The Grover mixer operator is a variational version of Grover's diffusion operator, introduced as a mixing operator for the Quantum Alternating Operator Ansatz (QAOA) and used in two variants known as Grover Mixer QAOA (GM-QAOA) and Grover Mixer Threshold QAOA (GM-Th-QAOA). An important property of these variants is that the expectation value is invariant over any permutation of states. As a consequence, the algorithm is independent of the structure of the problem. If, on the one hand, this characteristic raises serious doubts about the capacity of the algorithm to overcome the bound of the unstructured search problem, on the other hand, it can pave the way to its analytical study. In this sense, this work considers a statistical approach to analyze both GM-QAOA and GM-Th-QAOA that results in analytical expressions for the expectation value depending on the probability distribution associated with the problem Hamiltonian spectrum. Although in the case of GM-QAOA, the expression depends exponentially on the number of layers, the more simple case of GM-Th-QAOA results in an expression with complexity independent of that parameter, and, with it, we provide bounds for different performance metrics. Subsequently, we extend the analysis of GM-Th-QAOA to a more general context for QAOA with the Grover mixer we called Grover-based QAOA. In that framework, which allows the phase separation operator to encode any compilation of the cost function, we generalize all the bounds by using a contradiction argument with the optimality of Grover's algorithm on the unstructured search problem. As a result, we get the main contribution of this work, formalizing the notion that the Grover mixer, at most, reflects a quadratic Grover-style speed-up over classical brute force.