Case Duration Prediction

The foundational layer of any duration model is the historical pace of the specific court. Each jurisdiction has a unique temporal fingerprint—the Eastern District of Texas is not the Southern District of New York.

• Median time-to-trial by district and division
• Judicial vacancy impact: how unfilled benches stretch all timelines
• Local rule peculiarities: mandatory ADR windows, discovery stay periods
• Statistical baseline: a Weibull distribution fitted to historical case lifecycles

A model must first establish what is normal for a venue before it can identify what is anomalous about a specific case.

Duration is not a single prediction but a survival analysis problem modeled as a sequence of conditional transitions. Each procedural milestone resets the clock and updates the remaining time estimate.

• Kaplan-Meier estimators for time-to-event at each docket stage
• Cox proportional hazards models incorporating time-varying covariates
• Markov chain modeling of procedural state transitions
• Conditional updates: a granted motion to dismiss radically truncates the predicted tail

The system outputs not one number, but a hazard function showing the probability of resolution at each future time point.

Raw docket data is transformed into predictive complexity signals that drive duration estimates. These engineered features capture the latent friction within a case.

• Party count entropy: more parties = exponential scheduling complexity
• Claim density: number of distinct causes of action per complaint
• Docket entry velocity: frequency of filings as a proxy for contentiousness
• Class action flag: binary multiplier that dramatically extends all phases
• MDL involvement: multidistrict litigation coordination adds procedural layers

Feature importance analysis consistently shows that party structure and claim type dominate over raw document volume.

The assigned judge is one of the most statistically significant predictors of case duration. Judicial behavior modeling quantifies this effect.

• Individual judicial pace: each judge's historical median time-to-disposition
• Motion grant rate correlation: judges who deny more motions accelerate cases
• Trial setting practices: variance in how aggressively judges push to trial
• Senior status effect: part-time judges often have longer timelines

A robust model encodes the judge as a high-cardinality categorical feature with empirical Bayes shrinkage to handle judges with sparse histories.

A point estimate of duration is insufficient for risk management. The system must output calibrated prediction intervals that reflect true empirical uncertainty.

• Conformal prediction for distribution-free confidence bounds
• Quantile regression to directly model the 10th, 50th, and 90th percentiles
• Calibration plots: predicted vs. actual coverage probability
• Temporal decay: confidence intervals widen as the forecast horizon extends

The output is a statement like: "We are 80% confident this matter will resolve between 14 and 22 months." This enables actuarial reserve setting and portfolio risk aggregation.

Duration prediction is fundamentally a censored data problem. Standard regression fails because many cases in the training set are still ongoing—their true duration is unknown.

• Right-censoring: cases not yet resolved at the time of data extraction
• Survival models (Cox, AFT) naturally handle censored observations
• Competing risks: settlement vs. trial vs. dismissal as distinct exit paths
• Time-dependent AUC: model discrimination measured across the timeline

Ignoring censoring introduces systematic optimism bias—the model only learns from fast-resolving cases and underestimates durations for complex matters still in progress.