trueskill-tt/README.md

# TrueSkill - Through Time

Bayesian skill rating over a time axis.

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 being frozen
guesses and comparisons across eras become meaningful.

A Rust port of
[TrueSkillThroughTime.py](https://github.com/glandfried/TrueSkillThroughTime.py).

## Install

```toml
[dependencies]
trueskill-tt = "0.8"
```

Optional features, both off by default:

- `approx` — `approx`'s equality traits for `Gaussian`. Useful in tests.
- `rayon` — parallelises the within-slice sweep and the per-slice passes of
  `learning_curves` / `log_evidence`. Results stay bit-identical regardless of
  worker count; `just determinism` asserts it at 1, 2, 4 and 8 threads.

## Quickstart

Record results, converge, then read off skills.

```rust
use trueskill_tt::History;

let mut history = History::default();

history.record_winner(&"alice", &"bob", 1)?;
history.record_winner(&"bob", &"carol", 2)?;
history.record_winner(&"alice", &"carol", 3)?;

history.converge()?;

let alice = history.current_skill("alice").unwrap();
assert!(alice.mu() > 0.0, "alice won every game she played");
# Ok::<(), trueskill_tt::InferenceError>(())
```

The third argument is the time. It is what makes this Through Time rather than
plain TrueSkill: skill is inferred at each of those moments, not once at the
end. `learning_curve` reads the whole trajectory back.

```rust
# use trueskill_tt::History;
# let mut history = History::default();
# history.record_winner(&"alice", &"bob", 1)?;
# history.record_winner(&"bob", &"carol", 2)?;
# history.record_winner(&"alice", &"carol", 3)?;
# history.converge()?;
// `None` means the key is unknown; `Some(vec![])` means known but unplayed.
let curve = history.learning_curve("alice").unwrap();
for (time, skill) in &curve {
    println!("t={time}: {:.2} ± {:.2}", skill.mu(), skill.sigma());
}

// Everyone's latest posterior in one pass — the leaderboard query.
let latest = history.current_skills();
assert_eq!(latest.len(), 3);
# Ok::<(), trueskill_tt::InferenceError>(())
```

## Teams, rankings and draws

Anything beyond one-versus-one goes through the fluent event builder. An event
is only recorded by the terminal `.commit()`.

```rust
use trueskill_tt::History;

let mut history = History::builder().p_draw(0.1).build();

history
    .event(1)
    .team(["alice", "bob"])
    .team(["carol", "dave"])
    .ranking([0, 1])   // lower is better; equal values are a tie
    .commit()?;

history.converge()?;
# Ok::<(), trueskill_tt::InferenceError>(())
```

**A tie needs a positive `p_draw`.** A `p_draw` of zero asserts draws cannot
happen, so a tied result has no representable likelihood and is rejected rather
than fitted to something else:

```rust
use trueskill_tt::{History, InferenceError};

let mut history = History::default(); // p_draw defaults to 0.0
let err = history.record_draw(&"alice", &"bob", 1).unwrap_err();
assert!(matches!(err, InferenceError::TieWithoutDrawProbability { .. }));
```

This also catches `Outcome::winner(w, n)` for three or more teams, which ties
every loser.

## Which entry point?

| You want to | Use |
|---|---|
| One match, two competitors | `record_winner` / `record_draw` |
| Teams, explicit ranks, scores, per-member weights | `history.event(t)…commit()` |
| A batch you already have as values | `add_events(iter)` |
| Score a hypothetical with no history at all | `Game` |

`Game` is the odd one out and worth being explicit about: it is a single match's
factor graph, it does not participate in a `History`, and nothing it computes is
remembered. Reach for it to evaluate a matchup in isolation; reach for `History`
for everything that accumulates.

## `converge` is strict

`converge` returns `Err(NotConverged)` if the sweep hits `max_iter` with the
step still above `epsilon`, and `Err(NonFiniteStep)` if a sweep produces NaN.

It used to return `Ok` with `converged: false`, which was the worst available
shape. A fit that stops short is *wrong by a little*: every posterior is finite,
the ordering looks sensible, and nothing about the output says the numbers were
still moving. Detection was opt-in, and `let _ = h.converge()` silently opted
out — which is how a real defect hid in this crate's own test suite.

The default `max_iter` is high enough that reaching it means something is
genuinely wrong rather than that the history is large; the loop exits at
`epsilon` long before, so raising the cap costs nothing when it is not needed.
Use `converge_partial` when a deliberately capped, unconverged fit is the point.

Predictions are strict for the same reason: every `predict_*` method reads
skills through one gate that refuses a NaN-poisoned fit, rather than returning a
plausible number computed from it.

## Drift

Skill drift models how a competitor's true skill can change between appearances.
Each time they reappear after a gap, their skill uncertainty is widened by the
drift model before the new evidence is incorporated.

Drift is represented by the `Drift` trait (`src/drift.rs`), generic over the
history's time type:

```text
pub trait Drift<T: Time>: Copy + Debug + Send + Sync {
    fn variance_delta(&self, from: &T, to: &T) -> f64;
    fn variance_for_elapsed(&self, elapsed: i64) -> f64;
}
```

Both methods return the amount to add to `σ²`, not to `σ`. `variance_delta`
works from two timestamps; `variance_for_elapsed` takes an already-computed
elapsed count, and is used on the paths that cache it. `Gaussian::forget`
applies the result entirely in variance space — `from_mv(mu, variance() +
variance_delta)` — taking no square root.

That block is a quotation rather than a doctest. The custom-drift example below
is compiled by CI, so it is what actually pins the signature.

### ConstantDrift

The built-in `ConstantDrift` implements a linear random walk — skill uncertainty
grows proportionally to time:

```text
variance_delta = elapsed * γ²
```

This is the standard TrueSkill Through Time model. Pass a `ConstantDrift::new(gamma)`
when constructing a `Rating`:

```rust
use trueskill_tt::{ConstantDrift, Gaussian, Rating};

// gamma = 0.1 means skill can shift ~0.1 per time unit.
let rating: Rating<i64, ConstantDrift> =
    Rating::new(Gaussian::from_ms(0.0, 6.0), 1.0, ConstantDrift::new(0.1));

assert_eq!(rating.drift().gamma(), 0.1);
```

The type annotation is load-bearing: `ConstantDrift` implements `Drift<T>` for
every `T: Time`, so without it `T` is ambiguous.

### Custom drift

Implement `Drift<T>` to express any other model. For example, a drift that
saturates after a long absence, with uncertainty growing as the square root of
elapsed time instead of linearly:

```rust
use trueskill_tt::{Drift, Gaussian, History, Rating, Time};

#[derive(Clone, Copy, Debug)]
struct SqrtDrift {
    gamma: f64,
}

impl<T: Time> Drift<T> for SqrtDrift {
    fn variance_delta(&self, from: &T, to: &T) -> f64 {
        let elapsed = from.elapsed_to(to).max(0) as f64;
        elapsed.sqrt() * self.gamma * self.gamma
    }

    fn variance_for_elapsed(&self, elapsed: i64) -> f64 {
        (elapsed.max(0) as f64).sqrt() * self.gamma * self.gamma
    }
}

// On a single Rating:
let rating: Rating<i64, SqrtDrift> =
    Rating::new(Gaussian::from_ms(0.0, 6.0), 1.0, SqrtDrift { gamma: 0.5 });

// Or for a whole History, via the builder:
let history = History::builder().drift(SqrtDrift { gamma: 0.5 }).build();

assert_eq!(rating.beta(), 1.0);
assert_eq!(history.log_evidence(), 0.0);
```

`HistoryBuilder::drift` is the only way to set a history's drift model; there is
no `gamma()` shorthand. The default is `ConstantDrift::new(GAMMA)`.

### 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::new(g)` at
scale `s` behaves exactly as `ConstantDrift::new(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
use trueskill_tt::{ConstantDrift, Event, History, Member, Outcome, Team};

let mut h = History::builder().drift(ConstantDrift::new(0.1)).build();

h.add_events(vec![Event {
    time: 0,
    teams: [
        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)]),
    ]
    .into_iter()
    .collect(),
    outcome: Outcome::winner(0, 2),
}])
.unwrap();

h.converge().unwrap();
```

Like `with_prior`, the scale is **competitor configuration, not a per-event
value**: it applies to the competitor for the whole history, and it applies
whenever it is supplied — including on a key the history already knows.
Configuring one late still refits the whole history rather than taking effect
only from that event onward, because `converge` refits from competitor state.
Repeating the same value is inert; supplying two *different* values for one
competitor within a single batch is `InferenceError::ConflictingCompetitorConfig`,
since events in a batch have no order. The scale must be finite and
non-negative; ingestion otherwise fails with `InferenceError::InvalidParameter`.

The fluent `EventBuilder` reaches this too: `.team([...])` is the common case
and leaves both unset, while `.members([...])` takes `Member` values directly,
so `h.event(t).members([Member::new("layout_7").with_drift_scale(0.0)])` is
equivalent to the typed shape above.

## Scored outcomes

Use `Outcome::scores([...])` when you have continuous per-team scores rather
than just ranks. Adjacent score margins flow into a `MarginFactor` that adds
soft Gaussian evidence about the latent performance diff. Configure
`HistoryBuilder::score_sigma(σ)` to control how much you trust the margins
(smaller σ = more trust).

```rust
use trueskill_tt::History;

let mut h = History::builder().score_sigma(2.0).build();
h.event(1)
    .team(["alice"])
    .team(["bob"])
    .scores([21.0, 9.0])
    .commit()
    .unwrap();
h.converge().unwrap();
```

## Prediction

`predict_outcome` gives the full distribution over finishing orders. Each entry
is a rank vector in the same shape `Outcome::ranking` takes — equal ranks mean a
tie — so an outcome feeds straight back into inference.

```rust
use trueskill_tt::History;

let mut h = History::builder().p_draw(0.1).build();
h.record_winner(&"alice", &"bob", 1).unwrap();
h.converge().unwrap();

let p = h.predict_outcome(&[&[&"alice"], &[&"bob"]]).unwrap();

// Probabilities are exhaustive and disjoint, so they sum to one.
assert!((p.total() - 1.0).abs() < 1e-6);

let (best, likelihood) = p.most_likely().unwrap();
println!("most likely: {best:?} at {likelihood:.3}");
println!("draw:        {:.3}", p.probability_of(&[0, 0]));
```

Supports any number of teams. Because the outcome space grows factorially, the
full distribution is capped at `MAX_PREDICTED_TEAMS`; two cheaper entry points
stay available at any size:

- `predict_win_probabilities(teams)` — `P(team i finishes strictly first)`,
  quadratic in team count.
- `predict_ranking(teams, ranks)` — one specific finishing order.

Unknown keys are an error by default, not a silent omission: a team the history
has never seen cannot produce a confident-looking probability. The error names
the key, and every key must already be known — pre-filter with
`current_skill` if your caller cannot guarantee that.

If predicting for competitors you have never seen is the point rather than a
mistake, say so once:

```rust
use trueskill_tt::{History, UnknownKeys};

let h = History::builder().unknown_keys(UnknownKeys::Prior).build();
```

An unknown competitor is then answered from the configured prior, which is the
honest reading — you have no evidence about them — and correctly *widens* a team
that contains one. There is deliberately no "skip the member" mode: a team's
performance is the sum of its members, so dropping one would make the model more
certain because it knows less.

### Asking about one competitor

`Gaussian` answers tail questions directly, which is what a stopping rule needs:

```rust
use trueskill_tt::History;

let mut h = History::default();
h.record_winner(&"alice", &"bob", 1).unwrap();
h.converge().unwrap();

let skill = h.current_skill("alice").unwrap();

// "How sure am I that this is below the cutoff?" — a probability, not a
// `mu + z * sigma` band whose confidence drifts as sigma changes.
let _ = skill.probability_below(20.0);

// Use this rather than `1.0 - probability_below(x)`: the complement cancels
// away every digit in the upper tail, which is where a stopping rule lives.
let _ = skill.probability_above(30.0);
```

## Which match to play next

`quality()` measures whether a matchup is *fair*. That is not the same as
whether it is *informative*, and the two only coincide for two evenly matched
competitors. When each observation costs something, ask
`expected_information_gain` instead — the outcome-weighted divergence between
what you believe now and what you would believe afterwards.

```rust
use trueskill_tt::History;

let mut h = History::default();
for t in 1..=10 {
    h.record_winner(&"veteran", &"regular", t).unwrap();
    h.record_winner(&"regular", &"veteran", t + 100).unwrap();
}
h.record_winner(&"veteran", &"newcomer", 500).unwrap();
h.converge().unwrap();

let settled = h.expected_information_gain(&[&[&"veteran"], &[&"regular"]]).unwrap();
let unknown = h.expected_information_gain(&[&[&"veteran"], &[&"newcomer"]]).unwrap();

// Playing the newcomer teaches you more than replaying a settled rivalry.
assert!(unknown > settled);
```

The result is in nats, and is bounded by the entropy of the outcome: at most
`ln 2 ≈ 0.693` for a two-way result, `ln 3` once draws are possible, `ln k` for
`k` outcomes. A value near zero means you already know how it ends.

This costs one full inference pass **per possible outcome**, so it is far more
expensive than `quality()`. Scoring every pairing among `n` competitors is
`O(n² × outcomes)` passes — shortlist with `quality()` or
`predict_win_probabilities` first, then score only the shortlist.

## Other implementations

- [ttt-scala](https://github.com/ankurdave/ttt-scala)
- [ChessAnalysis #F](https://github.com/lucasmaystre/ChessAnalysis)
- [TrueSkillThroughTime.jl](https://github.com/glandfried/TrueSkillThroughTime.jl)
- [TrueSkillThroughTime.R](https://github.com/glandfried/TrueSkillThroughTime.R)
- [TrueSkill Through Time: Revisiting the History of Chess](https://www.microsoft.com/en-us/research/wp-content/uploads/2008/01/NIPS2007_0931.pdf)
- [TrueSkill Through Time. The full scientific documentation](https://glandfried.github.io/publication/landfried2021-learning/)

## Status

Every box on the old todo list is ticked, so it has been retired; open work
lives in the issue tracker instead. The crate is in use and the API is still
moving — breaking changes are batched into minor releases rather than dribbled
out. `CHANGELOG.md` lists them and [`MIGRATING.md`](MIGRATING.md) explains what
to do about them.

## License

Licensed under either of

- Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or
  <http://www.apache.org/licenses/LICENSE-2.0>)
- MIT license ([LICENSE-MIT](LICENSE-MIT) or
  <http://opensource.org/licenses/MIT>)

at your option.

### Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted
for inclusion in the work by you, as defined in the Apache-2.0 license, shall be
dual licensed as above, without any additional terms or conditions.