We present a Bayesian Power Ranking model to analyze Mexico’s electoral preferences. This model ranks potential presidential candidates for the 2024 elections using a wide array of surveys, each including a different subset of the roster of plausible candidates. The model employs a multinomial approach for observations, corrects for polling house biases, and uses a correlated random walk to dynamically update preferences. We defined the Potential Index, an index of relative preference probabilities, and established the Power Ranking, a comprehensive preference order over time. Throughout 2023, both the Potential Index and the Power Ranking provided insights into public opinion and media influence before the announcement of the official candidate list.