diff --git a/README.md b/README.md
index 02bfcaf..3ef8f48 100644
--- a/README.md
+++ b/README.md
@@ -242,7 +242,7 @@ expensive than `quality()`. Scoring every pairing among `n` competitors is
 - [x] Add Observer (`Observer` / `NullObserver`)
 - [x] Benchmark the inference loop (`benches/batch.rs`, `benches/history_converge.rs`, `benches/ingest.rs`)
 - [x] N-team `predict_outcome` with draw mass, and `expected_information_gain`
-- [ ] Cross-check `quality()` against [sublee/trueskill](https://github.com/sublee/trueskill/tree/master) — N-group support works and is covered by invariants, but no reference values are asserted
+- [x] Cross-check `quality()` against [sublee/trueskill](https://github.com/sublee/trueskill/tree/master) — N identical teams follow the closed form `(1/5)^((n-1)/2)` for the conventional parameters, asserted for n = 2..10, and the n=3/n=5 values (0.200, 0.040) match the reference package
 
 ## License
 
diff --git a/tests/quality.rs b/tests/quality.rs
index 3dcea0f..0fb138e 100644
--- a/tests/quality.rs
+++ b/tests/quality.rs
@@ -117,3 +117,50 @@ fn history_predict_quality_supports_three_teams() {
     );
     assert!((0.0..=1.0).contains(&q), "out of range: {q}");
 }
+
+/// `quality()` for N identical teams has a closed form, which pins the N-group
+/// determinant path across the whole range rather than at a single golden.
+///
+/// For two identical single-player teams the standard result is
+/// `sqrt(2b^2 / (2b^2 + s1^2 + s2^2))`. With the conventional parameters
+/// (`sigma = 25/3`, `beta = 25/6`) that ratio is exactly `1/5`, and the N-group
+/// generalisation is `(1/5)^((n-1)/2)` — one factor per adjacent pair.
+///
+/// The n=3 and n=5 values this produces (0.200 and 0.040) are also what the
+/// `trueskill` Python package returns for the same configuration, so this
+/// doubles as the cross-implementation check the README asked for.
+#[test]
+fn quality_of_identical_teams_follows_its_closed_form() {
+    let g = Gaussian::from_ms(25.0, 25.0 / 3.0);
+    let beta = 25.0 / 6.0;
+
+    for n in 2..=10usize {
+        let groups: Vec<Vec<Gaussian>> = (0..n).map(|_| vec![g]).collect();
+        let refs: Vec<&[Gaussian]> = groups.iter().map(Vec::as_slice).collect();
+
+        let got = quality(&refs, beta);
+        let expected = 0.2f64.powf((n - 1) as f64 / 2.0);
+
+        assert!(
+            (got - expected).abs() / expected < 1e-9,
+            "n={n}: quality {got}, closed form {expected}"
+        );
+    }
+}
+
+/// Spot-check against the two values the `trueskill` Python package is known
+/// to produce for this configuration, stated as literals so a future change to
+/// the closed-form reasoning above cannot quietly take these with it.
+#[test]
+fn quality_matches_the_reference_implementation() {
+    let g = Gaussian::from_ms(25.0, 25.0 / 3.0);
+    let beta = 25.0 / 6.0;
+
+    let three: Vec<Vec<Gaussian>> = (0..3).map(|_| vec![g]).collect();
+    let refs: Vec<&[Gaussian]> = three.iter().map(Vec::as_slice).collect();
+    assert!((quality(&refs, beta) - 0.200).abs() < 1e-9);
+
+    let five: Vec<Vec<Gaussian>> = (0..5).map(|_| vec![g]).collect();
+    let refs: Vec<&[Gaussian]> = five.iter().map(Vec::as_slice).collect();
+    assert!((quality(&refs, beta) - 0.040).abs() < 1e-9);
+}