Let us enter the SuperGeek Zone. Everybody knows Huber happiness, right? It’s a metric of how much happiness you get out of a game. It’s calculated as (game rating — baseline) * average length in minutes * number of games played. Ratings and baseline are on scale from 1 to 10, baseline is typically 4.5. So, a game you play a lot and rate high scores high on Huber happiness.
That’s basic. Matthew Gray has, however, come up with another, more intricate formula. Hot games metric takes Huber happiness and factors in novelty and replayability. Here’s the formula: X = S * S * sqrt(P) * H. S is 1 + (P/T), P is games played during a certain period, T is total of games played (for the game under inspection) and H is Huber happiness.
The S factor ranges from 1 to 2. It approaches 1 when the P/T ratio goes down, that is the game hasn’t been played much recently. If the game is brand new, so that all the games played have happened within the current period (whatever that is), it’s 2. So a new game will get a boost here.
It’s all very interesting, and the results look good, too. For example, this year my two hottest games, by far, have been Mhing and Industrial Waste. Now that I have implemented this in my game stat tool, choosing the best games for each year should be very simple; this metric works perfectly for something like that.