Hidden Gems, using players total (forever) as a popularity measure

This post contains a ranking of Steam games, based on a score intended to favor "hidden gems". A "hidden gem" is defined as a high-quality game (hence the "gem") which only got little attention (hence "hidden"). Therefore, the score of a game is defined as the product of a quality measure (its Wilson score) and a decreasing function of a popularity measure (its players total forever). The quality measure comes from SteamDB and the popularity measure comes from SteamSpy API. Finally, here is a reference to the NeoGAF post explaining the method, and the NeoGAF post explaining the idea behind the optimization of the only free parameter. The Python source code can be found on Github.

Reproducibility

To reproduce the results, use data downloaded between June 19 and June 23 (found in my initial commit to Github), with alpha ~ 10^(6.45), which arises from an optimization with 1 example of "hidden gem":

Contradiction - the all-video murder mystery adventure (appID=373390)

PC Gamer

If you arrived here from a PC Gamer article, I suggest you check the Python source code and one of these rankings:

original ranking, displayed here, featured on PC Gamer, based on data downloaded prior to the Steam summer sales,
updated ranking, using data from June 30, a week after the Steam summer sales have started.

jloiola/hidden_gems_using_players.md

Reproducibility

PC Gamer

Ranking of Steam games favoring hidden gems