Historical Analysis and Judicial Decision Prediction

Judicial prediction has progressed from feature-based court-outcome studies to neural and benchmark-driven legal language understanding. Yet many published evaluations still mix outcome identification, retrospective categorization, and prospective forecasting. This paper presents a chronology-scoped framework for historical judicial analysis that separates those settings and treats a prediction as valid only when all features, legal texts, judge histories, and retrieved precedents were available before the target decision. The framework, called Historical Judicial Outcome Modeling (HJOM), combines temporal feature inventory, citation-grounded phrase profiles, long-document encoders, legal-domain pretraining, and calibrated abstention. A controlled analytical study over appellate-style tasks compares lexical baselines, legal transformer encoders, historical profile models, and an HJOM ensemble under random and chronological splits. The results show that chronological evaluation lowers apparent performance but improves interpretability of model failures. The paper argues that useful judicial prediction requires temporal provenance, jurisdiction-aware task definitions, and explanation records that distinguish legal facts, panel history, and data artifacts.