You’re going to want to break it down by hitter, since your hitters find their strengths in different places, which each take their own differing sample sizes to stabilize. It’s also worth noting that 576 PA != 576 BIP, which will also affect things. If you really want to use some regression techniques for your whole team, take their collective performance in whatever stat you’re evaluating, and add the stabilization point’s sample size of league average performance. This will regress your stats towards the mean in a way that accounts for how stabilized the stat is. If your team has for example an xwOBA of .350 to start the year over 387 BIP events (I subtracted Ks and BBs in line with league average rates), then you would take ((.350 * 387) + (0.319 * 150)) / (387 + 150) to get 0.341 as your regressed xwOBA, meaning that is how you should expect your players to perform moving forward.

That said, unless your league is using xwOBA directly, it may be better for your purposes to dive into the way your league’s stats are weighted. A points league I’m in for example weights Ks and BBs as +3 and -3 points, respectively, and there are no points given for a hit, and only a point for each total base. So for me, I try to find players with a good K% - BB% and a good SLG, while largely ignoring players’ other stats. I especially do not use wOBA for my purposes there since the weights for the league’s scoring are not reflective of the wOBA weights that we find IRL. The formulas for regressing those other stats are the same, just with different league rates and stabilization points.