How We Estimate Outcome Probabilities
We do not predict outcomes. SCOTUS Analytics assigns probabilities to each justice's likely vote based on three independent analytical methods. When methods agree, confidence increases. When they disagree, we highlight the uncertainty.
Type: Historical voting patterns + brief analysis
AI: Claude Opus for argument extraction and matching
Type: AI-classified sentiment per utterance
AI: Claude Sonnet for semantic classification
Type: Computational (no AI)
Citation: J. Legal Studies, vol. 39 (June 2010)
Data Sources
| Source | Description | Size |
|---|---|---|
| Spaeth Database (SCDB) | Supreme Court case metadata, justice votes, issue codes (1946-present) | 13,930 cases |
| Spaeth Opinions Dataset | Full opinion text linked to SCDB case IDs | 2,621 opinions with text |
| Case Briefs | Petitioner and respondent briefs, replies, supplemental filings | Per case (6 PDFs for Trump v. Cook) |
| Oral Argument Transcripts | Official SCOTUS transcripts with speaker identification | Per case (447 utterances for Trump v. Cook) |
| Oral Argument Audio | Official SCOTUS audio recordings | Per case (~2 hours for Trump v. Cook) |
How Estimates Are Combined
Each methodology produces an independent probability estimate for how each justice will vote. The final integrated estimate weighs all three methods:
- When all three agree: High confidence in the estimate
- When two of three agree: Moderate confidence, with the dissenting method flagged
- When methods disagree: Lower confidence, with detailed explanation of why tone and quantity may diverge
The overall case outcome probability accounts for correlation between swing justices (Roberts, Kavanaugh, and Barrett tend to move together) and the procedural posture of the case.
Continuous Learning
The methodology is not fixed — it is a living system that is recalibrated every time the Court hands down a decision. When a case we tracked is decided, we score our published estimate against the actual result — the winning side, the vote split, and each individual justice — and we post that record openly, hits and misses alike (see the track record).
Each decision is then analyzed for which signals helped and which misled, and recurring patterns become explicit rules that change how the three methods are weighted. For example, from scored cases the model learned that a justice grilling one side hardest is often stress-testing the side it will ultimately favor — so oral-argument question counts, on their own, do not settle the winner. The controlling precedent governs — unless the legal question is genuinely open (no case squarely on point), in which case the oral-argument signal is promoted and given decisive weight. Lessons like this are recorded, versioned, and applied to every subsequent estimate.
Through the 2025 term the system has called the winning side correctly in a majority of the decided cases it tracked (the live tally is on the accuracy page). Every new decision updates both the score and the method — so the estimates you see reflect everything the model has learned to date, not a fixed formula.
Academic References
- Epstein, L., Landes, W. M., & Posner, R. A. (2010). Inferring the Winning Party in the Supreme Court from the Pattern of Questioning at Oral Argument. Journal of Legal Studies, 39(2).
- Turcian, A. & Stoicu-Tivadar, L. (2024). Multimodal sentiment analysis using audio and text fusion approach. Applied Sciences.
- Black, R. C., Treul, S. A., Johnson, T. R., & Goldman, J. (2011). Emotions, Oral Arguments, and Supreme Court Decision Making. Journal of Politics, 73(2).
- Spaeth, H. J. et al. (2025). The Supreme Court Database. Washington University Law.