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Percolation analysis has long been used to quantify the connectivity of the cosmic web. Most of the previous work is based on density fields on grids. By smoothing into fields, we lose information about galaxy properties like shape or luminosity. The lack of mathematical modeling also limits our understanding for the percolation analysis. To overcome these difficulties, we have studied percolation analysis based on discrete points. Using a friends-of-friends (FoF) algorithm, we generate the S−bb relation, between the fractional mass of the largest connected group (S) and the FoF linking length (bb). We propose a new model, the probability cloud cluster expansion theory to relate the S−bb relation with correlation functions. We show that the S−bb relation reflects a combination of all orders of correlation functions. Using N-body simulation, we find that the S−bb relation is robust against redshift distortion and incompleteness in observation. From the Bolshoi simulation, with halo abundance matching (HAM), we have generated a mock galaxy catalog. Good matching of the projected two-point correlation function with observation is confirmed. However, comparing the mock catalog with the latest galaxy catalog from Sloan Digital Sky Survey (SDSS) Data Release (DR)12, we have found significant differences in their S−bb relations. This indicates that the mock galaxy catalog cannot accurately retain higher-order correlation functions than the two-point correlation function, which reveals the limit of the HAM method. As a new measurement, the S−bb relation is applicable to a wide range of data types, fast to compute, and robust against redshift distortion and incompleteness and contains information of all orders of correlation functions.