In equating, smoothing techniques are frequently used to diminish sampling error. There are typically two types of smoothing: presmoothing and postsmoothing. For polynomial log-linear presmoothing, an optimum smoothing degree can be determined statistically based on the Akaike information criterion or Chi-square difference criterion. For cubic-spline postsmoothing, visual inspection has been an important tool in choosing such optimum degrees in operational settings. This study introduces a new statistic for assessing the fitness of the cubic-spline postsmoothing method, which accommodates three conditions: (1) one standard error band, (2) deviation from unsmoothed equivalents, and (3) smoothness. A principal advantage of the new statistic proposed in this study is that an optimum degree of smoothing can be selected automatically by giving consistent amount of attention to deviation and smoothness across multiple equatings, whereas visual inspection may not be consistent.