Heads up this weights all your scores towards 0. If you want to avoid this, an equally simple approach is to use (x+3)/(y+5) to weight towards 3/5, or any (x+a)/(y+b) to weight towards a/b. It turns out that this seemingly simple method has some (sorta) basis in mathematical rigor: you can model x and y as successes and total attempts from a Bernoulli random variable, a and b as the parameters in a beta prior distribution, and the final score to be the mean of the updated posterior distribution: https://en.wikipedia.org/wiki/Beta_distribution#Bayesian_inf...
(I saw first this covered in Murphy's Machine Learning: A Probabilistic Perspective, which I'd recommend if you're interested in this stuff)
(I saw first this covered in Murphy's Machine Learning: A Probabilistic Perspective, which I'd recommend if you're interested in this stuff)