Lawyers regularly predict court outcomes to make strategic decisions, including when, if at all, to sue or settle, what to argue, and how to reduce their clients' liability risk. Yet, lawyer predictions tend to be poorly calibrated and biased, which exacerbate unjustifiable disparities in civil case outcomes. Current machine learning (ML) approaches for predicting court outcomes are typically constrained to final dispositions or are based on features unavailable in real-time during litigation, like judicial opinions. Here, we present the first ML-based methods to support lawyer and client decision making in real-time for motion filings in civil proceedings. Using the State of Connecticut Judicial Branch administrative data and court case documents, we trained six classifiers to predict motion to strike outcomes in tort and vehicular cases between July 1, 2004 and February 18, 2019. Integrating dense word embeddings from complaint documents, which contain information specific to the claims alleged, with the Judicial Branch data improved classification accuracy across all models. Subsequent models defined using a novel attorney case-entropy feature, dense word embeddings using corpus specific TF-IDF weightings, and algorithmic classification rules yielded the best predictor, Adaboost, with a classification accuracy of 64.4%. An analysis of feature importance weights confirmed the usefulness of incorporating attorney case-entropy and natural language features from complaint documents. Since all features used in model training are available during litigation, these methods will help lawyers make better predictions than they otherwise could given disparities in lawyer and client resources. All ML models, training code, and evaluation scripts are available at https://github.com/aguiarlab/motionpredict.