Feather pecking in laying hens is a major welfare and production problem for commercial egg producers, resulting in mortality, loss of production as well as welfare issues for the damaged birds. Damaging outbreaks of feather pecking are currently impossible to control, despite a number of proposed interventions. However, the ability to predict feather damage in advance would be a valuable research tool for identifying which management or environmental factors could be the most effective interventions at different ages. This paper proposes a framework for forecasting the damage caused by injurious pecking based on automated image processing and statistical analysis. By frame-by-frame analysis of video recordings of laying hen flocks, optical flow measures are calculated as indicators of the movement of the birds. From the optical flow datasets, measures of disturbance are extracted using hidden Markov models. Based on these disturbance measures and age-related variables, the levels of feather damage in flocks in future weeks is predicted. Applying the proposed method to real-world datasets, it is shown that the disturbance measures offer improved predictive values for feather damage thus enabling an identification of flocks with probable prevalence of damage and injury later in lay.