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//! The Ingo algorithm, the predecessor of DWZ and one of the first rating algorithms invented in 1947.
//! Sometimes still used in Xiangqi ("Chinese Chess").
//!
//! Unlike with the other rating systems, with Ingo a lower rating is more desirable,
//! and negative values are possible, though unlikely.
//! A player with an Ingo rating of 0 has an equivalent Elo rating of 2840, and an Ingo rating of -1 equals 2848 Elo.
//!
//! # Quickstart
//!
//! This is the most basic example on how to use the Ingo Module.
//! Please take a look at the functions below to see more advanced use cases.
//!
//! ```
//! use skillratings::{
//! ingo::{ingo, IngoRating},
//! Outcomes,
//! };
//!
//! // Initialise a new player rating.
//! // We need to set the actual age for the player,
//! // if you are unsure what to set here, choose something that is greater than 25.
//! let player_one = IngoRating::new(19);
//!
//! // Or you can initialise it with your own values of course.
//! // Imagine these numbers being pulled from a database.
//! let (some_rating, some_age) = (150.4, 23);
//! let player_two = IngoRating {
//! rating: some_rating,
//! age: some_age,
//! };
//!
//! // The outcome of the match is from the perspective of player one.
//! let outcome = Outcomes::WIN;
//!
//! // The ingo function will calculate the new ratings for both players and return them.
//! let (new_player_one, new_player_two) = ingo(&player_one, &player_two, &outcome);
//! ```
//!
//! # More Information
//!
//! - [Wikipedia Article (German, no english version available)](https://de.wikipedia.org/wiki/Ingo-Zahl)
//! - [Archive of Ingo Ratings (German)](https://www.schachbund.de/ingo-spiegel.html)
//! - [Ingo Rules (German, PDF Download)](https://www.schachbund.de/ingo-spiegel.html?file=files/dsb/historie/ingo-spiegel/Ingo-Regeln.pdf&cid=28120)
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
use crate::{elo::EloRating, Outcomes};
#[derive(Copy, Clone, Debug, PartialEq)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
/// The Ingo rating of a player.
///
/// Note that unlike in the other systems, a lower score is better than a higher score.
/// Negative values are possible.
///
/// The age is the actual age of the player, if unsure or unavailable set this to `>25`.
/// Converting from an `EloRating` or using `IngoRating::default()` will set the age to 26.
///
/// The default rating is 230.0.
pub struct IngoRating {
/// The rating value for a player, by default 230.0.
/// Note that a lower rating is more desirable.
pub rating: f64,
/// The age of the player, if uncertain or unavailable set this to `>25`.
pub age: usize,
}
impl IngoRating {
#[must_use]
/// Initialise a new `IngoRating` with a rating of 230.0 and the given age.
/// The age is the actual age of the player, if unsure or unavailable set this to `>25`.
pub const fn new(age: usize) -> Self {
Self { rating: 230.0, age }
}
}
impl Default for IngoRating {
fn default() -> Self {
Self::new(26)
}
}
impl From<(f64, usize)> for IngoRating {
fn from((r, a): (f64, usize)) -> Self {
Self { rating: r, age: a }
}
}
// Just in case the age is unknown.
impl From<f64> for IngoRating {
fn from(r: f64) -> Self {
Self { rating: r, age: 26 }
}
}
impl From<EloRating> for IngoRating {
fn from(e: EloRating) -> Self {
Self {
rating: 355.0 - (e.rating / 8.0),
..Default::default()
}
}
}
#[must_use]
/// Calculates the [`IngoRating`]s of two players based on their ratings, and the outcome of the game.
///
/// Takes in two players as [`IngoRating`]s, and an [`Outcome`](Outcomes).
///
/// Instead of the traditional way of calculating the Ingo for only one player only using a list of results,
/// we are calculating the Ingo rating for two players at once, like in the Elo calculation,
/// to make it easier to see instant results.
///
/// The outcome of the match is in the perspective of `player_one`.
/// This means [`Outcomes::WIN`] is a win for `player_one` and [`Outcomes::LOSS`] is a win for `player_two`.
///
/// A lower Ingo rating is more desirable, and while negative values are possible,
/// a player with an Ingo rating of 0 has an equivalent Elo rating of 2840.
///
/// # Examples
/// ```
/// use skillratings::{
/// ingo::{ingo, IngoRating},
/// Outcomes,
/// };
///
/// let player_one = IngoRating {
/// rating: 130.0,
/// age: 40,
/// };
/// let player_two = IngoRating {
/// rating: 160.0,
/// age: 40,
/// };
///
/// let (new_one, new_two) = ingo(&player_one, &player_two, &Outcomes::WIN);
///
/// assert!((new_one.rating.round() - 129.0).abs() < f64::EPSILON);
/// assert!((new_two.rating.round() - 161.0).abs() < f64::EPSILON);
/// ```
pub fn ingo(
player_one: &IngoRating,
player_two: &IngoRating,
outcome: &Outcomes,
) -> (IngoRating, IngoRating) {
let outcome1 = outcome.to_chess_points();
let outcome2 = 1.0 - outcome1;
let perf1 = performance(player_two.rating, outcome1);
let perf2 = performance(player_one.rating, outcome2);
let development1 = age_to_devcoefficent(player_one.age);
let development2 = age_to_devcoefficent(player_two.age);
let new_rating1 = perf1.mul_add(1.0, player_one.rating * development1) / (1.0 + development1);
let new_rating2 = perf2.mul_add(1.0, player_two.rating * development2) / (1.0 + development2);
(
IngoRating {
rating: new_rating1,
age: player_one.age,
},
IngoRating {
rating: new_rating2,
age: player_two.age,
},
)
}
#[must_use]
/// The "traditional" way of calculating a [`IngoRating`] of a player in a rating period.
///
/// Takes in a player as an [`IngoRating`] and their results as a Slice of tuples containing the opponent as an [`IngoRating`],
/// and the outcome of the game as an [`Outcome`](Outcomes)
///
/// The outcome of the match is in the perspective of the player.
/// This means [`Outcomes::WIN`] is a win for the player and [`Outcomes::LOSS`] is a win for the opponent.
///
/// # Examples
/// ```
/// use skillratings::{
/// ingo::{ingo_rating_period, IngoRating},
/// Outcomes,
/// };
///
/// let player = IngoRating {
/// rating: 130.0,
/// age: 40,
/// };
///
/// let opponent1 = IngoRating {
/// rating: 160.0,
/// age: 40,
/// };
///
/// let opponent2 = IngoRating {
/// rating: 160.0,
/// age: 40,
/// };
///
/// let opponent3 = IngoRating {
/// rating: 55.0,
/// age: 40,
/// };
///
/// let opponent4 = IngoRating {
/// rating: 90.0,
/// age: 40,
/// };
///
/// let results = vec![
/// (opponent1, Outcomes::WIN),
/// (opponent2, Outcomes::DRAW),
/// (opponent3, Outcomes::WIN),
/// (opponent4, Outcomes::LOSS),
/// ];
///
/// let new_player = ingo_rating_period(&player, &results);
///
/// assert!((new_player.rating.round() - 126.0).abs() < f64::EPSILON);
/// ```
pub fn ingo_rating_period(player: &IngoRating, results: &[(IngoRating, Outcomes)]) -> IngoRating {
// Ingo was meant to be used in tournaments, so we do not need to loop over the opponents here.
let development = age_to_devcoefficent(player.age);
let average_points =
results.iter().map(|r| r.1.to_chess_points()).sum::<f64>() / results.len() as f64;
let average_opponent_rating =
results.iter().map(|r| r.0.rating).sum::<f64>() / results.len() as f64;
let performance = performance(average_opponent_rating, average_points);
let new_rating = performance.mul_add(results.len() as f64, player.rating * development)
/ (results.len() as f64 + development);
IngoRating {
rating: new_rating,
age: player.age,
}
}
#[must_use]
/// Calculates the expected outcome of two players based on Ingo.
///
/// Takes in two players as [`IngoRating`]s and returns the probability of victory for each player as an [`f64`] between 1.0 and 0.0.
/// 1.0 means a certain victory for the player, 0.0 means certain loss.
/// Values near 0.5 mean a draw is likely to occur.
///
/// # Examples
/// ```
/// use skillratings::{
/// ingo::{expected_score, IngoRating},
/// Outcomes,
/// };
///
/// let player_one = IngoRating {
/// rating: 130.0,
/// age: 40,
/// };
/// let player_two = IngoRating {
/// rating: 160.0,
/// age: 40,
/// };
///
/// let (exp1, exp2) = expected_score(&player_one, &player_two);
///
/// assert!(((exp1 * 100.0).round() - 80.0).abs() < f64::EPSILON);
/// assert!(((exp2 * 100.0).round() - 20.0).abs() < f64::EPSILON);
///
/// assert!((exp1 + exp2 - 1.0).abs() < f64::EPSILON);
/// ```
pub fn expected_score(player_one: &IngoRating, player_two: &IngoRating) -> (f64, f64) {
let exp_one = 0.5 + (player_two.rating - player_one.rating) / 100.0;
let exp_two = 1.0 - exp_one;
(exp_one, exp_two)
}
fn performance(average_rating: f64, score: f64) -> f64 {
average_rating - (100.0 * score - 50.0)
}
/// Similar to the DWZ algorithm, we use the age of the player to get the development coefficient.
const fn age_to_devcoefficent(age: usize) -> f64 {
match age {
0..=20 => 10.0,
21..=25 => 15.0,
_ => 20.0,
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_ingo() {
let player_one = IngoRating {
rating: 130.0,
age: 40,
};
let player_two = IngoRating {
rating: 160.0,
age: 40,
};
let (p1, p2) = ingo(&player_one, &player_two, &Outcomes::WIN);
assert!((p1.rating.round() - 129.0).abs() < f64::EPSILON);
assert!((p2.rating.round() - 161.0).abs() < f64::EPSILON);
let (p1, p2) = ingo(&player_one, &player_two, &Outcomes::LOSS);
assert!((p1.rating.round() - 134.0).abs() < f64::EPSILON);
assert!((p2.rating.round() - 156.0).abs() < f64::EPSILON);
let young_player = IngoRating {
rating: 230.0,
age: 7,
};
let old_player = IngoRating {
rating: 109.0,
age: 78,
};
let (yp, op) = ingo(&young_player, &old_player, &Outcomes::DRAW);
assert!((yp.rating.round() - 219.0).abs() < f64::EPSILON);
assert!((op.rating.round() - 115.0).abs() < f64::EPSILON);
}
#[test]
fn test_ingo_rating_period() {
let player_one = IngoRating {
rating: 130.0,
age: 22,
};
let player_two = IngoRating {
rating: 160.0,
age: 40,
};
let results = vec![(player_two, Outcomes::WIN)];
let p1 = ingo_rating_period(&player_one, &results);
assert!((p1.rating.round() - 129.0).abs() < f64::EPSILON);
let player_two = IngoRating {
rating: 160.0,
age: 40,
};
let player_three = IngoRating {
rating: 160.0,
age: 40,
};
let player_four = IngoRating {
rating: 55.0,
age: 22,
};
let player_five = IngoRating {
rating: 90.0,
age: 40,
};
let results = vec![
(player_two, Outcomes::WIN),
(player_three, Outcomes::DRAW),
(player_four, Outcomes::WIN),
(player_five, Outcomes::LOSS),
];
let p1 = ingo_rating_period(&player_one, &results);
assert!((p1.rating.round() - 124.0).abs() < f64::EPSILON);
}
#[test]
fn test_expected_score() {
let player_one = IngoRating {
rating: 130.0,
age: 40,
};
let player_two = IngoRating {
rating: 160.0,
age: 40,
};
let (exp1, exp2) = expected_score(&player_one, &player_two);
assert!(((exp1 * 100.0).round() - 80.0).abs() < f64::EPSILON);
assert!(((exp2 * 100.0).round() - 20.0).abs() < f64::EPSILON);
assert!((exp1 + exp2 - 1.0).abs() < f64::EPSILON);
}
#[test]
fn ingo_elo_conv() {
let elo_player = EloRating::new();
let ingo_player = IngoRating::from(elo_player);
assert!((ingo_player.rating - 230.0).abs() < f64::EPSILON);
assert_eq!(ingo_player.age, 26);
let ingo_player = IngoRating::default();
let elo_player = EloRating::from(ingo_player);
assert!((elo_player.rating - 1000.0).abs() < f64::EPSILON);
}
#[test]
#[allow(clippy::clone_on_copy)]
fn test_misc_stuff() {
let player_one = IngoRating::new(14);
assert_eq!(player_one, player_one.clone());
assert!(!format!("{:?}", player_one).is_empty());
assert_eq!(IngoRating::from((222.0, 26)), IngoRating::from(222.0));
}
}