The authors explain the role of goal softening in the convergence of alignment probability employed in Ordinal Optimization. Using the an order statistics formulation, they examine the exponential decrease of misalignment probability bounds when two or more designs are compared. Their conclusion states that, by relaxing the good enough subset and selected subset criteria it is exponentially efficient in terms of matching good designs in a selected group.