Efficiency estimation of a wave energy converter for different wave climates and design parameters prior to its deployment is at the heart of the wave energy researches and of paramount importance especially for investors, shareholders and decision makers. There is no thorough data-driven investigation exists in the literature for developing an analytical formula for estimation of an oscillating water column (OWC) efficiency. Therefore, present study is an attempt to fill this gap via constructing different predictive analytical models namely, multilinear regression, polynomial regression and novel Geno-Nonlinear. Required data for training and validation phases of the models are generated through an extensive experimental campaign. Performances of the models are evaluated via root mean square error and constant of efficiency diagnostic measures. All models estimate the efficiency of an OWC from three dimensionless variables representing the power take-off damping, frontwall draft and incident waves. The introduced novel Geno-Nonlinear formula is optimized via Genetic Algorithms and found to be the best model with its remarkably accurate estimation results. The structure of the novel Geno-Nonlinear expression physically suits in describing the phenomenon and offers scientists, engineers and researches a simple, practical yet an accurate convenient tool for reliable efficiency estimation of an OWC.(c) 2021 Elsevier Ltd. All rights reserved.