Let the Markov chain state after each toss be , where is player 's net winnings and is the next player to toss. From it moves to with probability and to with probability . The initial state is , and a return means hitting any state with at positive time.
From , the probability of hitting zero isIndeed, in the positive-drift case the bounded harmonic solution ofuses the decaying root and boundary value , yielding ; with nonpositive drift the walk hits zero surely. By the reflected argument, from the probability isConditioning on the first toss, the required return probability isBoth formulas equal one at the boundary .
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