Regression to Average for 50,50 Chances

I am currently reading a book from Daniel Kahneman and he talks about the principle of the Regression to the average.

However I fail to understand why this principle does not apply to 50 50 chances. Say we toss a coin. Tossing 10 times head has the same chance as tossing any other combination. Now the 11th throw still has equal chance for both head and tail. That is clear. But if we give each side a weight, say 1 for head and 2 for tail. Wouldn't we expect the average over all throws to be 1.5? And wouldn't we expect the the average to equalise more and more over each throw?

How does those 2 principles not contradict each other?

Thanks for your help!