The prior predictive distribution is useful as a tool for exploring the behavior of your model conditional on the priors you have specified. If you have a lot of data, prior specification can matter less, but in cases where data are sparse estimates will be shrunk toward the prior, so its nice to know what that implies for your model.
For example, in sports analytics it can be useful to use your priors to represent the population distribution of the parameter (since that encompasses the target of your inference and prediction most of the time, and we have really good population-wide data). So, sampling from the prior predictive should return quantities that look like your overall population, and from which parameters corresponding to individuals within your analysis might be drawn.
For your particular problem, choosing between two quite uninformative priors–Beta(1,1) vs Beta(0.5, 0.5)–with a sample size of 73 will make little difference, so you don’t have much to gain from running a prior predictive check.
In general, its a cheap and easy way to check on the behavior of your model before it sees any data, and can sometimes catch some misspecification. I don’t do it all the time, but when I don’t I often wish I had!