👉 Rankings math is a statistical framework used to quantify and predict the relative performance of entities, such as websites, products, or individuals, based on historical data and probabilistic models. It typically involves constructing a probability distribution over possible rankings, often using techniques like Bayesian inference or machine learning algorithms. The core idea is to estimate the likelihood of an entity achieving a certain position in a ranking, considering factors like user behavior, content quality, and other relevant metrics. This is achieved by defining a ranking function that maps entities to their expected ranks, and then using methods like maximum likelihood estimation or Markov Chain Monte Carlo (MCMC) to refine these estimates. The result is a ranked list that not only reflects past performance but also predicts future rankings, allowing for dynamic adjustments and personalized recommendations.
