We investigate Monte-carlo sampling in games with imperfect information. We show that for very simple game trees the chance of nding the optimal strategy with Monte-carlo sampling rapidly approaches zero as the number of moves in the game increases. We explain this sub-optimality by identifying the diierent kinds of errors that can arise, and by analysing their interplay. We also relate our tes...