases.
Back to the original point -- if you used a formula with a default rating of 3 it would underestimate low-frequency movies. People don't view movies at random. They try to pick things they will like, which should be reflected in the statistical model.