Confused about setting priors in BRMS (reference variables)

I am trying to build a bayesian probit regression model to predict strikeouts in Major League Baseball. I have observations for every plate appearance this year with the following columns:

batterName - name of the batter
pitcherName - name of the pitcher
venueName - name of the ballpark

I am trying to set an informative prior using preseason projections from another source for the players strikeout percentage by making a player with a preseason projection of, say, a 25% strikeout rate, have a prior of N(invnormcdf(0.25), sigma), where I fix the value of sigma. The issue that I am running into is that BRMS holds out the earliest value in the alphabet for each variable as the reference and does not let me set a prior. How do I adjust for this, because I want to set an informative prior for all of the players and venues? If BRMS makes me hold out a player P with a preseason prior of N(invnormcdf(x), sigma), should I set the prior for another arbitrary player with a preseason prior of N(invnormcdf(y), sigma) as N(invnormcdf(y) - invnormcdf(x), sigma)? When I want to update my model with new observations as the season progresses, will this sufficiently update the opinion of the reference player? I am worried if the reference player’s strikeout percentage diverges significantly from my preseason prior it will not be accounted for correctly. Thank you. Let me know if any other information is needed.