T4 (MarginFactor): scored outcomes via Gaussian-margin EP evidence

Adds soft Gaussian-observation evidence on the per-pair diff variable,
enabling continuous score margins as a richer alternative to ranks.

Public API:
- `Outcome::Scored([scores])` (non-breaking enum extension under
  `#[non_exhaustive]`).
- `Game::scored(teams, outcome, options)` constructor parallel to
  `Game::ranked`.
- `EventBuilder::scores([...])` fluent helper.
- `HistoryBuilder::score_sigma(σ)` knob (default 1.0, validated > 0).
- `GameOptions::score_sigma`.
- `EventKind` re-exported from `lib.rs` (annotated `#[non_exhaustive]`).
- New `InferenceError::InvalidParameter { name, value }` variant.

Internals:
- `MarginFactor` (`factor/margin.rs`): Gaussian observation factor that
  closes in one EP step; cavity-cached log-evidence mirrors `TruncFactor`.
- `BuiltinFactor::Margin` dispatch arm.
- `DiffFactor` enum in `game.rs` lets `Game::likelihoods` and the new
  `likelihoods_scored` share the per-pair link abstraction.
- Per-event `EventKind { Ranked, Scored { score_sigma } }` routed through
  `TimeSlice::add_events`, `iteration_direct`, and `log_evidence`.

Tests: 88 lib + 27 integration (4 new in `tests/scored.rs`); existing
goldens byte-identical.  Bench: `benches/scored.rs` baseline ~960µs for
60 events × 20-player pool with default convergence.

Plan: docs/superpowers/plans/2026-04-27-t4-margin-factor.md
Spec item marked Done.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

README.md

@@ -71,6 +71,27 @@ let h = History::builder()
     .build();
 ```
 
+## Scored outcomes
+
+Use `Outcome::scores([...])` when you have continuous per-team scores rather
+than just ranks. Adjacent score margins flow into a `MarginFactor` that adds
+soft Gaussian evidence about the latent performance diff. Configure
+`HistoryBuilder::score_sigma(σ)` to control how much you trust the margins
+(smaller σ = more trust).
+
+```rust
+use trueskill_tt::{History, Outcome};
+
+let mut h = History::builder().score_sigma(2.0).build();
+h.event(1)
+    .team(["alice"])
+    .team(["bob"])
+    .scores([21.0, 9.0])
+    .commit()
+    .unwrap();
+h.converge().unwrap();
+```
+
 ## Todo
 
 - [x] Implement approx for Gaussian