Herding is a feature of investor behavior in financial markets, particularly in market stress. We apply an approach based on the cross-sectional dispersion of individual stocks' betas, which allows us to extract herding patterns, using two dynamic methodologies to measure the herding phenomenon over time with a state-space model for the Cryptocurrency Market. The results reveal that herding toward the market shows significant movement, and persistence regardless of the market condition, expressed through the market index, market volatility, and the volatility index. When analyzing path herding is possible to observe that herding was intense during the investigated period. We also identify a positive relationship between herding and market stress.