A new sea state bias modeling approach is presented that makes use of altimeter-derived marine geoid estimates. This method contrasts with previous models that require differencing between repeat altimeter passes for SSB isolation, along with complex bivariate inversion, to derive a relation between wind speed, wave height and SSB. Here one directly bin-averages sea height residuals over the wind and wave correlatives. Comparison with the most current nonparametric repeat-pass model shows close agreement and provides a first validation of this simpler and more direct technique. Success is attributed mainly to extensive space and time averaging. Ease in implementation and benefits in working with absolute levels provide much appeal. Further advantages and potential limitations, centered on the need to effectively randomize large sea level anomaly components to expose the bias, are also discussed.