Merge api/renames (#75, #78)

Two names that described the wrong thing.

`scores_with_sigma(scores, sigma)` reads as "these scores have prior
sigma 2.0". The quantity is observation noise on the score *margin*, in
the units of the scores, and it is spelled `score_sigma` at every config
site — `HistoryBuilder::score_sigma`, `GameOptions::score_sigma`,
`EventKind::Scored { score_sigma }` — so this was the one place the
crate used a third meaning of "sigma" for it. Its own doc had to
disambiguate itself: "`sigma` overrides `HistoryBuilder::score_sigma`".
`scores_with_noise(scores, score_sigma)` on both `Outcome` and
`EventBuilder`.

`predict_quality` predicts nothing. Its own doc says it answers "is this
matchup *fair*", not "what will happen", and the `predict_*` family is
otherwise exactly the methods returning a probability or a distribution
over outcomes. `History::quality` also makes the free/method pair
consistent: free `quality` pairs with `History::quality` the way free
`expected_information_gain` already pairs with
`History::expected_information_gain`. The rule that was already being
followed and never stated — a free function scores a hypothetical from
explicit parameters, the same-named method asks it against the fit — is
now written on the method.

Closes #75. Refs #78 (part 4).

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ

src/event_builder.rs

@@ -171,13 +171,21 @@ where
 
     /// Set explicit per-team continuous scores with a per-event noise override.
     ///
-    /// `sigma` overrides `HistoryBuilder::score_sigma` for this event only.
-    /// Must be `> 0.0`. Constructing the outcome with a non-positive or NaN
-    /// sigma is allowed; the value is rejected with
-    /// `InferenceError::InvalidParameter` when the event is ingested, so
-    /// callers get an error from `commit` rather than a panic.
-    pub fn scores_with_sigma<I: IntoIterator<Item = f64>>(mut self, scores: I, sigma: f64) -> Self {
-        self.event.outcome = crate::Outcome::scores_with_sigma(scores, sigma);
+    /// `score_sigma` is the observation noise on the *score margin*, not a
+    /// skill sigma, and it overrides `HistoryBuilder::score_sigma` for this
+    /// event only. A small value takes the margin near-literally; a large one
+    /// barely moves the ratings.
+    ///
+    /// Must be `> 0.0`. Building the outcome with a non-positive or NaN value
+    /// is allowed; it is rejected with `InferenceError::InvalidParameter` when
+    /// the event is ingested, so callers get an error from `commit` rather
+    /// than a panic.
+    pub fn scores_with_noise<I: IntoIterator<Item = f64>>(
+        mut self,
+        scores: I,
+        score_sigma: f64,
+    ) -> Self {
+        self.event.outcome = crate::Outcome::scores_with_noise(scores, score_sigma);
         self
     }
 

src/history.rs

@@ -1188,7 +1188,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                 // `converge` refuses to report a NaN fit, but nothing stopped a
                 // caller ignoring that error and predicting anyway. Measured on
                 // a point-mass-prior history with `beta(0.0)`, after `converge`
-                // returned `NonFiniteResult`: `predict_quality` gave `Ok(NaN)`,
+                // returned `NonFiniteResult`: `quality` gave `Ok(NaN)`,
                 // `predict_outcome().total()` gave `NaN`, and
                 // `predict_win_probabilities` gave `Ok([0.0, 0.0])` — finite,
                 // plausible, and summing to zero against a doc that promises
@@ -1226,7 +1226,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         //
         // Every prediction here is a statement about how performances *vary*,
         // and in this configuration nothing varies. The consequences were three
-        // different wrong answers rather than one error. `predict_quality`
+        // different wrong answers rather than one error. `quality`
         // **panicked** — "cannot invert a singular matrix", from a
         // `Result`-returning method, on a history that had converged cleanly —
         // because the contrast covariance `beta^2 A^T A + A^T S A` is exactly
@@ -1301,14 +1301,21 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         })
     }
 
-    /// Draw-probability quality metric for the given teams (key slices).
+    /// How fair a matchup between these teams would be, against the fit.
     ///
-    /// Values range roughly `[0, 1]`; 1 == perfectly matched. Supports any
+    /// Values range roughly `[0, 1]`; 1 is perfectly matched. Supports any
     /// number of teams.
     ///
-    /// Note this answers "is this matchup *fair*", which is not the same as
-    /// "is this matchup *informative*" — the two coincide for two evenly
-    /// matched teams and diverge elsewhere.
+    /// The method form of the free [`quality`](crate::quality), which scores a
+    /// hypothetical from explicit skill distributions instead. That is the rule
+    /// the whole family follows: a free function takes parameters, the
+    /// same-named `History` method asks the question against what was fitted.
+    ///
+    /// It was `predict_quality` until #78 pointed out that it predicts nothing
+    /// — it answers "is this matchup *fair*", not "what will happen". Fair is
+    /// also not the same as *informative*: the two coincide for two evenly
+    /// matched teams and diverge elsewhere. See
+    /// [`History::expected_information_gain`] for the other question.
     ///
     /// # Preconditions
     ///
@@ -1329,7 +1336,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// — the fit did not converge — and `InvalidParameter` if `beta` is zero
     /// and every skill is a point mass, leaving no performance distribution to
     /// predict from.
-    pub fn predict_quality<Q>(&self, teams: &[&[&Q]]) -> Result<f64, InferenceError>
+    pub fn quality<Q>(&self, teams: &[&[&Q]]) -> Result<f64, InferenceError>
     where
         K: Borrow<Q>,
         Q: Hash + Eq + ?Sized + std::fmt::Debug,
@@ -1826,7 +1833,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     /// ones that would actually be fitted if the matchup were played and
     /// recorded.
     ///
-    /// Distinct from [`History::predict_quality`], which measures *fairness*.
+    /// Distinct from [`History::quality`], which measures *fairness*.
     /// The two coincide for two evenly matched competitors and diverge
     /// elsewhere. See [`expected_information_gain`](crate::expected_information_gain)
     /// for the scale, the analytic `ln k` ceiling, and the cost.
@@ -4153,7 +4160,7 @@ mod tests {
                 crate::Team::with_members([crate::Member::new("a")]),
                 crate::Team::with_members([crate::Member::new("b")]),
             ],
-            outcome: Outcome::scores_with_sigma([3.0, 1.0], 0.5),
+            outcome: Outcome::scores_with_noise([3.0, 1.0], 0.5),
         }])
         .unwrap();
         let _ = h_a.converge().unwrap();
@@ -4195,7 +4202,7 @@ mod tests {
                 crate::Team::with_members([crate::Member::new("a")]),
                 crate::Team::with_members([crate::Member::new("b")]),
             ],
-            outcome: Outcome::scores_with_sigma([3.0, 1.0], 2.0),
+            outcome: Outcome::scores_with_noise([3.0, 1.0], 2.0),
         }])
         .unwrap();
         let _ = h_a.converge().unwrap();
@@ -4261,7 +4268,7 @@ mod tests {
         h_a.event(0_i64)
             .team(["a"])
             .team(["b"])
-            .scores_with_sigma([3.0, 1.0], 2.0)
+            .scores_with_noise([3.0, 1.0], 2.0)
             .commit()
             .unwrap();
         let _ = h_a.converge().unwrap();
@@ -4274,7 +4281,7 @@ mod tests {
                 crate::Team::with_members([crate::Member::new("a")]),
                 crate::Team::with_members([crate::Member::new("b")]),
             ],
-            outcome: Outcome::scores_with_sigma([3.0, 1.0], 2.0),
+            outcome: Outcome::scores_with_noise([3.0, 1.0], 2.0),
         }])
         .unwrap();
         let _ = h_b.converge().unwrap();

src/outcome.rs

@@ -113,11 +113,16 @@ impl Outcome {
 
     /// Explicit per-team continuous scores with a per-event noise override.
     ///
+    /// The noise is on the *observed score margin*, in the units of the scores
+    /// themselves — it is not a skill sigma, which is what the old name
+    /// `scores_with_sigma` read as. It overrides `HistoryBuilder::score_sigma`
+    /// for this event only.
+    ///
     /// `score_sigma` must be `> 0.0`. Constructing an `Outcome` with a
     /// non-positive or NaN value is allowed; the value is rejected with
     /// `InferenceError::InvalidParameter` when the event is ingested, so
     /// callers get an error rather than a panic.
-    pub fn scores_with_sigma<I: IntoIterator<Item = f64>>(scores: I, score_sigma: f64) -> Self {
+    pub fn scores_with_noise<I: IntoIterator<Item = f64>>(scores: I, score_sigma: f64) -> Self {
         Self::Scored {
             scores: scores.into_iter().collect(),
             score_sigma: Some(score_sigma),
@@ -211,7 +216,7 @@ mod tests {
 
     #[test]
     fn scores_with_sigma_round_trips() {
-        let o = Outcome::scores_with_sigma([10.0, 4.0], 0.5);
+        let o = Outcome::scores_with_noise([10.0, 4.0], 0.5);
         assert_eq!(o.team_count(), 2);
         assert_eq!(o.as_scores(), Some(&[10.0, 4.0][..]));
     }
@@ -227,7 +232,7 @@ mod tests {
 
     #[test]
     fn scores_with_sigma_sets_sigma_some() {
-        let o = Outcome::scores_with_sigma([3.0, 1.0], 2.0);
+        let o = Outcome::scores_with_noise([3.0, 1.0], 2.0);
         match o {
             Outcome::Scored { score_sigma, .. } => assert_eq!(score_sigma, Some(2.0)),
             Outcome::Ranked(_) => panic!("expected Scored variant"),
@@ -239,7 +244,7 @@ mod tests {
     /// `tests/degenerate_inputs.rs::scored_event_rejects_non_positive_sigma`.
     #[test]
     fn scores_with_sigma_defers_validation_to_ingestion() {
-        let o = Outcome::scores_with_sigma([3.0, 1.0], 0.0);
+        let o = Outcome::scores_with_noise([3.0, 1.0], 0.0);
         match o {
             Outcome::Scored { score_sigma, .. } => assert_eq!(score_sigma, Some(0.0)),
             Outcome::Ranked(_) => panic!("expected Scored variant"),

tests/api_shape.rs

@@ -203,7 +203,7 @@ fn predict_quality_two_teams() {
     h.record_winner(&"a", &"b", 1).unwrap();
     let _ = h.converge().unwrap();
 
-    let q = h.predict_quality(&[&[&"a"], &[&"b"]]).unwrap();
+    let q = h.quality(&[&[&"a"], &[&"b"]]).unwrap();
     assert!(q > 0.0 && q <= 1.0);
 }
 

tests/constructor_validation.rs

@@ -102,7 +102,7 @@ fn every_magnitude_parameter_rejects_a_negative_value() {
             }),
         ),
         (
-            "Outcome::scores_with_sigma (at ingestion)",
+            "Outcome::scores_with_noise (at ingestion)",
             Box::new(|v| {
                 let mut h = History::builder().build();
                 h.add_events(vec![trueskill_tt::Event {
@@ -111,7 +111,7 @@ fn every_magnitude_parameter_rejects_a_negative_value() {
                         trueskill_tt::Team::with_members([Member::new("a")]),
                         trueskill_tt::Team::with_members([Member::new("b")]),
                     ],
-                    outcome: Outcome::scores_with_sigma([3.0, 1.0], v),
+                    outcome: Outcome::scores_with_noise([3.0, 1.0], v),
                 }])
                 .is_err()
             }),

tests/degenerate_inputs.rs

@@ -222,7 +222,7 @@ fn scored_event_rejects_non_positive_sigma() {
         .event(1)
         .team(["a"])
         .team(["b"])
-        .scores_with_sigma([3.0, 1.0], f64::NAN)
+        .scores_with_noise([3.0, 1.0], f64::NAN)
         .commit()
         .unwrap_err();
     assert!(matches!(

tests/key_ergonomics.rs

@@ -52,7 +52,7 @@ fn every_team_shaped_query_accepts_the_same_slice() {
     let h = owned();
     let teams: &[&[&str]] = &[&["alice"], &["bob"]];
 
-    h.predict_quality(teams).expect("quality");
+    h.quality(teams).expect("quality");
     let _ = h.predict_outcome(teams).expect("outcome");
     h.predict_ranking(teams, &[0, 1]).expect("ranking");
     h.expected_information_gain(teams)

tests/prediction.rs

@@ -34,7 +34,7 @@ fn unknown_keys_are_reported_not_silently_dropped() {
         h.predict_win_probabilities(&[&[&"a"], &[&"ghost"]])
             .is_err()
     );
-    assert!(h.predict_quality(&[&[&"a"], &[&"ghost"]]).is_err());
+    assert!(h.quality(&[&[&"a"], &[&"ghost"]]).is_err());
     assert!(h.predict_ranking(&[&[&"a"], &[&"ghost"]], &[0, 1]).is_err());
 }
 
@@ -407,7 +407,7 @@ fn prior_reaches_every_prediction_entry_point() {
     let h = history_with_policy(&["a", "b"], trueskill_tt::UnknownKeys::Prior);
     let teams: &[&[&&str]] = &[&[&"a"], &[&"ghost"]];
 
-    assert!(h.predict_quality(teams).is_ok());
+    assert!(h.quality(teams).is_ok());
     assert!(h.predict_win_probabilities(teams).is_ok());
     assert!(h.predict_outcome(teams).is_ok());
     assert!(h.predict_ranking(teams, &[0, 1]).is_ok());

tests/prediction_guards.rs

@@ -78,7 +78,7 @@ macro_rules! all_predictions {
     ($h:ident, $f:expr) => {{
         let teams: &[&[&&'static str]] = &[&[&"a"], &[&"b"]];
         let f = $f;
-        f("predict_quality", $h.predict_quality(teams).map(|_| ()));
+        f("quality", $h.quality(teams).map(|_| ()));
         f(
             "predict_win_probabilities",
             $h.predict_win_probabilities(teams).map(|_| ()),
@@ -116,7 +116,7 @@ fn degenerate_performances_are_refused_rather_than_answered_wrongly() {
     assert_eq!(skill.sigma(), 0.0);
     assert!(skill.mu().is_finite());
 
-    // `predict_quality` previously PANICKED here, out of a method that returns
+    // `quality` previously PANICKED here, out of a method that returns
     // `Result`: the contrast covariance is exactly singular when beta is zero
     // and every skill is a point mass.
     all_predictions!(h, |name: &str, r: Result<(), InferenceError>| {

tests/quality.rs

@@ -110,11 +110,8 @@ fn history_predict_quality_supports_three_teams() {
     h.record_winner(&"b", &"c", 2).unwrap();
     let _ = h.converge().unwrap();
 
-    let q = h.predict_quality(&[&[&"a"], &[&"b"], &[&"c"]]).unwrap();
-    assert!(
-        q.is_finite(),
-        "3-team predict_quality must be finite, got {q}"
-    );
+    let q = h.quality(&[&[&"a"], &[&"b"], &[&"c"]]).unwrap();
+    assert!(q.is_finite(), "3-team quality must be finite, got {q}");
     assert!((0.0..=1.0).contains(&q), "out of range: {q}");
 }
 

tests/validation.rs

@@ -139,7 +139,7 @@ fn ingestion_rejects_a_tie_without_a_draw_probability() {
     );
 }
 
-/// `Outcome::scores_with_sigma` documents that a non-positive sigma is
+/// `Outcome::scores_with_noise` documents that a non-positive sigma is
 /// accepted at construction and rejected at ingestion.
 #[test]
 fn ingestion_rejects_a_non_positive_per_event_score_sigma() {
@@ -152,7 +152,7 @@ fn ingestion_rejects_a_non_positive_per_event_score_sigma() {
                     Team::with_members([Member::new("a")]),
                     Team::with_members([Member::new("b")]),
                 ],
-                outcome: Outcome::scores_with_sigma([21.0, 9.0], sigma),
+                outcome: Outcome::scores_with_noise([21.0, 9.0], sigma),
             }])
             .expect_err("a non-positive per-event sigma must be rejected");
         assert!(