Conversion of wave motion to electrical energy depends on the instantaneous sea state at a given location, which can change on second, minute and hour time-scales. In particular, information about wave groupiness (sets) is important in determining the efficiency of a wave energy converter at a given location. We report the use of a spectral method to analyze buoy time data to determine the sea state and express it as average wave height and period, as well as percentage slack time when the waves are smaller than the converter can use. The proposed method is a sinusoid estimation algorithm based on Capon’s maximum likelihood estimate, which converges to the point spectrum of the sinusoids in the wave data. Therefore one can estimate sinusoid frequencies and amplitudes from a signal corrupted by noise. Present method is compared against FFT in frequency domain. The results of these spectral methods are used to obtain statistical characteristics of waves. As a golden standard, the same wave characteristics are also determined using a peak finding algorithm. The statistical wave data is then used to predict the power output of the wave energy converter using a model derived from experimental data. The implications for ”harmonic” versus non-harmonic devices are discussed briefly.