Automotive rollover is a complex mechanical phenomenon. In order to understand the mechanism of rollover and develop any potential countermeasures for occupant protection, efficient and repeatable laboratory tests are necessary. However, these tests are not well understood and are still an active area of research interest. It is not always easy or intuitive to estimate the necessary initial and boundary conditions for such tests to assure repeatability. This task can be even more challenging when rollover is a second or third event (e.g. frontal impact followed by a rollover). In addition, often vehicle and occupant kinematics need to be estimated a-priori, first for the safe operation of the crew and equipment safety, and second for capturing and recording the event. It is important to achieve the required vehicle kinematics in an efficient manner and thus reduce repetitive tests. Mathematical modeling of the phenomenon can greatly assist in understanding such kinematics.In this study, math tools were used in three phases. The first step was to provide initial guidance to the testing through simple rigid body models. The second used the results of those tests to validate more detailed and complex models. Once validated, in the third phase, such models can be used for further parametric studies in a potential predictive manner, and restraint systems development. This paper provides the description and the methodology for nine laboratory tests for which modeling was used extensively. The type and applicability/validity of the models for various tests are described along with their level of complexity. In two specific test cases, (fall-over and multi-event bounce-over test), the model was able to provide the necessary input to the physical test setup so that the desired vehicle kinematics was achieved in the first trial.The paper discusses the potential ability to use simplified models to optimize the test conditions such that desired vehicle kinematics can be achieved with minimal trial-and-error in a full scale rollover testing.