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.
Spaeth Precedent Analysis
Matches case arguments to the Supreme Court Database (SCDB) issue codes,
finds precedent cases where current justices voted, and analyzes voting patterns
across 75+ relevant cases per argument.
Source: Supreme Court Database (Spaeth)
Type: Historical voting patterns + brief analysis
AI: Claude Opus for argument extraction and matching
Type: Historical voting patterns + brief analysis
AI: Claude Opus for argument extraction and matching
Sentiment Analysis
Classifies the tone and content of each justice's questions during oral argument
using the Turcian & Stoicu-Tivadar (2024) fusion approach. Measures skepticism,
hostility, approval, and other sentiments directed at each side.
Source: Oral argument transcript
Type: AI-classified sentiment per utterance
AI: Claude Sonnet for semantic classification
Type: AI-classified sentiment per utterance
AI: Claude Sonnet for semantic classification
ELP Question Counts
Purely mechanical counting based on Epstein, Landes & Posner (2010):
the party asked MORE questions by the justices tends to LOSE.
Counts questions, words, and interruptions per justice per side.
Source: Oral argument transcript
Type: Computational (no AI)
Citation: J. Legal Studies, vol. 39 (June 2010)
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.
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.