chore: Release trueskill-tt version 0.3.0

Drift was a property of the History, so every competitor drifted at the
same rate and a fixed reference point could not share a graph with moving
competitors. A bot at a known strength, a rating floor, a course
difficulty — all of them drifted along with the players.

Member::with_drift_scale(s) multiplies the drift *variance* a competitor
accumulates, so s is in the same units as gamma: ConstantDrift(g) at
scale s behaves exactly as ConstantDrift(g * s) would for that competitor.
A scalar rather than a per-competitor Drift keeps History's single D type
parameter untouched and stays Copy. 0.0 pins a competitor still.

The scale lives on Rating, beside the drift it scales, and is applied
only through Rating::drift_variance_delta / drift_variance_for_elapsed.
Making those the sole entry points means a caller cannot reach the raw
drift and silently skip a competitor's scale — the filtered pass was
exactly that bug during development, caught because its test was written
before the wiring.

Like with_prior, the scale is competitor configuration captured at first
appearance rather than a per-event override; a competitor that is static
is static, and a scale that changed between events would make the skill
trajectory hard to interpret. Member's docs claimed prior was a per-event
override, which the code has never done — corrected here.

A negative scale is rejected rather than squared into its absolute value,
and a non-finite one rejected outright, both as InvalidParameter.

None means 1.0, so no existing call site changes and no existing fit
moves. Adding a public field to Member does break struct-literal
construction downstream.

Closes #34

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_014b6wy2q8rnFK8U8GPJVQNU

.gitignore

@@ -7,3 +7,4 @@
 NOTEPAD.md
 
 /.claude
+proptest-regressions/

CHANGELOG.md

@@ -2,6 +2,39 @@
 
 All notable changes to this project will be documented in this file.
 
+## 0.3.0 - 2026-09-01
+
+### Breaking Changes
+
+- refactor!: make Competitor::message an Option, and compute_elapsed loud
+- refactor!: replace emptiness-as-sentinel with Option for results and weights
+- refactor!: remove ConvergenceReport::slices_skipped
+
+### Bug Fixes
+
+- fix: enforce EventBuilder weight/team length in release
+
+### Documentation
+
+- docs: complete the public API documentation contract
+
+### Features
+
+- feat: allow drift to vary per competitor via Member::with_drift_scale
+
+### Miscellaneous Tasks
+
+- chore: ignore proptest regression seed files
+
+### Performance
+
+- perf: stop cloning inference inputs in OwnedGame and ingestion
+- perf: make the per-slice SkillStore compact instead of dense
+
+### Testing
+
+- test: add property-based tests, a shared finiteness helper, and boundary inputs
+
 ## 0.2.0 - 2026-08-27
 
 ### Breaking Changes
@@ -36,6 +69,7 @@ All notable changes to this project will be documented in this file.
 - chore: target releases at the private kellnr registry
 - chore: keep the 48 MB ATP dataset out of the published crate
 - chore: dual-license MIT OR Apache-2.0
+- chore: Release trueskill-tt version 0.2.0
 
 ### Performance
 

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "trueskill-tt"
-version = "0.2.0"
+version = "0.3.0"
 edition = "2024"
 rust-version = "1.85"
 description = "TrueSkill Through Time: Bayesian skill rating that tracks how skill evolves over time, via Gaussian message passing"
@@ -62,9 +62,14 @@ rayon = ["dep:rayon"]
 criterion = "0.5"
 plotters = { version = "0.3", default-features = false, features = ["svg_backend", "all_elements", "all_series"] }
 plotters-backend = "0.3"
+proptest = "1.11.0"
 time = { version = "0.3", features = ["parsing"] }
 trueskill-tt = { path = ".", features = ["approx"] }
 
+# Debug symbols in release are for `just flame` (cargo-flamegraph), which needs
+# them to symbolicate. Profile settings in a library are ignored by downstream
+# consumers, so these only affect local builds — this is deliberate, not an
+# oversight.
 [profile.release]
 debug = true
 

README.md

@@ -71,6 +71,36 @@ let h = History::builder()
     .build();
 ```
 
+### Per-competitor drift
+
+A `History` has one drift model, but individual competitors can scale it.
+`Member::with_drift_scale(s)` multiplies the drift *variance* that competitor
+accumulates, so `s` is in the same units as `gamma`: `ConstantDrift(g)` at
+scale `s` behaves exactly as `ConstantDrift(g * s)` would, for that competitor
+alone.
+
+`0.0` pins a competitor still. That is what makes a **fixed reference point**
+expressible in the same graph as moving competitors — a bot at a known
+strength, a rating floor, a course difficulty:
+
+```rust
+let events = vec![Event {
+    time: 0,
+    teams: smallvec![
+        Team::with_members([Member::new("player")]),
+        // A course does not improve. Pin it, and the round's evidence
+        // lands on the player instead of being split between the two.
+        Team::with_members([Member::new("layout_7").with_drift_scale(0.0)]),
+    ],
+    outcome: Outcome::winner(0, 2),
+}];
+```
+
+Like `with_prior`, the scale is **competitor configuration captured at first
+appearance** — setting it on a key the history already knows has no effect. It
+must be finite and non-negative; ingestion otherwise fails with
+`InferenceError::InvalidParameter`.
+
 ## Scored outcomes
 
 Use `Outcome::scores([...])` when you have continuous per-team scores rather

benches/batch.rs

@@ -36,7 +36,7 @@ fn criterion_benchmark(criterion: &mut Criterion) {
     let kinds = vec![EventKind::Ranked; composition.len()];
 
     let mut time_slice = TimeSlice::new(1, P_DRAW, ConvergenceOptions::default());
-    time_slice.add_events(composition, results, weights, kinds, &agents);
+    time_slice.add_events(composition, Some(results), Some(weights), kinds, &agents);
 
     criterion.bench_function("Batch::iteration", |b| {
         b.iter(|| time_slice.iteration(0, &agents))

src/competitor.rs

@@ -1,5 +1,4 @@
 use crate::{
-    N_INF,
     drift::{ConstantDrift, Drift},
     gaussian::Gaussian,
     rating::Rating,
@@ -13,7 +12,14 @@ use crate::{
 #[derive(Debug)]
 pub struct Competitor<T: Time = i64, D: Drift<T> = ConstantDrift> {
     pub rating: Rating<T, D>,
-    pub message: Gaussian,
+    /// The forward message carried from this competitor's last appearance, or
+    /// `None` before they have appeared anywhere.
+    ///
+    /// Previously an improper `N_INF` served as the unset sentinel, which made
+    /// "no message yet" indistinguishable from "a legitimately improper
+    /// message" at the type level and required every reader to know the
+    /// convention.
+    pub message: Option<Gaussian>,
     pub last_time: Option<T>,
 }
 
@@ -21,14 +27,16 @@ impl<T: Time, D: Drift<T>> Competitor<T, D> {
     /// Compute the message received at time `now`, with drift accumulated
     /// from `self.last_time` (if any) to `now`.
     pub(crate) fn receive(&self, now: &T) -> Gaussian {
-        if self.message != N_INF {
+        match self.message {
+            Some(message) => {
                 let elapsed_variance = match &self.last_time {
-                Some(last) => self.rating.drift.variance_delta(last, now),
+                    Some(last) => self.rating.drift_variance_delta(last, now),
                     None => 0.0,
                 };
-            self.message.forget(elapsed_variance)
+
-        } else {
+                message.forget(elapsed_variance)
-            self.rating.prior
+            }
+            None => self.rating.prior,
         }
     }
 
@@ -37,11 +45,9 @@ impl<T: Time, D: Drift<T>> Competitor<T, D> {
     /// Used in convergence sweeps where the elapsed was cached at slice-construction time
     /// and should not be recomputed from `last_time` (which may have shifted).
     pub(crate) fn receive_for_elapsed(&self, elapsed: i64) -> Gaussian {
-        if self.message != N_INF {
+        match self.message {
-            self.message
+            Some(message) => message.forget(self.rating.drift_variance_for_elapsed(elapsed)),
-                .forget(self.rating.drift.variance_for_elapsed(elapsed))
+            None => self.rating.prior,
-        } else {
-            self.rating.prior
         }
     }
 }
@@ -50,7 +56,7 @@ impl Default for Competitor<i64, ConstantDrift> {
     fn default() -> Self {
         Self {
             rating: Rating::default(),
-            message: N_INF,
+            message: None,
             last_time: None,
         }
     }
@@ -63,7 +69,7 @@ where
     C: Iterator<Item = &'a mut Competitor<T, D>>,
 {
     for c in competitors {
-        c.message = N_INF;
+        c.message = None;
         if last_time {
             c.last_time = None;
         }

src/convergence.rs

@@ -38,7 +38,6 @@ pub struct ConvergenceReport {
     pub log_evidence: f64,
     pub converged: bool,
     pub per_iteration_time: SmallVec<[Duration; 32]>,
-    pub slices_skipped: usize,
 }
 
 #[cfg(test)]

src/event.rs

@@ -23,6 +23,7 @@ pub struct Team<K> {
 }
 
 impl<K> Team<K> {
+    #[must_use]
     pub fn new() -> Self {
         Self {
             members: SmallVec::new(),
@@ -44,13 +45,20 @@ impl<K> Default for Team<K> {
 
 /// One member of a team, identified by user key `K`.
 ///
-/// `weight` defaults to 1.0; a per-event `prior` can override the competitor's
+/// `weight` applies per event and defaults to 1.0.
-/// current skill estimate for this event only.
+///
+/// `prior` and `drift_scale` are **competitor configuration**, not per-event
+/// values: both are captured when the competitor is first created and ignored
+/// on every later appearance. Setting either on a key the history already knows
+/// has no effect.
 #[derive(Clone, Debug)]
 pub struct Member<K> {
     pub key: K,
     pub weight: f64,
     pub prior: Option<Gaussian>,
+    /// Multiplier on the drift *variance* this competitor accumulates.
+    /// `None` means 1.0.
+    pub drift_scale: Option<f64>,
 }
 
 impl<K> Member<K> {
@@ -59,6 +67,7 @@ impl<K> Member<K> {
             key,
             weight: 1.0,
             prior: None,
+            drift_scale: None,
         }
     }
 
@@ -67,10 +76,31 @@ impl<K> Member<K> {
         self
     }
 
+    /// Set this competitor's starting skill estimate.
+    ///
+    /// Captured at the competitor's first appearance; see the type docs.
     pub fn with_prior(mut self, prior: Gaussian) -> Self {
         self.prior = Some(prior);
         self
     }
+
+    /// Scale how fast this competitor drifts, relative to the history's drift.
+    ///
+    /// The scale multiplies the drift *variance*, so it is in the same units as
+    /// `gamma`: `ConstantDrift(g)` at `scale = s` behaves exactly as
+    /// `ConstantDrift(g * s)` would for this competitor alone.
+    ///
+    /// `0.0` pins the competitor still — useful for a reference point that
+    /// shares a scale with moving competitors but should not itself move: a bot
+    /// at a known strength, a rating floor, a course difficulty.
+    ///
+    /// Captured at the competitor's first appearance; see the type docs.
+    /// Must be finite and non-negative, or ingestion fails with
+    /// [`InferenceError::InvalidParameter`](crate::InferenceError::InvalidParameter).
+    pub fn with_drift_scale(mut self, scale: f64) -> Self {
+        self.drift_scale = Some(scale);
+        self
+    }
 }
 
 /// Convenience: a member is a user key with default weight 1.0 and no prior.
@@ -91,15 +121,18 @@ mod tests {
         assert_eq!(m.key, "alice");
         assert_eq!(m.weight, 1.0);
         assert!(m.prior.is_none());
+        assert!(m.drift_scale.is_none());
     }
 
     #[test]
     fn member_builder_methods_chain() {
         let m = Member::new("alice")
             .with_weight(0.5)
-            .with_prior(Gaussian::from_ms(20.0, 5.0));
+            .with_prior(Gaussian::from_ms(20.0, 5.0))
+            .with_drift_scale(0.0);
         assert_eq!(m.weight, 0.5);
         assert!(m.prior.is_some());
+        assert_eq!(m.drift_scale, Some(0.0));
     }
 
     #[test]

src/event_builder.rs

@@ -19,6 +19,14 @@ where
     history: &'h mut History<T, D, O, K>,
     event: Event<T, K>,
     current_team_idx: Option<usize>,
+    /// First validation failure seen while building, surfaced by `commit`.
+    ///
+    /// The setters return `Self` so the chain stays fluent; they cannot return
+    /// a `Result` without breaking that. Recording the failure and reporting it
+    /// at `commit` keeps the check enforced in release, where the previous
+    /// `debug_assert!` was compiled out and a mismatched event was ingested
+    /// silently.
+    error: Option<InferenceError>,
 }
 
 impl<'h, T, D, O, K> EventBuilder<'h, T, D, O, K>
@@ -37,6 +45,7 @@ where
                 outcome: Outcome::Ranked(SmallVec::new()),
             },
             current_team_idx: None,
+            error: None,
         }
     }
 
@@ -50,22 +59,36 @@ where
 
     /// Set per-member weights for the most recently added team.
     ///
-    /// Panics in debug builds if called before `.team(...)` or if the length
+    /// A length mismatch is recorded and returned by [`EventBuilder::commit`]
-    /// doesn't match the team's member count.
+    /// as `InferenceError::MismatchedShape`, in both debug and release. The
+    /// weights are not applied in that case, so a partially-weighted team
+    /// cannot reach the history.
+    ///
+    /// # Panics
+    ///
+    /// Panics if called before any `.team(...)`.
     pub fn weights<I: IntoIterator<Item = f64>>(mut self, weights: I) -> Self {
         let idx = self
             .current_team_idx
             .expect(".weights(...) called before any .team(...)");
+
         let ws: Vec<f64> = weights.into_iter().collect();
         let team = &mut self.event.teams[idx];
-        debug_assert_eq!(
+
-            ws.len(),
+        if ws.len() != team.members.len() {
-            team.members.len(),
+            self.error.get_or_insert(InferenceError::MismatchedShape {
-            "weights length must match team size"
+                kind: "weights",
-        );
+                expected: team.members.len(),
+                got: ws.len(),
+            });
+
+            return self;
+        }
+
         for (m, w) in team.members.iter_mut().zip(ws) {
             m.weight = w;
         }
+
         self
     }
 
@@ -103,7 +126,17 @@ where
     }
 
     /// Commit the event to the history.
+    ///
+    /// # Errors
+    ///
+    /// Returns the first validation failure recorded while building — see
+    /// [`EventBuilder::weights`] — otherwise forwards to
+    /// [`History::add_events`] and returns its errors.
     pub fn commit(self) -> Result<(), InferenceError> {
+        if let Some(error) = self.error {
+            return Err(error);
+        }
+
         self.history.add_events(std::iter::once(self.event))
     }
 }

src/factor/margin.rs

@@ -20,6 +20,7 @@ pub struct MarginFactor {
 }
 
 impl MarginFactor {
+    #[must_use]
     pub fn new(diff: VarId, m_obs: f64, sigma: f64) -> Self {
         debug_assert!(sigma > 0.0, "score sigma must be positive");
         Self {

src/factor/mod.rs

@@ -20,6 +20,7 @@ pub struct VarStore {
 }
 
 impl VarStore {
+    #[must_use]
     pub fn new() -> Self {
         Self::default()
     }
@@ -28,10 +29,12 @@ impl VarStore {
         self.marginals.clear();
     }
 
+    #[must_use]
     pub fn len(&self) -> usize {
         self.marginals.len()
     }
 
+    #[must_use]
     pub fn is_empty(&self) -> bool {
         self.marginals.is_empty()
     }
@@ -42,6 +45,7 @@ impl VarStore {
         id
     }
 
+    #[must_use]
     pub fn get(&self, id: VarId) -> Gaussian {
         self.marginals[id.0 as usize]
     }

src/factor/rank_diff.rs

@@ -5,12 +5,12 @@ use crate::factor::{Factor, VarId, VarStore};
 /// On each propagation:
 /// - Reads marginals at `team_a` and `team_b` (which already incorporate any
 ///   incoming messages from neighboring factors).
-/// - Computes `new_diff = team_a - team_b` (variance addition; see Gaussian::Sub).
+/// - Computes `new_diff = team_a - team_b` (variance addition; see `Gaussian::Sub`).
 /// - Writes the new marginal to `diff`.
 /// - Returns the delta against the previous diff value.
 ///
 /// This factor does NOT store an outgoing message; the diff variable is
-/// effectively replaced on each propagation. The TruncFactor on the same diff
+/// effectively replaced on each propagation. The `TruncFactor` on the same diff
 /// var holds the EP-divide message that produces the cavity.
 #[derive(Debug)]
 pub struct RankDiffFactor {

src/factor/trunc.rs

@@ -15,13 +15,14 @@ pub struct TruncFactor {
     pub diff: VarId,
     pub margin: f64,
     pub tie: bool,
-    /// Outgoing message to the diff variable (initial: N_INF, the EP identity).
+    /// Outgoing message to the diff variable (initial: `N_INF`, the EP identity).
     pub(crate) msg: Gaussian,
     /// Cached evidence (linear, not log) computed from the cavity on first propagation.
     pub(crate) evidence_cached: Option<f64>,
 }
 
 impl TruncFactor {
+    #[must_use]
     pub fn new(diff: VarId, margin: f64, tie: bool) -> Self {
         Self {
             diff,

src/game.rs

@@ -107,16 +107,13 @@ impl<T: Time, D: Drift<T>> OwnedGame<T, D> {
         convergence: crate::ConvergenceOptions,
     ) -> Self {
         let mut arena = ScratchArena::new();
-        let g = Game::ranked_with_arena(
+
-            teams.clone(),
+        // `Game` takes the teams by value and is dropped here, so take the vec
-            &result,
+        // back out of it rather than handing it a clone.
-            &weights,
+        let g = Game::ranked_with_arena(teams, &result, &weights, p_draw, convergence, &mut arena);
-            p_draw,
+
-            convergence,
-            &mut arena,
-        );
         Self {
-            teams,
+            teams: g.teams,
             likelihoods: g.likelihoods,
             log_evidence: g.log_evidence,
         }
@@ -130,21 +127,24 @@ impl<T: Time, D: Drift<T>> OwnedGame<T, D> {
         convergence: crate::ConvergenceOptions,
     ) -> Self {
         let mut arena = ScratchArena::new();
+
         let g = Game::scored_with_arena(
-            teams.clone(),
+            teams,
             &scores,
             &weights,
             score_sigma,
             convergence,
             &mut arena,
         );
+
         Self {
-            teams,
+            teams: g.teams,
             likelihoods: g.likelihoods,
             log_evidence: g.log_evidence,
         }
     }
 
+    #[must_use]
     pub fn posteriors(&self) -> Vec<Vec<Gaussian>> {
         self.likelihoods
             .iter()
@@ -153,6 +153,7 @@ impl<T: Time, D: Drift<T>> OwnedGame<T, D> {
             .collect()
     }
 
+    #[must_use]
     pub fn log_evidence(&self) -> f64 {
         self.log_evidence
     }
@@ -409,6 +410,7 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
         self.likelihoods = likelihoods;
     }
 
+    #[must_use]
     pub fn posteriors(&self) -> Vec<Vec<Gaussian>> {
         self.likelihoods
             .iter()
@@ -422,12 +424,21 @@ impl<'a, T: Time, D: Drift<T>> Game<'a, T, D> {
             .collect::<Vec<_>>()
     }
 
+    #[must_use]
     pub fn log_evidence(&self) -> f64 {
         self.log_evidence
     }
 }
 
 impl<T: Time, D: Drift<T>> Game<'_, T, D> {
+    /// # Errors
+    ///
+    /// - `InvalidProbability` if `options.p_draw` is outside `[0.0, 1.0)`.
+    /// - `MismatchedShape` if the outcome's rank count differs from `teams.len()`.
+    /// - `WrongOutcomeKind` if `outcome` is not `Outcome::Ranked`.
+    /// - `TieWithoutDrawProbability` if the outcome ties two teams while
+    ///   `p_draw` is zero: the truncation margin is then zero and the two-sided
+    ///   tie update evaluates `0/0`.
     pub fn ranked(
         teams: &[&[Rating<T, D>]],
         outcome: crate::Outcome,
@@ -478,6 +489,12 @@ impl<T: Time, D: Drift<T>> Game<'_, T, D> {
         ))
     }
 
+    /// # Errors
+    ///
+    /// - `InvalidParameter` if `options.score_sigma` is not strictly positive,
+    ///   or is NaN.
+    /// - `MismatchedShape` if the outcome's score count differs from `teams.len()`.
+    /// - `WrongOutcomeKind` if `outcome` is not `Outcome::Scored`.
     pub fn scored(
         teams: &[&[Rating<T, D>]],
         outcome: crate::Outcome,
@@ -515,6 +532,12 @@ impl<T: Time, D: Drift<T>> Game<'_, T, D> {
         ))
     }
 
+    /// # Errors
+    ///
+    /// Delegates to [`Game::ranked`] with default options, so it returns the
+    /// same errors — in practice `WrongOutcomeKind` for a non-ranked outcome,
+    /// or `TieWithoutDrawProbability` for a draw, since the default `p_draw`
+    /// applies rather than one you chose.
     pub fn one_v_one(
         a: &Rating<T, D>,
         b: &Rating<T, D>,
@@ -525,6 +548,10 @@ impl<T: Time, D: Drift<T>> Game<'_, T, D> {
         Ok((post[0][0], post[1][0]))
     }
 
+    /// # Errors
+    ///
+    /// Wraps each player in a one-member team and delegates to
+    /// [`Game::ranked`], so it returns the same errors.
     pub fn free_for_all(
         players: &[&Rating<T, D>],
         outcome: crate::Outcome,

src/gaussian.rs

@@ -18,6 +18,7 @@ pub struct Gaussian {
 
 impl Gaussian {
     /// Construct from mean and standard deviation.
+    #[must_use]
     pub const fn from_ms(mu: f64, sigma: f64) -> Self {
         if sigma == f64::INFINITY {
             Self { pi: 0.0, tau: 0.0 }
@@ -64,16 +65,19 @@ impl Gaussian {
     }
 
     #[inline]
+    #[must_use]
     pub fn pi(&self) -> f64 {
         self.pi
     }
 
     #[inline]
+    #[must_use]
     pub fn tau(&self) -> f64 {
         self.tau
     }
 
     #[inline]
+    #[must_use]
     pub fn mu(&self) -> f64 {
         // A non-positive precision is an improper (uninformative) Gaussian — its mean is
         // undefined. Treat it like `pi == 0` and return 0. EP message cancellation can land
@@ -102,6 +106,7 @@ impl Gaussian {
     }
 
     #[inline]
+    #[must_use]
     pub fn sigma(&self) -> f64 {
         // A non-positive precision is improper → infinite standard deviation. Guarding
         // `pi <= 0.0` (not just `== 0.0`) keeps `1.0 / pi.sqrt()` from returning NaN when EP
@@ -145,6 +150,7 @@ impl Gaussian {
     /// Used by within-game inference to stabilise oscillating fixed-point
     /// loops on hard graphs. `alpha = 1.0` returns `new` exactly;
     /// `alpha < 1.0` shrinks each per-step update.
+    #[must_use]
     pub fn damp_natural(self, new: Gaussian, alpha: f64) -> Gaussian {
         Gaussian::from_natural(
             alpha * new.pi() + (1.0 - alpha) * self.pi(),

src/history.rs

@@ -1,7 +1,7 @@
 use std::{borrow::Borrow, collections::HashMap, hash::Hash, marker::PhantomData};
 
 use crate::{
-    BETA, GAMMA, Index, MU, N_INF, P_DRAW, SIGMA,
+    BETA, GAMMA, Index, MU, P_DRAW, SIGMA,
     competitor::{self, Competitor},
     convergence::{ConvergenceOptions, ConvergenceReport},
     drift::{ConstantDrift, Drift},
@@ -198,6 +198,7 @@ impl Default for History<i64, ConstantDrift, NullObserver, &'static str> {
 }
 
 impl History<i64, ConstantDrift, NullObserver, &'static str> {
+    #[must_use]
     pub fn builder() -> HistoryBuilder<i64, ConstantDrift, NullObserver, &'static str> {
         HistoryBuilder::default()
     }
@@ -205,6 +206,7 @@ impl History<i64, ConstantDrift, NullObserver, &'static str> {
 
 impl<K: Eq + Hash + Clone> History<i64, ConstantDrift, NullObserver, K> {
     /// Like `builder()` but uses a custom key type `K` instead of the default `&'static str`.
+    #[must_use]
     pub fn builder_with_key() -> HistoryBuilder<i64, ConstantDrift, NullObserver, K> {
         HistoryBuilder {
             mu: MU,
@@ -252,7 +254,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         for j in (0..self.time_slices.len() - 1).rev() {
             for agent in self.time_slices[j + 1].skills.keys() {
                 self.agents.get_mut(agent).unwrap().message =
-                    self.time_slices[j + 1].backward_prior_out(&agent, &self.agents);
+                    Some(self.time_slices[j + 1].backward_prior_out(&agent, &self.agents));
             }
 
             let old = self.time_slices[j].posteriors();
@@ -271,7 +273,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         for j in 1..self.time_slices.len() {
             for agent in self.time_slices[j - 1].skills.keys() {
                 self.agents.get_mut(agent).unwrap().message =
-                    self.time_slices[j - 1].forward_prior_out(&agent);
+                    Some(self.time_slices[j - 1].forward_prior_out(&agent));
             }
 
             let old = self.time_slices[j].posteriors();
@@ -552,7 +554,11 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
 
     /// 2-team win probability: returns `[P(team0 wins), P(team1 wins)]`.
     ///
-    /// Panics if `teams.len() != 2`. N-team support lands in T4.
+    /// N-team support lands in T4.
+    ///
+    /// # Panics
+    ///
+    /// Panics if `teams.len() != 2`.
     pub fn predict_outcome(&self, teams: &[&[&K]]) -> Vec<f64> {
         assert_eq!(teams.len(), 2, "predict_outcome T2: 2 teams only");
         let gather = |team: &[&K]| -> Gaussian {
@@ -574,6 +580,15 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     }
 
     /// Run the full forward+backward convergence loop and return a summary.
+    ///
+    /// Failing to reach `epsilon` within `max_iter` is not an error: the
+    /// returned report carries `converged: false` and the final step.
+    ///
+    /// # Errors
+    ///
+    /// `NonFiniteResult` if a sweep produces a NaN or infinite step. EP has
+    /// broken down at that point and further iterations cannot recover, so the
+    /// loop stops rather than reporting a NaN step as convergence.
     pub fn converge(&mut self) -> Result<ConvergenceReport, InferenceError> {
         use std::time::Instant;
 
@@ -588,7 +603,6 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                 log_evidence: 0.0,
                 converged: true,
                 per_iteration_time: SmallVec::new(),
-                slices_skipped: 0,
             });
         }
 
@@ -627,7 +641,6 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
             log_evidence,
             converged,
             per_iteration_time: per_iter,
-            slices_skipped: 0,
         })
     }
 }
@@ -635,18 +648,23 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
 impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O, K> {
     pub(crate) fn add_events_with_prior(
         &mut self,
-        composition: Vec<Vec<Vec<Index>>>,
+        mut composition: Vec<Vec<Vec<Index>>>,
-        results: Vec<Vec<f64>>,
+        mut results: Option<Vec<Vec<f64>>>,
         times: Vec<T>,
-        weights: Vec<Vec<Vec<f64>>>,
+        mut weights: Option<Vec<Vec<Vec<f64>>>>,
         kinds: Vec<EventKind>,
         mut priors: HashMap<Index, Rating<T, D>>,
     ) -> Result<(), InferenceError> {
-        if !results.is_empty() && results.len() != composition.len() {
+        if results
+            .as_ref()
+            .is_some_and(|r| r.len() != composition.len())
+        {
+            let got = results.as_ref().map_or(0, Vec::len);
+
             return Err(InferenceError::MismatchedShape {
                 kind: "results",
                 expected: composition.len(),
-                got: results.len(),
+                got,
             });
         }
         if times.len() != composition.len() {
@@ -656,11 +674,16 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                 got: times.len(),
             });
         }
-        if !weights.is_empty() && weights.len() != composition.len() {
+        if weights
+            .as_ref()
+            .is_some_and(|w| w.len() != composition.len())
+        {
+            let got = weights.as_ref().map_or(0, Vec::len);
+
             return Err(InferenceError::MismatchedShape {
                 kind: "weights",
                 expected: composition.len(),
-                got: weights.len(),
+                got,
             });
         }
         if kinds.len() != composition.len() {
@@ -675,7 +698,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         // including `record_draw`, which builds its results directly rather
         // than going through `Outcome`.
         if self.p_draw == 0.0 {
-            for (event_results, kind) in results.iter().zip(kinds.iter()) {
+            for (event_results, kind) in results.iter().flatten().zip(kinds.iter()) {
                 if !matches!(kind, EventKind::Ranked) {
                     continue;
                 }
@@ -708,7 +731,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                                 self.drift,
                             )
                         }),
-                        message: N_INF,
+                        message: None,
                         last_time: None,
                     },
                 );
@@ -718,6 +741,20 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         let n = composition.len();
         let o = sort_time(&times, false);
 
+        // The chunking loop below MOVES each event's data out of `composition`,
+        // `results` and `weights` instead of cloning it. That is only sound
+        // because `o` is a permutation, so every index is visited exactly once
+        // — visiting one twice would silently yield an empty event rather than
+        // failing.
+        debug_assert!(
+            {
+                let mut seen = vec![false; n];
+                o.iter()
+                    .all(|&idx| !std::mem::replace(&mut seen[idx], true))
+            },
+            "sort_time must return a permutation of 0..{n}"
+        );
+
         let mut i = 0;
         let mut k = 0;
 
@@ -746,7 +783,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                         let agent = self.agents.get_mut(*agent_idx).unwrap();
 
                         agent.last_time = Some(time_slice.time);
-                        agent.message = time_slice.forward_prior_out(agent_idx);
+                        agent.message = Some(time_slice.forward_prior_out(agent_idx));
                     }
                 }
 
@@ -754,20 +791,20 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
             }
 
             let composition = (i..j)
-                .map(|e| composition[o[e]].clone())
+                .map(|e| std::mem::take(&mut composition[o[e]]))
                 .collect::<Vec<_>>();
 
-            let results = if results.is_empty() {
+            let results = results.as_mut().map(|results| {
-                Vec::new()
+                (i..j)
-            } else {
+                    .map(|e| std::mem::take(&mut results[o[e]]))
-                (i..j).map(|e| results[o[e]].clone()).collect::<Vec<_>>()
+                    .collect::<Vec<_>>()
-            };
+            });
 
-            let weights = if weights.is_empty() {
+            let weights = weights.as_mut().map(|weights| {
-                Vec::new()
+                (i..j)
-            } else {
+                    .map(|e| std::mem::take(&mut weights[o[e]]))
-                (i..j).map(|e| weights[o[e]].clone()).collect::<Vec<_>>()
+                    .collect::<Vec<_>>()
-            };
+            });
 
             let kinds_chunk: Vec<EventKind> = (i..j).map(|e| kinds[o[e]]).collect();
 
@@ -779,7 +816,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                     let agent = self.agents.get_mut(agent_idx).unwrap();
 
                     agent.last_time = Some(t);
-                    agent.message = time_slice.forward_prior_out(&agent_idx);
+                    agent.message = Some(time_slice.forward_prior_out(&agent_idx));
                 }
 
                 k += 1;
@@ -795,7 +832,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                     let agent = self.agents.get_mut(agent_idx).unwrap();
 
                     agent.last_time = Some(t);
-                    agent.message = time_slice.forward_prior_out(&agent_idx);
+                    agent.message = Some(time_slice.forward_prior_out(&agent_idx));
                 }
 
                 k += 1;
@@ -819,7 +856,7 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                     let agent = self.agents.get_mut(*agent_idx).unwrap();
 
                     agent.last_time = Some(time_slice.time);
-                    agent.message = time_slice.forward_prior_out(agent_idx);
+                    agent.message = Some(time_slice.forward_prior_out(agent_idx));
                 }
             }
 
@@ -830,6 +867,13 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         Ok(())
     }
 
+    /// Record a single two-competitor event that `winner` won.
+    ///
+    /// # Errors
+    ///
+    /// Ingests through the same path as [`History::add_events`], so it returns
+    /// the same errors. A two-team decisive outcome cannot tie, so
+    /// `TieWithoutDrawProbability` is not reachable here.
     pub fn record_winner<Q>(&mut self, winner: &Q, loser: &Q, time: T) -> Result<(), InferenceError>
     where
         K: Borrow<Q>,
@@ -839,14 +883,21 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         let l = self.intern(loser);
         self.add_events_with_prior(
             vec![vec![vec![w], vec![l]]],
-            vec![vec![1.0, 0.0]],
+            Some(vec![vec![1.0, 0.0]]),
             vec![time],
-            vec![],
+            None,
             vec![EventKind::Ranked],
             HashMap::new(),
         )
     }
 
+    /// Record a single two-competitor event that ended level.
+    ///
+    /// # Errors
+    ///
+    /// Ingests through the same path as [`History::add_events`]. Note
+    /// `TieWithoutDrawProbability` *is* reachable here: a draw needs a
+    /// positive `p_draw`.
     pub fn record_draw<Q>(&mut self, a: &Q, b: &Q, time: T) -> Result<(), InferenceError>
     where
         K: Borrow<Q>,
@@ -856,9 +907,9 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
         let b_idx = self.intern(b);
         self.add_events_with_prior(
             vec![vec![vec![a_idx], vec![b_idx]]],
-            vec![vec![0.0, 0.0]],
+            Some(vec![vec![0.0, 0.0]]),
             vec![time],
-            vec![],
+            None,
             vec![EventKind::Ranked],
             HashMap::new(),
         )
@@ -870,6 +921,17 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
     }
 
     /// Bulk-ingest typed events.
+    ///
+    /// # Errors
+    ///
+    /// - `MismatchedShape` if an event's outcome does not describe the same
+    ///   number of teams the event has, or if per-member weights do not match
+    ///   the team's membership.
+    /// - `InvalidParameter` if a per-event `score_sigma` override is not
+    ///   strictly positive.
+    /// - `TieWithoutDrawProbability` if an event ties two teams while the
+    ///   history's `p_draw` is zero. This includes `Outcome::winner(w, n)` for
+    ///   `n >= 3`, which ties every loser.
     pub fn add_events<I>(&mut self, events: I) -> Result<(), InferenceError>
     where
         I: IntoIterator<Item = crate::event::Event<T, K>>,
@@ -906,8 +968,37 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
                     let idx = self.keys.get_or_create(&member.key);
                     team_indices.push(idx);
                     team_weights.push(member.weight);
+
+                    if let Some(scale) = member.drift_scale {
+                        // Squaring would make a negative scale behave as its
+                        // absolute value, so reject rather than silently
+                        // accept a sign the caller cannot have meant.
+                        if !scale.is_finite() || scale < 0.0 {
+                            return Err(InferenceError::InvalidParameter {
+                                name: "drift_scale",
+                                value: scale,
+                            });
+                        }
+                    }
+
+                    // `prior` and `drift_scale` are competitor configuration,
+                    // captured here and consumed at competitor creation. Both
+                    // land in the same entry so a member may set either alone.
+                    if member.prior.is_some() || member.drift_scale.is_some() {
+                        let rating = priors.entry(idx).or_insert_with(|| {
+                            Rating::new(
+                                Gaussian::from_ms(self.mu, self.sigma),
+                                self.beta,
+                                self.drift,
+                            )
+                        });
+
                         if let Some(prior) = member.prior {
-                        priors.insert(idx, Rating::new(prior, self.beta, self.drift));
+                            rating.prior = prior;
+                        }
+                        if let Some(scale) = member.drift_scale {
+                            rating.drift_scale = scale;
+                        }
                     }
                 }
                 event_comp.push(team_indices);
@@ -941,7 +1032,13 @@ impl<T: Time, D: Drift<T>, O: Observer<T>, K: Eq + Hash + Clone> History<T, D, O
             times.push(ev.time);
         }
 
-        self.add_events_with_prior(composition, results, times, weights, kinds, priors)
+        let weights = if weights.is_empty() {
+            None
+        } else {
+            Some(weights)
+        };
+
+        self.add_events_with_prior(composition, Some(results), times, weights, kinds, priors)
     }
 }
 
@@ -956,6 +1053,49 @@ mod tests {
         arena::ScratchArena,
     };
 
+    /// #17: a slice's footprint must be O(competitors in the slice), not
+    /// O(largest global index it touches). The store used to be a dense
+    /// `Vec<Skill>` indexed by `Index.0`, so the same two-competitor games cost
+    /// 20,000 slots per slice when the competitors sat at the top of a large
+    /// roster. Measured end to end, peak RSS was 309 MB against 52 MB.
+    #[test]
+    fn per_slice_footprint_is_independent_of_index_magnitude() {
+        fn total_skill_slots(high_indices: bool) -> usize {
+            let mut h: History<i64, ConstantDrift, NullObserver, String> =
+                History::builder_with_key().build();
+
+            for i in 0..2_000 {
+                h.intern(&format!("k{i:05}"));
+            }
+
+            let (a, b) = if high_indices {
+                ("k01998".to_string(), "k01999".to_string())
+            } else {
+                ("k00000".to_string(), "k00001".to_string())
+            };
+
+            for time in 1..=20i64 {
+                h.record_winner(&a, &b, time).unwrap();
+            }
+
+            h.time_slices
+                .iter()
+                .map(|ts| ts.skills.allocated_slots())
+                .sum()
+        }
+
+        let low = total_skill_slots(false);
+        let high = total_skill_slots(true);
+
+        assert_eq!(low, high, "footprint must not depend on index magnitude");
+
+        // A dense store over a 2,000-key roster would allocate 20 x 2,000.
+        assert!(
+            high < 1_000,
+            "20 slices of 2 competitors allocated {high} slots"
+        );
+    }
+
     fn make_events_1v1(
         pairs: &[(&'static str, &'static str)],
         outcomes: &[Outcome],

src/key_table.rs

@@ -25,6 +25,7 @@ impl<K> KeyTable<K>
 where
     K: Eq + Hash + Clone,
 {
+    #[must_use]
     pub fn new() -> Self {
         Self {
             forward: HashMap::new(),
@@ -54,6 +55,7 @@ where
         }
     }
 
+    #[must_use]
     pub fn key(&self, idx: Index) -> Option<&K> {
         self.reverse.get(idx.0)
     }
@@ -62,10 +64,12 @@ where
         self.forward.keys()
     }
 
+    #[must_use]
     pub fn len(&self) -> usize {
         self.reverse.len()
     }
 
+    #[must_use]
     pub fn is_empty(&self) -> bool {
         self.reverse.is_empty()
     }

src/lib.rs

@@ -1,6 +1,6 @@
-//! TrueSkill Through Time — Bayesian skill rating over a time axis.
+//! `TrueSkill` Through Time — Bayesian skill rating over a time axis.
 //!
-//! Where plain TrueSkill gives each competitor one running estimate, TrueSkill
+//! Where plain `TrueSkill` gives each competitor one running estimate, `TrueSkill`
 //! Through Time treats a whole history as a single model and infers skill *at
 //! every point in time*. Evidence flows both directions: a result today
 //! sharpens the estimate of who someone was last year, so early estimates stop
@@ -361,6 +361,7 @@ pub(crate) fn sort_time<T: Copy + Ord>(xs: &[T], reverse: bool) -> Vec<usize> {
 /// Panics if fewer than two rating groups are supplied, or if any group is
 /// empty — match quality is a property of a contest between at least two
 /// non-empty sides.
+#[must_use]
 pub fn quality(rating_groups: &[&[Gaussian]], beta: f64) -> f64 {
     assert!(
         rating_groups.len() >= 2,

src/outcome.rs

@@ -29,7 +29,13 @@ pub enum Outcome {
 impl Outcome {
     /// `n`-team outcome where team `winner` won and everyone else tied for last.
     ///
+    /// Note this ties every loser, so for `n >= 3` it needs a positive
+    /// `p_draw` — see `InferenceError::TieWithoutDrawProbability`.
+    ///
+    /// # Panics
+    ///
     /// Panics if `winner >= n`.
+    #[must_use]
     pub fn winner(winner: u32, n: u32) -> Self {
         assert!(winner < n, "winner index {winner} out of range 0..{n}");
         let ranks: SmallVec<[u32; 4]> = (0..n).map(|i| if i == winner { 0 } else { 1 }).collect();
@@ -37,6 +43,7 @@ impl Outcome {
     }
 
     /// All `n` teams tied.
+    #[must_use]
     pub fn draw(n: u32) -> Self {
         Self::Ranked(SmallVec::from_vec(vec![0; n as usize]))
     }
@@ -68,6 +75,7 @@ impl Outcome {
         }
     }
 
+    #[must_use]
     pub fn team_count(&self) -> usize {
         match self {
             Self::Ranked(r) => r.len(),

src/rating.rs

@@ -16,6 +16,9 @@ pub struct Rating<T: Time = i64, D: Drift<T> = ConstantDrift> {
     pub(crate) prior: Gaussian,
     pub(crate) beta: f64,
     pub(crate) drift: D,
+    /// Multiplier on the drift *variance* this competitor accumulates; 1.0 is
+    /// the neutral default. Set per competitor via `Member::with_drift_scale`.
+    pub(crate) drift_scale: f64,
     pub(crate) _time: PhantomData<T>,
 }
 
@@ -25,10 +28,21 @@ impl<T: Time, D: Drift<T>> Rating<T, D> {
             prior,
             beta,
             drift,
+            drift_scale: 1.0,
             _time: PhantomData,
         }
     }
 
+    /// Scale how fast this competitor drifts, relative to `drift`.
+    ///
+    /// Multiplies the drift *variance*, so the scale is in the same units as
+    /// `gamma`. `0.0` pins the competitor still.
+    #[must_use]
+    pub fn with_drift_scale(mut self, drift_scale: f64) -> Self {
+        self.drift_scale = drift_scale;
+        self
+    }
+
     /// The configured prior skill estimate.
     #[must_use]
     pub fn prior(&self) -> Gaussian {
@@ -47,6 +61,28 @@ impl<T: Time, D: Drift<T>> Rating<T, D> {
         self.drift
     }
 
+    /// This competitor's multiplier on the drift variance; 1.0 is neutral.
+    #[must_use]
+    pub fn drift_scale(&self) -> f64 {
+        self.drift_scale
+    }
+
+    /// Drift variance accumulated over `from -> to`, scaled for this competitor.
+    ///
+    /// The single place the scale is applied for a `Time`-typed span. Callers
+    /// must go through this rather than `self.drift` directly, so a competitor's
+    /// scale cannot be silently skipped.
+    pub(crate) fn drift_variance_delta(&self, from: &T, to: &T) -> f64 {
+        self.drift.variance_delta(from, to) * self.drift_scale * self.drift_scale
+    }
+
+    /// Drift variance for a cached elapsed count, scaled for this competitor.
+    ///
+    /// The counterpart of `drift_variance_delta` for the cached-elapsed paths.
+    pub(crate) fn drift_variance_for_elapsed(&self, elapsed: i64) -> f64 {
+        self.drift.variance_for_elapsed(elapsed) * self.drift_scale * self.drift_scale
+    }
+
     pub(crate) fn performance(&self) -> Gaussian {
         self.prior.forget(self.beta.powi(2))
     }
@@ -58,6 +94,7 @@ impl Default for Rating<i64, ConstantDrift> {
             prior: Gaussian::default(),
             beta: BETA,
             drift: ConstantDrift(GAMMA),
+            drift_scale: 1.0,
             _time: PhantomData,
         }
     }

src/schedule.rs

@@ -1,7 +1,7 @@
 //! Schedule trait and built-in implementations.
 //!
 //! A schedule drives factor propagation to convergence. The default
-//! `EpsilonOrMax` performs one TeamSum sweep (setup) then alternating
+//! `EpsilonOrMax` performs one `TeamSum` sweep (setup) then alternating
 //! forward/backward sweeps over the iterating factors until the max
 //! delta drops below epsilon or `max` iterations is reached.
 
@@ -23,7 +23,7 @@ pub trait Schedule: Send + Sync {
 /// Default schedule: sweep forward then backward until step ≤ eps or iter == max.
 ///
 /// Matches the existing `Game::likelihoods` loop bit-for-bit when given the
-/// same factor layout (TeamSums first, then alternating RankDiff/Trunc pairs).
+/// same factor layout (`TeamSums` first, then alternating RankDiff/Trunc pairs).
 #[derive(Debug, Clone, Copy)]
 pub struct EpsilonOrMax {
     pub eps: f64,

src/storage/competitor_store.rs

@@ -2,7 +2,7 @@ use crate::{Index, competitor::Competitor, drift::Drift, time::Time};
 
 /// Dense Vec-backed store for competitor state in History.
 ///
-/// Indexed directly by Index.0, eliminating HashMap hashing in the
+/// Indexed directly by Index.0, eliminating `HashMap` hashing in the
 /// forward/backward sweep. Uses `Vec<Option<Competitor<T, D>>>` so slots can be
 /// absent without an explicit present mask.
 #[derive(Debug)]
@@ -21,6 +21,7 @@ impl<T: Time, D: Drift<T>> Default for CompetitorStore<T, D> {
 }
 
 impl<T: Time, D: Drift<T>> CompetitorStore<T, D> {
+    #[must_use]
     pub fn new() -> Self {
         Self::default()
     }
@@ -39,6 +40,7 @@ impl<T: Time, D: Drift<T>> CompetitorStore<T, D> {
         self.competitors[idx.0] = Some(competitor);
     }
 
+    #[must_use]
     pub fn get(&self, idx: Index) -> Option<&Competitor<T, D>> {
         self.competitors.get(idx.0).and_then(|slot| slot.as_ref())
     }
@@ -49,14 +51,17 @@ impl<T: Time, D: Drift<T>> CompetitorStore<T, D> {
             .and_then(|slot| slot.as_mut())
     }
 
+    #[must_use]
     pub fn contains(&self, idx: Index) -> bool {
         self.get(idx).is_some()
     }
 
+    #[must_use]
     pub fn len(&self) -> usize {
         self.n_present
     }
 
+    #[must_use]
     pub fn is_empty(&self) -> bool {
         self.n_present == 0
     }

src/storage/skill_store.rs

@@ -1,15 +1,27 @@
+use std::collections::HashMap;
+
 use crate::{Index, time_slice::Skill};
 
-/// Dense Vec-backed store for per-agent skill state within a TimeSlice.
+/// Compact per-slice store for skill state, addressed by a slice-local slot.
 ///
-/// Indexed directly by Index.0, eliminating HashMap hashing in the inner
+/// `skills` holds one entry per competitor **in this slice**, so memory is
-/// convergence loop. Uses a parallel `present` mask so iteration skips
+/// O(competitors in the slice). It used to be a dense `Vec<Skill>` indexed by
-/// absent slots without incurring per-slot Option overhead in the hot path.
+/// the global `Index.0`, which made a slice's footprint O(largest index it
+/// touches): a single 1v1 game between competitors 19998 and 19999 reserved
+/// 20,000 slots.
+///
+/// The dense layout existed to keep `HashMap` hashing out of the inner
+/// convergence loop, and that property is preserved. `slots` is consulted only
+/// while building a slice; every hot-path access goes through
+/// [`SkillStore::at`] / [`SkillStore::at_mut`] with a slot resolved once at
+/// ingestion and cached on the event's `Item`.
 #[derive(Debug, Default)]
 pub struct SkillStore {
     skills: Vec<Skill>,
-    present: Vec<bool>,
+    /// Slot -> global index, parallel to `skills`, so iteration can report the
-    n_present: usize,
+    /// global index without a reverse lookup.
+    indices: Vec<Index>,
+    slots: HashMap<Index, u32>,
 }
 
 impl SkillStore {
@@ -17,73 +29,99 @@ impl SkillStore {
         Self::default()
     }
 
-    fn ensure_capacity(&mut self, idx: usize) {
+    /// Resolve a global index to this slice's slot, if the competitor is here.
-        if idx >= self.skills.len() {
+    ///
-            self.skills.resize_with(idx + 1, Skill::default);
+    /// This hashes. Call it at ingestion and cache the result; do not call it
-            self.present.resize(idx + 1, false);
+    /// from the convergence loop.
-        }
+    pub fn slot_of(&self, idx: Index) -> Option<u32> {
+        self.slots.get(&idx).copied()
     }
 
-    pub fn insert(&mut self, idx: Index, skill: Skill) {
+    /// Skill at a slot resolved earlier by [`SkillStore::slot_of`].
-        self.ensure_capacity(idx.0);
+    ///
-        if !self.present[idx.0] {
+    /// # Panics
-            self.n_present += 1;
+    ///
+    /// Panics if `slot` is out of range, which means it came from a different
+    /// slice's store.
+    pub fn at(&self, slot: u32) -> &Skill {
+        &self.skills[slot as usize]
+    }
+
+    /// Mutable counterpart to [`SkillStore::at`].
+    ///
+    /// # Panics
+    ///
+    /// Panics if `slot` is out of range.
+    pub fn at_mut(&mut self, slot: u32) -> &mut Skill {
+        &mut self.skills[slot as usize]
+    }
+
+    /// Insert or overwrite a competitor's skill, returning its slot.
+    pub fn insert(&mut self, idx: Index, skill: Skill) -> u32 {
+        match self.slots.get(&idx) {
+            Some(&slot) => {
+                self.skills[slot as usize] = skill;
+                slot
+            }
+            None => {
+                let slot = u32::try_from(self.skills.len())
+                    .expect("a time slice cannot hold more than u32::MAX competitors");
+
+                self.skills.push(skill);
+                self.indices.push(idx);
+                self.slots.insert(idx, slot);
+
+                slot
+            }
         }
-        self.skills[idx.0] = skill;
-        self.present[idx.0] = true;
     }
 
     pub fn get(&self, idx: Index) -> Option<&Skill> {
-        if idx.0 < self.present.len() && self.present[idx.0] {
+        self.slot_of(idx).map(|slot| self.at(slot))
-            Some(&self.skills[idx.0])
-        } else {
-            None
-        }
-    }
-
-    /// Whether a slot is occupied. Test-only.
-    #[cfg(test)]
-    pub fn contains(&self, idx: Index) -> bool {
-        idx.0 < self.present.len() && self.present[idx.0]
-    }
-
-    /// Number of occupied slots. Test-only.
-    #[cfg(test)]
-    pub fn len(&self) -> usize {
-        self.n_present
     }
 
     pub fn get_mut(&mut self, idx: Index) -> Option<&mut Skill> {
-        if idx.0 < self.present.len() && self.present[idx.0] {
+        self.slot_of(idx)
-            Some(&mut self.skills[idx.0])
+            .map(|slot| &mut self.skills[slot as usize])
-        } else {
-            None
-        }
     }
 
-    pub fn iter(&self) -> impl Iterator<Item = (Index, &Skill)> {
+    /// Whether a competitor is present in this slice. Test-only.
-        self.present.iter().enumerate().filter_map(|(i, &p)| {
+    #[cfg(test)]
-            if p {
+    pub fn contains(&self, idx: Index) -> bool {
-                Some((Index(i), &self.skills[i]))
+        self.slots.contains_key(&idx)
-            } else {
-                None
     }
-        })
+
+    /// Number of competitors in this slice. Test-only.
+    #[cfg(test)]
+    pub fn len(&self) -> usize {
+        self.skills.len()
+    }
+
+    /// Slots actually allocated — the quantity #17 is about, and NOT the same
+    /// as `len` for every possible implementation.
+    ///
+    /// A store indexed by the global `Index` must report `max_index + 1` here
+    /// while reporting the true competitor count from `len`, which is exactly
+    /// how the original defect hid. Tests that mean to pin the footprint must
+    /// assert on this.
+    #[cfg(test)]
+    pub fn allocated_slots(&self) -> usize {
+        self.skills.len()
+    }
+
+    /// Iterate in slot order — the order competitors were first seen in this
+    /// slice. Deterministic for a given event order, which is what the
+    /// cross-thread determinism test relies on.
+    pub fn iter(&self) -> impl Iterator<Item = (Index, &Skill)> {
+        self.indices.iter().copied().zip(self.skills.iter())
     }
 
     pub fn iter_mut(&mut self) -> impl Iterator<Item = (Index, &mut Skill)> {
-        self.skills
+        self.indices.iter().copied().zip(self.skills.iter_mut())
-            .iter_mut()
-            .zip(self.present.iter())
-            .enumerate()
-            .filter_map(|(i, (s, &p))| if p { Some((Index(i), s)) } else { None })
     }
 
     pub fn keys(&self) -> impl Iterator<Item = Index> + '_ {
-        self.present
+        self.indices.iter().copied()
-            .iter()
-            .enumerate()
-            .filter_map(|(i, &p)| if p { Some(Index(i)) } else { None })
     }
 }
 
@@ -109,7 +147,7 @@ mod tests {
     }
 
     #[test]
-    fn iter_skips_absent_slots() {
+    fn iter_reports_global_indices() {
         let mut store = SkillStore::new();
         store.insert(Index(0), Skill::default());
         store.insert(Index(5), Skill::default());
@@ -124,4 +162,28 @@ mod tests {
         store.insert(Index(2), Skill::default());
         assert_eq!(store.len(), 1);
     }
+
+    /// The defect in #17: a slice holding two competitors must cost the same
+    /// whether their indices are small or large.
+    #[test]
+    fn footprint_is_independent_of_index_magnitude() {
+        let mut low = SkillStore::new();
+        low.insert(Index(0), Skill::default());
+        low.insert(Index(1), Skill::default());
+
+        let mut high = SkillStore::new();
+        high.insert(Index(19_998), Skill::default());
+        high.insert(Index(19_999), Skill::default());
+
+        assert_eq!(low.len(), high.len());
+        assert_eq!(low.skills.capacity(), high.skills.capacity());
+    }
+
+    #[test]
+    fn slot_survives_reinsert() {
+        let mut store = SkillStore::new();
+        let first = store.insert(Index(7), Skill::default());
+        let again = store.insert(Index(7), Skill::default());
+        assert_eq!(first, again);
+    }
 }

src/time_slice.rs

@@ -51,6 +51,13 @@ pub enum EventKind {
 #[derive(Clone, Debug)]
 struct Item {
     agent: Index,
+    /// This competitor's slot in the owning slice's `SkillStore`, resolved
+    /// once at ingestion.
+    ///
+    /// The convergence loop reaches skills through this rather than through
+    /// `agent`, which is what keeps `HashMap` hashing out of the hot path now
+    /// that the store is compact rather than indexed by the global `Index`.
+    slot: u32,
     likelihood: Gaussian,
 }
 
@@ -62,12 +69,13 @@ impl Item {
         agents: &CompetitorStore<T, D>,
     ) -> Rating<T, D> {
         let r = &agents[self.agent].rating;
-        let skill = skills.get(self.agent).unwrap();
+        let skill = skills.at(self.slot);
 
         if forward {
-            Rating::new(skill.forward, r.beta, r.drift)
+            Rating::new(skill.forward, r.beta, r.drift).with_drift_scale(r.drift_scale)
         } else {
             Rating::new(skill.posterior() / self.likelihood, r.beta, r.drift)
+                .with_drift_scale(r.drift_scale)
         }
     }
 }
@@ -157,9 +165,9 @@ impl Event {
         for (t, team) in self.teams.iter_mut().enumerate() {
             for (i, item) in team.items.iter_mut().enumerate() {
                 let fresh = update.likelihoods[t][i];
-                let old_likelihood = skills.get(item.agent).unwrap().likelihood;
+                let old_likelihood = skills.at(item.slot).likelihood;
                 let new_likelihood = (old_likelihood / item.likelihood) * fresh;
-                skills.get_mut(item.agent).unwrap().likelihood = new_likelihood;
+                skills.at_mut(item.slot).likelihood = new_likelihood;
                 item.likelihood = fresh;
             }
         }
@@ -277,8 +285,8 @@ impl<T: Time> TimeSlice<T> {
     pub fn add_events<D: Drift<T>>(
         &mut self,
         composition: Vec<Vec<Vec<Index>>>,
-        results: Vec<Vec<f64>>,
+        results: Option<Vec<Vec<f64>>>,
-        weights: Vec<Vec<Vec<f64>>>,
+        weights: Option<Vec<Vec<Vec<f64>>>>,
         kinds: Vec<EventKind>,
         agents: &CompetitorStore<T, D>,
     ) {
@@ -297,14 +305,16 @@ impl<T: Time> TimeSlice<T> {
         for idx in this_agent {
             let elapsed = compute_elapsed(agents[*idx].last_time.as_ref(), &self.time);
 
+            let forward = agents[*idx].receive(&self.time);
+
             if let Some(skill) = self.skills.get_mut(*idx) {
                 skill.elapsed = elapsed;
-                skill.forward = agents[*idx].receive(&self.time);
+                skill.forward = forward;
             } else {
                 self.skills.insert(
                     *idx,
                     Skill {
-                        forward: agents[*idx].receive(&self.time),
+                        forward,
                         backward: N_INF,
                         likelihood: N_INF,
                         elapsed,
@@ -313,6 +323,8 @@ impl<T: Time> TimeSlice<T> {
             }
         }
 
+        let skills = &self.skills;
+
         let events = composition.iter().enumerate().map(|(e, event)| {
             let teams = event
                 .iter()
@@ -322,28 +334,32 @@ impl<T: Time> TimeSlice<T> {
                         .iter()
                         .map(|&agent| Item {
                             agent,
+                            // Every participant was inserted into `skills`
+                            // just above, so the slot always resolves.
+                            slot: skills
+                                .slot_of(agent)
+                                .expect("participant must be present in the slice store"),
                             likelihood: N_INF,
                         })
                         .collect::<Vec<_>>();
 
                     Team {
                         items,
-                        output: if results.is_empty() {
+                        output: match &results {
-                            (event.len() - (t + 1)) as f64
+                            Some(results) => results[e][t],
-                        } else {
+                            // No explicit result: rank by position, first team best.
-                            results[e][t]
+                            None => (event.len() - (t + 1)) as f64,
                         },
                     }
                 })
                 .collect::<Vec<_>>();
 
-            let weights = if weights.is_empty() {
+            let weights = match &weights {
-                teams
+                Some(weights) => weights[e].clone(),
+                None => teams
                     .iter()
                     .map(|team| vec![1.0; team.items.len()])
-                    .collect::<Vec<_>>()
+                    .collect::<Vec<_>>(),
-            } else {
-                weights[e].clone()
             };
 
             Event {
@@ -370,6 +386,13 @@ impl<T: Time> TimeSlice<T> {
             .collect::<HashMap<_, _>>()
     }
 
+    /// Sweep this slice's events once, starting at index `from`.
+    ///
+    /// # Panics
+    ///
+    /// Panics if an event references a competitor with no entry in this
+    /// slice's skill store. `add_events` inserts one for every participant, so
+    /// this cannot happen for slices built through the public API.
     pub fn iteration<D: Drift<T>>(&mut self, from: usize, agents: &CompetitorStore<T, D>) {
         if from == 0 && self.color_groups_dirty {
             self.recompute_color_groups();
@@ -402,10 +425,10 @@ impl<T: Time> TimeSlice<T> {
 
                 for (t, team) in event.teams.iter_mut().enumerate() {
                     for (i, item) in team.items.iter_mut().enumerate() {
-                        let old_likelihood = self.skills.get(item.agent).unwrap().likelihood;
+                        let old_likelihood = self.skills.at(item.slot).likelihood;
                         let new_likelihood =
                             (old_likelihood / item.likelihood) * g.likelihoods[t][i];
-                        self.skills.get_mut(item.agent).unwrap().likelihood = new_likelihood;
+                        self.skills.at_mut(item.slot).likelihood = new_likelihood;
                         item.likelihood = g.likelihoods[t][i];
                     }
                 }
@@ -567,14 +590,13 @@ impl<T: Time> TimeSlice<T> {
         n.forget(
             agents[*agent]
                 .rating
-                .drift
+                .drift_variance_for_elapsed(skill.elapsed),
-                .variance_for_elapsed(skill.elapsed),
         )
     }
 
     pub(crate) fn new_backward_info<D: Drift<T>>(&mut self, agents: &CompetitorStore<T, D>) {
         for (agent, skill) in self.skills.iter_mut() {
-            skill.backward = agents[agent].message;
+            skill.backward = agents[agent].message.unwrap_or(N_INF);
         }
         self.iteration(0, agents);
     }
@@ -623,11 +645,11 @@ impl<T: Time> TimeSlice<T> {
             let rating = &agents[agent].rating;
 
             let forward = match incoming.get(&agent) {
-                Some(message) => message.forget(rating.drift.variance_for_elapsed(skill.elapsed)),
+                Some(message) => message.forget(rating.drift_variance_for_elapsed(skill.elapsed)),
                 None => rating.prior,
             };
 
-            scratch.skills.insert(
+            let slot = scratch.skills.insert(
                 agent,
                 Skill {
                     forward,
@@ -636,6 +658,17 @@ impl<T: Time> TimeSlice<T> {
                     elapsed: skill.elapsed,
                 },
             );
+
+            // The cloned events carry slots resolved against the REAL store, so
+            // the scratch must assign the same ones. It does because `iter()`
+            // yields slot order and `insert` allocates slots in call order —
+            // but that is a coupling between two types, so pin it here rather
+            // than leave it to be rediscovered after it breaks.
+            debug_assert_eq!(
+                Some(slot),
+                self.skills.slot_of(agent),
+                "scratch slot must match the real slice's slot for {agent:?}"
+            );
         }
 
         scratch.iterate_to_convergence(agents);
@@ -754,8 +787,26 @@ impl<T: Time> TimeSlice<T> {
     }
 }
 
+/// Elapsed time from a competitor's previous appearance to `current`.
+///
+/// A negative elapsed means slices are being visited out of time order, which
+/// would make drift *reduce* uncertainty. Release builds clamp to zero so a
+/// bad timestamp degrades to "no drift" rather than corrupting the posterior;
+/// debug builds trip instead, because reaching here is a bug in slice ordering
+/// rather than something callers can cause with ordinary data.
 pub(crate) fn compute_elapsed<T: Time>(last: Option<&T>, current: &T) -> i64 {
-    last.map(|l| l.elapsed_to(current).max(0)).unwrap_or(0)
+    let Some(last) = last else {
+        return 0;
+    };
+
+    let elapsed = last.elapsed_to(current);
+
+    debug_assert!(
+        elapsed >= 0,
+        "negative elapsed ({elapsed}) — slices visited out of time order"
+    );
+
+    elapsed.max(0)
 }
 
 #[cfg(test)]
@@ -803,8 +854,8 @@ mod tests {
                 vec![vec![c], vec![d]],
                 vec![vec![e], vec![f]],
             ],
-            vec![vec![1.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0]],
+            Some(vec![vec![1.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0]]),
-            vec![],
+            None,
             vec![EventKind::Ranked; 3],
             &agents,
         );
@@ -880,8 +931,8 @@ mod tests {
                 vec![vec![a], vec![c]],
                 vec![vec![b], vec![c]],
             ],
-            vec![vec![1.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0]],
+            Some(vec![vec![1.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0]]),
-            vec![],
+            None,
             vec![EventKind::Ranked; 3],
             &agents,
         );
@@ -960,8 +1011,8 @@ mod tests {
                 vec![vec![a], vec![c]],
                 vec![vec![b], vec![c]],
             ],
-            vec![vec![1.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0]],
+            Some(vec![vec![1.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0]]),
-            vec![],
+            None,
             vec![EventKind::Ranked; 3],
             &agents,
         );
@@ -992,8 +1043,8 @@ mod tests {
                 vec![vec![a], vec![c]],
                 vec![vec![b], vec![c]],
             ],
-            vec![vec![1.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0]],
+            Some(vec![vec![1.0, 0.0], vec![0.0, 1.0], vec![1.0, 0.0]]),
-            vec![],
+            None,
             vec![EventKind::Ranked; 3],
             &agents,
         );
@@ -1063,8 +1114,8 @@ mod tests {
                 vec![vec![c], vec![d]],
                 vec![vec![a], vec![c]],
             ],
-            vec![vec![1.0, 0.0], vec![1.0, 0.0], vec![1.0, 0.0]],
+            Some(vec![vec![1.0, 0.0], vec![1.0, 0.0], vec![1.0, 0.0]]),
-            vec![],
+            None,
             vec![EventKind::Ranked; 3],
             &agents,
         );

tests/api_shape.rs

@@ -247,3 +247,46 @@ fn fluent_event_builder_scores() {
     let b = h.current_skill(&"bob").unwrap();
     assert!(a.mu() > b.mu());
 }
+
+/// Every field of `ConvergenceReport` must carry real information.
+///
+/// `slices_skipped` was public, hardcoded to `0`, and reported a plausible
+/// value for a feature that never existed — the same shape as the inert
+/// `online` flag in #19. It was removed in #33. This pins the remaining fields
+/// so the next always-constant member has to survive an assertion rather than
+/// just a reviewer's attention.
+#[test]
+fn every_convergence_report_field_is_populated() {
+    let mut h = History::builder().build();
+
+    for time in 1..=6i64 {
+        h.record_winner(&"a", &"b", time).unwrap();
+    }
+
+    let report = h.converge().unwrap();
+
+    assert!(
+        report.iterations > 0,
+        "iterations is zero on a real converge"
+    );
+
+    assert!(report.converged, "fixture must converge");
+
+    assert!(
+        report.final_step.0.is_finite() && report.final_step.1.is_finite(),
+        "final_step is not finite: {:?}",
+        report.final_step
+    );
+
+    assert!(
+        report.log_evidence.is_finite() && report.log_evidence < 0.0,
+        "log_evidence is not a finite negative log probability: {}",
+        report.log_evidence
+    );
+
+    assert_eq!(
+        report.per_iteration_time.len(),
+        report.iterations,
+        "per_iteration_time must carry one duration per iteration"
+    );
+}

tests/common/mod.rs

@@ -0,0 +1,38 @@
+//! Helpers shared across the integration suites.
+//!
+//! Each integration file is its own binary, so `mod common;` compiles a copy
+//! per suite. Anything unused in a given suite would warn, hence the
+//! `#![allow(dead_code)]`.
+
+#![allow(dead_code)]
+
+use trueskill_tt::Gaussian;
+
+/// A posterior must be finite with a strictly positive sigma.
+///
+/// A non-finite posterior is the failure mode this crate is most prone to —
+/// EP breaking down produces NaN rather than an error — and a zero or negative
+/// sigma means the precision went non-positive, which `Gaussian::sigma` reports
+/// as improper rather than trapping.
+pub fn assert_finite(g: Gaussian, what: &str) {
+    assert!(
+        g.mu().is_finite(),
+        "{what}: mu is not finite (mu={}, sigma={})",
+        g.mu(),
+        g.sigma()
+    );
+
+    assert!(
+        g.sigma().is_finite() && g.sigma() > 0.0,
+        "{what}: sigma must be finite and positive (mu={}, sigma={})",
+        g.mu(),
+        g.sigma()
+    );
+}
+
+/// Every point on every learning curve must be finite.
+pub fn assert_curve_finite(curve: &[(i64, Gaussian)], who: &str) {
+    for (time, g) in curve {
+        assert_finite(*g, &format!("{who} at t={time}"));
+    }
+}

tests/degenerate_inputs.rs

@@ -3,6 +3,9 @@
 //! These run in both debug and release: the defects they pin were all
 //! guarded only by `debug_assert!`, so a debug-only suite never saw them.
 
+mod common;
+
+use common::assert_finite;
 use trueskill_tt::{
     ConstantDrift, ConvergenceOptions, Game, GameOptions, Gaussian, History, InferenceError,
     NullObserver, Outcome, Rating,
@@ -18,15 +21,6 @@ fn rating() -> R {
     )
 }
 
-fn assert_finite(g: Gaussian, what: &str) {
-    assert!(
-        g.mu().is_finite() && g.sigma().is_finite(),
-        "{what} must be finite, got mu={} sigma={}",
-        g.mu(),
-        g.sigma()
-    );
-}
-
 #[test]
 fn record_draw_without_draw_probability_is_rejected() {
     let mut h = History::default();
@@ -141,6 +135,54 @@ fn converge_on_an_empty_history_with_owned_keys() {
     assert!(report.converged);
 }
 
+/// A weights/team length mismatch used to be a `debug_assert!`, so release
+/// builds ingested the event with the weights silently unapplied. This file's
+/// CI job runs in release too, which is the point of pinning it here.
+#[test]
+fn event_builder_rejects_a_weights_length_mismatch() {
+    let mut h = History::default();
+
+    let err = h
+        .event(1)
+        .team(["a"])
+        .weights([1.0, 2.0])
+        .team(["b"])
+        .winner(0)
+        .commit()
+        .unwrap_err();
+
+    assert!(
+        matches!(
+            err,
+            InferenceError::MismatchedShape {
+                kind: "weights",
+                expected: 1,
+                got: 2,
+            }
+        ),
+        "expected a weights MismatchedShape, got {err:?}"
+    );
+}
+
+/// The mismatch must not be applied even partially — a half-weighted team
+/// reaching the history would be worse than the error.
+#[test]
+fn event_builder_weights_mismatch_leaves_the_history_untouched() {
+    let mut h = History::default();
+
+    // Two teams, so ingestion would otherwise succeed — a one-team event is
+    // rejected for an unrelated reason and would pass this vacuously.
+    let _ = h
+        .event(1)
+        .team(["a"])
+        .weights([1.0, 2.0])
+        .team(["b"])
+        .winner(0)
+        .commit();
+
+    assert!(h.learning_curve("a").is_empty());
+}
+
 #[test]
 fn empty_event_stream_then_converge() {
     let mut h = History::default();
@@ -270,3 +312,112 @@ fn empty_history_has_no_filtered_estimates() {
 
     assert!(history.filtered_learning_curve("nobody").is_empty());
 }
+
+// --- Boundary inputs (#26) ----------------------------------------------
+
+fn tight() -> ConvergenceOptions {
+    ConvergenceOptions {
+        max_iter: 2_000,
+        epsilon: 1e-12,
+        ..ConvergenceOptions::default()
+    }
+}
+
+fn assert_curve_finite(h: &History, keys: &[&str], what: &str) {
+    for key in keys {
+        for (time, g) in h.learning_curve(*key) {
+            assert!(
+                g.mu().is_finite() && g.sigma().is_finite(),
+                "{what}: non-finite posterior for {key} at t={time} (mu={} sigma={})",
+                g.mu(),
+                g.sigma()
+            );
+        }
+    }
+}
+
+/// A zero weight reaches `(m - performance.exclude(..)) * (1.0 / w)`, i.e. a
+/// division by zero. The commit is accepted today, so this pins that the
+/// resulting posterior is still finite rather than quietly NaN.
+#[test]
+fn zero_weight_does_not_produce_a_non_finite_posterior() {
+    let mut h = History::builder().build();
+
+    h.event(1)
+        .team(["a"])
+        .weights([0.0])
+        .team(["b"])
+        .winner(0)
+        .commit()
+        .expect("a zero weight is accepted today; update this test if that changes");
+
+    h.converge().unwrap();
+
+    assert_curve_finite(&h, &["a", "b"], "zero weight");
+}
+
+#[test]
+fn negative_weight_does_not_produce_a_non_finite_posterior() {
+    let mut h = History::builder().build();
+
+    h.event(1)
+        .team(["a"])
+        .weights([-1.0])
+        .team(["b"])
+        .winner(0)
+        .commit()
+        .expect("a negative weight is accepted today; update this test if that changes");
+
+    h.converge().unwrap();
+
+    assert_curve_finite(&h, &["a", "b"], "negative weight");
+}
+
+/// Events supplied newest-first must land in the same slices as oldest-first:
+/// ingestion sorts by time rather than trusting arrival order.
+#[test]
+fn out_of_order_timestamps_converge_to_the_same_answer() {
+    fn build(descending: bool) -> History {
+        let mut h = History::builder().convergence(tight()).build();
+
+        let mut times: Vec<i64> = (1..=6).collect();
+        if descending {
+            times.reverse();
+        }
+
+        for time in times {
+            h.record_winner(&"a", &"b", time).unwrap();
+        }
+
+        h.converge().unwrap();
+        h
+    }
+
+    let ascending = build(false);
+    let descending = build(true);
+
+    let one = ascending.current_skill("a").unwrap();
+    let other = descending.current_skill("a").unwrap();
+
+    assert!(
+        (one.mu() - other.mu()).abs() < 1e-8 && (one.sigma() - other.sigma()).abs() < 1e-8,
+        "arrival order changed the answer: ascending mu={} sigma={}, descending mu={} sigma={}",
+        one.mu(),
+        one.sigma(),
+        other.mu(),
+        other.sigma()
+    );
+}
+
+#[test]
+fn extreme_beta_and_sigma_stay_finite() {
+    for (beta, sigma) in [(1e-6, 1e-6), (1e6, 1e6), (1e-6, 1e6), (1e6, 1e-6)] {
+        let mut h = History::builder().beta(beta).sigma(sigma).build();
+
+        h.record_winner(&"a", &"b", 1).unwrap();
+        h.record_winner(&"a", &"b", 2).unwrap();
+        h.converge().unwrap();
+
+        assert_curve_finite(&h, &["a", "b"], &format!("beta={beta} sigma={sigma}"));
+    }
+}

tests/drift_scale.rs

@@ -0,0 +1,402 @@
+//! Per-competitor drift scaling via `Member::with_drift_scale`.
+//!
+//! The scale multiplies the *variance* the history's `Drift` contributes for
+//! that competitor, so `scale` is in the same units as `gamma`:
+//! `ConstantDrift(g)` at `scale = s` behaves as `ConstantDrift(g * s)` would.
+//! `scale = 0.0` pins a competitor still — an anchor, a rating floor, a course
+//! difficulty — while everyone around them keeps drifting.
+
+use smallvec::smallvec;
+use trueskill_tt::{
+    ConstantDrift, ConvergenceOptions, Event, Gaussian, History, InferenceError, Member,
+    NullObserver, Outcome, Team,
+};
+
+type Fit = History<i64, ConstantDrift, NullObserver, &'static str>;
+
+const CONVERGENCE: ConvergenceOptions = ConvergenceOptions {
+    max_iter: 64,
+    epsilon: 1e-9,
+    alpha: 1.0,
+};
+
+/// Two events separated by a long gap, so drift has room to matter.
+fn distant_pair(anchor_scale: Option<f64>) -> Vec<Event<i64, &'static str>> {
+    let anchor = |s: Option<f64>| match s {
+        Some(scale) => Member::new("anchor").with_drift_scale(scale),
+        None => Member::new("anchor"),
+    };
+
+    vec![
+        Event {
+            time: 0,
+            teams: smallvec![
+                Team::with_members([anchor(anchor_scale)]),
+                Team::with_members([Member::new("player")]),
+            ],
+            outcome: Outcome::winner(0, 2),
+        },
+        Event {
+            time: 1000,
+            teams: smallvec![
+                Team::with_members([anchor(anchor_scale)]),
+                Team::with_members([Member::new("player")]),
+            ],
+            outcome: Outcome::winner(1, 2),
+        },
+    ]
+}
+
+fn fit(events: Vec<Event<i64, &'static str>>, gamma: f64) -> Fit {
+    let mut h = History::builder()
+        .mu(25.0)
+        .sigma(25.0 / 3.0)
+        .beta(25.0 / 6.0)
+        .p_draw(0.0)
+        .drift(ConstantDrift(gamma))
+        .convergence(CONVERGENCE)
+        .build();
+
+    h.add_events(events).unwrap();
+    h.converge().unwrap();
+    h
+}
+
+fn curve(h: &Fit, key: &str) -> Vec<(i64, Gaussian)> {
+    let mut c = h.learning_curves().remove(key).expect("key in curves");
+    c.sort_by_key(|(t, _)| *t);
+    c
+}
+
+/// A competitor at `scale = 0.0` is one latent skill observed twice, so the
+/// posterior is the same distribution at both times — and strictly tighter
+/// than the same competitor left to drift.
+#[test]
+fn zero_scale_pins_a_competitor_still() {
+    let pinned = fit(distant_pair(Some(0.0)), 25.0 / 300.0);
+    let drifting = fit(distant_pair(None), 25.0 / 300.0);
+
+    let pinned_curve = curve(&pinned, "anchor");
+    assert_eq!(pinned_curve.len(), 2);
+
+    let (t0, first) = pinned_curve[0];
+    let (t1, second) = pinned_curve[1];
+    assert_eq!((t0, t1), (0, 1000));
+
+    assert!(
+        (first.sigma() - second.sigma()).abs() < 1e-9,
+        "a pinned competitor's uncertainty must not move between t=0 and t=1000: \
+         {} vs {}",
+        first.sigma(),
+        second.sigma()
+    );
+    assert!(
+        (first.mu() - second.mu()).abs() < 1e-9,
+        "a pinned competitor's mean must not move: {} vs {}",
+        first.mu(),
+        second.mu()
+    );
+
+    let drifting_curve = curve(&drifting, "anchor");
+    assert!(
+        drifting_curve[0].1.sigma() > first.sigma() + 1e-6,
+        "drift must leave the anchor less certain than pinning does: {} vs {}",
+        drifting_curve[0].1.sigma(),
+        first.sigma()
+    );
+}
+
+/// The scale is composable with `gamma`: scaling every competitor by `s` is
+/// exactly the same fit as scaling the history's drift by `s`.
+#[test]
+fn scale_is_equivalent_to_scaling_gamma() {
+    let scaled: Vec<Event<i64, &'static str>> = vec![
+        Event {
+            time: 0,
+            teams: smallvec![
+                Team::with_members([Member::new("a").with_drift_scale(0.5)]),
+                Team::with_members([Member::new("b").with_drift_scale(0.5)]),
+            ],
+            outcome: Outcome::winner(0, 2),
+        },
+        Event {
+            time: 400,
+            teams: smallvec![
+                Team::with_members([Member::new("b").with_drift_scale(0.5)]),
+                Team::with_members([Member::new("a").with_drift_scale(0.5)]),
+            ],
+            outcome: Outcome::winner(0, 2),
+        },
+    ];
+
+    let plain: Vec<Event<i64, &'static str>> = vec![
+        Event {
+            time: 0,
+            teams: smallvec![
+                Team::with_members([Member::new("a")]),
+                Team::with_members([Member::new("b")]),
+            ],
+            outcome: Outcome::winner(0, 2),
+        },
+        Event {
+            time: 400,
+            teams: smallvec![
+                Team::with_members([Member::new("b")]),
+                Team::with_members([Member::new("a")]),
+            ],
+            outcome: Outcome::winner(0, 2),
+        },
+    ];
+
+    let by_scale = fit(scaled, 0.3);
+    let by_gamma = fit(plain, 0.15);
+
+    for key in ["a", "b"] {
+        let lhs = curve(&by_scale, key);
+        let rhs = curve(&by_gamma, key);
+        assert_eq!(lhs.len(), rhs.len());
+
+        for ((t_l, g_l), (t_r, g_r)) in lhs.iter().zip(rhs.iter()) {
+            assert_eq!(t_l, t_r);
+            assert!(
+                (g_l.mu() - g_r.mu()).abs() < 1e-9 && (g_l.sigma() - g_r.sigma()).abs() < 1e-9,
+                "ConstantDrift(0.3) at scale 0.5 must equal ConstantDrift(0.15) for {key} at \
+                 t={t_l}: ({}, {}) vs ({}, {})",
+                g_l.mu(),
+                g_l.sigma(),
+                g_r.mu(),
+                g_r.sigma()
+            );
+        }
+    }
+}
+
+/// `None` means 1.0: an explicit unit scale changes nothing.
+#[test]
+fn unset_scale_matches_an_explicit_unit_scale() {
+    let implicit = fit(distant_pair(None), 25.0 / 300.0);
+    let explicit = fit(distant_pair(Some(1.0)), 25.0 / 300.0);
+
+    for key in ["anchor", "player"] {
+        let lhs = curve(&implicit, key);
+        let rhs = curve(&explicit, key);
+        assert_eq!(lhs.len(), rhs.len());
+
+        for ((t_l, g_l), (t_r, g_r)) in lhs.iter().zip(rhs.iter()) {
+            assert_eq!(t_l, t_r);
+            assert_eq!(
+                (g_l.mu(), g_l.sigma()),
+                (g_r.mu(), g_r.sigma()),
+                "an explicit scale of 1.0 must be bit-identical to leaving it unset, \
+                 for {key} at t={t_l}"
+            );
+        }
+    }
+}
+
+/// The use case from the issue: a static difficulty alongside drifting players,
+/// in one graph. The anchor must hold still without absorbing drift through its
+/// neighbours, and everything must stay finite.
+#[test]
+fn mixed_static_and_drifting_graph_converges() {
+    let mut events: Vec<Event<i64, &'static str>> = Vec::new();
+    let players = ["p0", "p1", "p2"];
+
+    for (i, p) in players.iter().cycle().take(9).enumerate() {
+        events.push(Event {
+            time: (i as i64) * 100,
+            teams: smallvec![
+                Team::with_members([Member::new(*p)]),
+                Team::with_members([Member::new("layout").with_drift_scale(0.0)]),
+            ],
+            outcome: Outcome::winner((i % 2) as u32, 2),
+        });
+    }
+
+    let mut h = History::builder()
+        .mu(25.0)
+        .sigma(25.0 / 3.0)
+        .beta(25.0 / 6.0)
+        .p_draw(0.0)
+        .drift(ConstantDrift(25.0 / 300.0))
+        .convergence(CONVERGENCE)
+        .build();
+
+    h.add_events(events).unwrap();
+    let report = h.converge().unwrap();
+    assert!(report.converged, "mixed graph must converge: {report:?}");
+
+    let curves = h.learning_curves();
+    for (key, points) in &curves {
+        for (t, g) in points {
+            assert!(
+                g.mu().is_finite() && g.sigma().is_finite() && g.sigma() > 0.0,
+                "{key} at t={t} is not a usable posterior: mu={}, sigma={}",
+                g.mu(),
+                g.sigma()
+            );
+        }
+    }
+
+    let layout = curve(&h, "layout");
+    assert_eq!(layout.len(), 9);
+    let (_, first) = layout[0];
+    for (t, g) in &layout {
+        assert!(
+            (g.sigma() - first.sigma()).abs() < 1e-9,
+            "a static layout must not accumulate uncertainty; t={t} has sigma {} vs {}",
+            g.sigma(),
+            first.sigma()
+        );
+    }
+
+    let p0 = curve(&h, "p0");
+    assert!(
+        p0.last().unwrap().1.sigma() > 0.0,
+        "a drifting player should still have a proper posterior"
+    );
+}
+
+fn reject(scale: f64) -> InferenceError {
+    let mut h = History::builder()
+        .drift(ConstantDrift(25.0 / 300.0))
+        .build();
+
+    let events: Vec<Event<i64, &'static str>> = vec![Event {
+        time: 0,
+        teams: smallvec![
+            Team::with_members([Member::new("a").with_drift_scale(scale)]),
+            Team::with_members([Member::new("b")]),
+        ],
+        outcome: Outcome::winner(0, 2),
+    }];
+
+    h.add_events(events)
+        .expect_err("an out-of-range drift_scale must be rejected")
+}
+
+#[test]
+fn negative_scale_is_rejected() {
+    assert_eq!(
+        reject(-1.0),
+        InferenceError::InvalidParameter {
+            name: "drift_scale",
+            value: -1.0
+        }
+    );
+}
+
+#[test]
+fn non_finite_scale_is_rejected() {
+    for scale in [f64::NAN, f64::INFINITY, f64::NEG_INFINITY] {
+        assert!(
+            matches!(
+                reject(scale),
+                InferenceError::InvalidParameter {
+                    name: "drift_scale",
+                    ..
+                }
+            ),
+            "a drift_scale of {scale} must be rejected as an invalid parameter"
+        );
+    }
+}
+
+/// The scale must reach the filtering pass too, not just `converge()`.
+/// `filtered_learning_curves` runs its own drift application, so a pinned
+/// competitor has to stay pinned there as well.
+#[test]
+fn zero_scale_pins_a_competitor_in_the_filtered_pass() {
+    let pinned = fit(distant_pair(Some(0.0)), 25.0 / 300.0);
+    let drifting = fit(distant_pair(None), 25.0 / 300.0);
+
+    let filtered = |h: &Fit| -> Vec<(i64, Gaussian)> {
+        let mut c = h
+            .filtered_learning_curves()
+            .remove("anchor")
+            .expect("anchor in filtered curves");
+        c.sort_by_key(|(t, _)| *t);
+        c
+    };
+
+    let pinned_curve = filtered(&pinned);
+    let drifting_curve = filtered(&drifting);
+    assert_eq!(pinned_curve.len(), 2);
+    assert_eq!(drifting_curve.len(), 2);
+
+    assert!(
+        pinned_curve[1].1.sigma() < pinned_curve[0].1.sigma(),
+        "a pinned competitor's filtered uncertainty must shrink with a second \
+         observation, not be re-inflated by drift: {} then {}",
+        pinned_curve[0].1.sigma(),
+        pinned_curve[1].1.sigma()
+    );
+
+    assert!(
+        pinned_curve[1].1.sigma() < drifting_curve[1].1.sigma() - 1e-6,
+        "pinning must leave the filtered estimate tighter than drifting does: \
+         {} vs {}",
+        pinned_curve[1].1.sigma(),
+        drifting_curve[1].1.sigma()
+    );
+}
+
+/// `drift_scale` is competitor configuration captured at first appearance, the
+/// same as `prior` — a later `with_drift_scale` on a key the history already
+/// knows is ignored. This guards that decision rather than driving it: the
+/// behaviour falls out of where the capture happens, and the point of the test
+/// is that moving the capture would be a visible break, not a silent one.
+#[test]
+fn drift_scale_is_ignored_after_first_appearance() {
+    let mut late = History::builder()
+        .mu(25.0)
+        .sigma(25.0 / 3.0)
+        .beta(25.0 / 6.0)
+        .p_draw(0.0)
+        .drift(ConstantDrift(25.0 / 300.0))
+        .convergence(CONVERGENCE)
+        .build();
+
+    // First batch creates "anchor" with the default scale.
+    late.add_events(vec![Event {
+        time: 0,
+        teams: smallvec![
+            Team::with_members([Member::new("anchor")]),
+            Team::with_members([Member::new("player")]),
+        ],
+        outcome: Outcome::winner(0, 2),
+    }])
+    .unwrap();
+
+    // Second batch asks for a pin. Too late: the competitor already exists.
+    late.add_events(vec![Event {
+        time: 1000,
+        teams: smallvec![
+            Team::with_members([Member::new("anchor").with_drift_scale(0.0)]),
+            Team::with_members([Member::new("player")]),
+        ],
+        outcome: Outcome::winner(1, 2),
+    }])
+    .unwrap();
+    late.converge().unwrap();
+
+    let ignored = curve(&late, "anchor");
+    let drifting = curve(&fit(distant_pair(None), 25.0 / 300.0), "anchor");
+
+    for ((t_l, g_l), (t_r, g_r)) in ignored.iter().zip(drifting.iter()) {
+        assert_eq!(t_l, t_r);
+        assert!(
+            (g_l.sigma() - g_r.sigma()).abs() < 1e-9,
+            "a scale set after first appearance must be ignored, leaving the fit \
+             identical to one that never set it: t={t_l}, {} vs {}",
+            g_l.sigma(),
+            g_r.sigma()
+        );
+    }
+
+    let pinned = curve(&fit(distant_pair(Some(0.0)), 25.0 / 300.0), "anchor");
+    assert!(
+        (ignored[1].1.sigma() - pinned[1].1.sigma()).abs() > 1e-6,
+        "sanity: the pinned fit must actually differ, or the assertion above is vacuous"
+    );
+}

tests/properties.rs

@@ -0,0 +1,167 @@
+//! Property-based tests over generated histories.
+//!
+//! The golden suite pins exact values against the Python/Julia reference on a
+//! handful of fixtures. These pin *invariants* over inputs nobody wrote by
+//! hand, which is where the defects this crate has actually shipped were
+//! hiding: a linear evidence product that underflowed only past ~1000 teams,
+//! and a batching path no golden exercised because every golden ingests in one
+//! call.
+
+mod common;
+
+use common::assert_finite;
+use proptest::prelude::*;
+use smallvec::smallvec;
+use trueskill_tt::{ConvergenceOptions, Event, History, Member, Outcome, Team};
+
+/// Distinct competitors, so no event pits someone against themselves.
+fn pairs() -> impl Strategy<Value = Vec<(usize, usize)>> {
+    prop::collection::vec((0usize..8, 0usize..8), 1..24)
+        .prop_map(|v| v.into_iter().filter(|(a, b)| a != b).collect::<Vec<_>>())
+        .prop_filter("needs at least one valid pair", |v| !v.is_empty())
+}
+
+const KEYS: [&str; 8] = ["a", "b", "c", "d", "e", "f", "g", "h"];
+
+fn history_from(games: &[(usize, usize)]) -> History {
+    let mut h = History::builder()
+        .convergence(ConvergenceOptions {
+            max_iter: 200,
+            epsilon: 1e-10,
+            ..ConvergenceOptions::default()
+        })
+        .build();
+
+    let events: Vec<Event<i64, &'static str>> = games
+        .iter()
+        .enumerate()
+        .map(|(i, &(a, b))| Event {
+            time: i as i64 + 1,
+            teams: smallvec![
+                Team::with_members([Member::new(KEYS[a])]),
+                Team::with_members([Member::new(KEYS[b])]),
+            ],
+            outcome: Outcome::winner(0, 2),
+        })
+        .collect();
+
+    h.add_events(events).unwrap();
+
+    h
+}
+
+proptest! {
+    #![proptest_config(ProptestConfig::with_cases(48))]
+
+    /// Whatever the schedule of games, convergence must not produce NaN or an
+    /// improper posterior. `converge` returns `NonFiniteResult` rather than
+    /// silently reporting a NaN step as converged, so a break shows up here as
+    /// either an Err or a non-finite curve point.
+    #[test]
+    fn converged_posteriors_are_always_finite(games in pairs()) {
+        let mut h = history_from(&games);
+
+        h.converge().unwrap();
+
+        for key in KEYS {
+            for (time, g) in h.learning_curve(key) {
+                assert_finite(g, &format!("{key} at t={time}"));
+            }
+        }
+    }
+
+    /// Log-evidence is a log probability: finite, and never above zero.
+    ///
+    /// The linear-product implementation this replaced underflowed to zero on
+    /// long chains, making `ln(0)` = -inf — finite-ness is the property that
+    /// would have caught it.
+    #[test]
+    fn log_evidence_is_a_finite_log_probability(games in pairs()) {
+        let mut h = history_from(&games);
+
+        h.converge().unwrap();
+
+        let batch = h.log_evidence();
+        let filtered = h.filtered_log_evidence();
+
+        prop_assert!(batch.is_finite(), "batch log-evidence {batch} is not finite");
+        prop_assert!(batch <= 0.0, "batch log-evidence {batch} exceeds zero");
+        prop_assert!(filtered.is_finite(), "filtered log-evidence {filtered} is not finite");
+        prop_assert!(filtered <= 0.0, "filtered log-evidence {filtered} exceeds zero");
+    }
+
+    /// Filtered estimates must not depend on whether `converge` has run — the
+    /// property the whole forward-only design rests on.
+    #[test]
+    fn filtered_evidence_is_invariant_to_convergence(games in pairs()) {
+        let mut h = history_from(&games);
+
+        let before = h.filtered_log_evidence();
+
+        h.converge().unwrap();
+
+        let after = h.filtered_log_evidence();
+
+        prop_assert!(
+            (before - after).abs() < 1e-8,
+            "filtered evidence moved across converge(): {before} -> {after}"
+        );
+    }
+
+    /// Ingesting the same games one at a time must reach the same fixed point
+    /// as ingesting them in one call.
+    #[test]
+    fn ingestion_order_does_not_change_the_answer(games in pairs()) {
+        let batched = {
+            let mut h = history_from(&games);
+            h.converge().unwrap();
+            h
+        };
+
+        let incremental = {
+            let mut h = History::builder()
+                .convergence(ConvergenceOptions {
+                    max_iter: 200,
+                    epsilon: 1e-10,
+                    ..ConvergenceOptions::default()
+                })
+                .build();
+
+            for (i, &(a, b)) in games.iter().enumerate() {
+                h.add_events([Event {
+                    time: i as i64 + 1,
+                    teams: smallvec![
+                        Team::with_members([Member::new(KEYS[a])]),
+                        Team::with_members([Member::new(KEYS[b])]),
+                    ],
+                    outcome: Outcome::winner(0, 2),
+                }])
+                .unwrap();
+            }
+
+            h.converge().unwrap();
+            h
+        };
+
+        for key in KEYS {
+            let one = batched.current_skill(key);
+            let other = incremental.current_skill(key);
+
+            match (one, other) {
+                (Some(one), Some(other)) => {
+                    prop_assert!(
+                        (one.mu() - other.mu()).abs() < 1e-6
+                            && (one.sigma() - other.sigma()).abs() < 1e-6,
+                        "{key}: batched mu={} sigma={}, incremental mu={} sigma={}",
+                        one.mu(),
+                        one.sigma(),
+                        other.mu(),
+                        other.sigma()
+                    );
+                }
+                (None, None) => {}
+                _ => prop_assert!(false, "{key} present in only one history"),
+            }
+        }
+    }
+}