Stability of bipedal systems in frontal plane is affected by the hip offset, to the extent that adjusting stride time using feedforward retraction and extension of the legs can lead to stable oscillations without feedback control. This feedforward stabilization can be leveraged to reduce the control effort and energy expenditure and increase the locomotion robustness. However, there is limited understanding of how key parameters, such as mass, stiffness, leg length, and hip width, affect stability and the minimum stride frequency needed to maintain it. This study aims to address these gaps through analyzing how individual model parameters and the system's natural frequency influence the minimum stride frequency required to maintain a stable cycle. We propose a method to predict the minimum stride frequency, and compare the predicted stride frequencies with actual values for randomly generated models. The findings of this work provide a better understanding of the frontal plane stability mechanisms and how feedforward stabilization can be leveraged to reduce the control effort.