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(+5)

I'm sorry, but i think this approach is a bit wrong, because it doesn't emphatize the fact that games with more votes have a more precise score. It's like saying that every game that has more than 10 ratings has a 100% precise score.
I think a Bayesian approach would have given a better result.

For example, the formula for calculating the Top Rated 250 Titles in IMDb  gives a true Bayesian estimate:

weighted rating (WR) = (v ÷ (v+m)) × R + (m ÷ (v+m)) × C 

where: R = average for the movie (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 

I believe that, with the same formula, if we substitute m with the median (in this case 10), we would get a more accurate estimate of what the game score should be.

(+1)

Here you go link

Admin
I'm sorry, but i think this approach is a bit wrong, because it doesn't emphatize the fact that games with more votes have a more precise score. 

Thanks for the feedback. The goals of IMDB ranking are a lot different than ranking jam entries. For a jam you have to be careful about giving an advantage to popular games because it makes it impossible to compete if you don't have access to an audience of people. IMDB doesn't really care about this issue, they are happy to show what is both popular and highly rated because it boosts conversion rate on their list.

That being said though, I think using Bayesian average is a good formula, and maybe will be added as an option for future game jams.

(1 edit)

Maybe you can also have a look at an algorithm like Wilson Score