Effective monitoring of bulbar disease progression in persons with amyotrophic lateral sclerosis (ALS) requires rapid, objective, automatic assessment of speech loss. The purpose of this work was to identify acoustic features that aid in predicting intelligibility loss and speaking rate decline in individuals with ALS. Features were derived from statistics of the first (F1) and second (F2) formant frequency trajectories and their first and second derivatives. Motivated by a possible link between components of formant dynamics and specific articulator movements, these features were also computed for low-pass and high-pass filtered formant trajectories. When compared to clinician-rated intelligibility and speaking rate assessments, F2 features, particularly mean F2 speed and a novel feature, mean F2 acceleration, were most strongly correlated with intelligibility and speaking rate, respectively (Spearman correlations > 0.70, p < 0.0001). These features also yielded the best predictions in regression experiments (r > 0.60, p < 0.0001). Comparable results were achieved using low-pass filtered F2 trajectory features, with higher correlations and lower prediction errors achieved for speaking rate over intelligibility. These findings suggest information can be exploited in specific frequency components of formant trajectories, with implications for automatic monitoring of ALS.