1. The output of every model is not just a point estimate (single value), it's actually a probability distribution. So you can know not just the best guess at a player's value, but all the possible "true" values. All bayesian models also require prior distributions for each coefficient as well, which I mention in the article
2. The computation itself has to be done through some bayesian computation method. That's a bit more of a complicated subject, but if you do some googling you can learn more about that.
What makes this model Bayesian?
This means two things:
1. The output of every model is not just a point estimate (single value), it's actually a probability distribution. So you can know not just the best guess at a player's value, but all the possible "true" values. All bayesian models also require prior distributions for each coefficient as well, which I mention in the article
2. The computation itself has to be done through some bayesian computation method. That's a bit more of a complicated subject, but if you do some googling you can learn more about that.