feat: added a Drift trait and a "default" ConstantDrift implementation

README.md

@@ -11,6 +11,66 @@ Rust port of [TrueSkillThroughTime.py](https://github.com/glandfried/TrueSkillTh
 - [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/)
 
+## Drift
+
+Skill drift models how a player's true skill can change between appearances. Each time a player reappears 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:
+
+```rust
+pub trait Drift: Copy + Debug {
+    fn variance_delta(&self, elapsed: i64) -> f64;
+}
+```
+
+`variance_delta` returns the amount to add to `σ²` given the elapsed time since the player last played. Internally, `Gaussian::forget` uses this to compute the new sigma: `σ_new = sqrt(σ² + variance_delta)`.
+
+### ConstantDrift
+
+The built-in `ConstantDrift` implements a linear random walk — skill uncertainty grows proportionally to time:
+
+```
+variance_delta = elapsed * γ²
+```
+
+This is the standard TrueSkill Through Time model. Use it by passing a `ConstantDrift(gamma)` when constructing a `Player`:
+
+```rust
+use trueskill_tt::{Player, Gaussian, drift::ConstantDrift};
+
+// gamma = 0.1 means skill can shift ~0.1 per time unit
+let player = Player::new(Gaussian::from_ms(0.0, 6.0), 1.0, ConstantDrift(0.1));
+```
+
+### Custom drift
+
+Implement `Drift` to express any other model. For example, a drift that saturates after a long absence (uncertainty grows with the square root of elapsed time instead of linearly):
+
+```rust
+use trueskill_tt::drift::Drift;
+
+#[derive(Clone, Copy, Debug)]
+struct SqrtDrift {
+    gamma: f64,
+}
+
+impl Drift for SqrtDrift {
+    fn variance_delta(&self, elapsed: i64) -> f64 {
+        (elapsed as f64).sqrt() * self.gamma * self.gamma
+    }
+}
+
+let player = Player::new(Gaussian::from_ms(0.0, 6.0), 1.0, SqrtDrift { gamma: 0.5 });
+```
+
+To use a custom drift type with `History`, use the `.drift()` builder method instead of `.gamma()`:
+
+```rust
+let h = History::builder()
+    .drift(SqrtDrift { gamma: 0.5 })
+    .build();
+```
+
 ## Todo
 
 - [x] Implement approx for Gaussian