In Poker, it is important to judge the likelihood of an opponents action, but for unknown opponents, we have few samples available. One method proposed in http://www.husng.com/content/interpreting-small-sample-sizes-bayesian-estimators was to use bayesian estimators, but the author used a simple heuristic for constructing the prior.

Assume we have a database with a large number of opponents and their frequencies of the actions (however, the number of samples per opponent might be very different, e.g. there might be opponents where we have just 5 hands and others we have 10000 hands). In my understanding, each opponent can be described by a binomial distribution and our prior could be determined by "combining" those into a Beta distribution. 

The questions I have are:
1. Is the thought process so far correct? Especially, does it make sense to split the observations into several opponents or can we see all different ones as a single "default" opponent and it leads to the same prior?
2. Is it a problem that we have a different sample size for each opponent? I'm thinking especially on cases where we have a tiny sample size for an opponent.
3. How can calculate the parameters of our prior beta distribution (alpha and beta) based on the data?