multi_skill/systems/
glicko.rs

1//! Glicko system details: https://en.wikipedia.org/wiki/Glicko_rating_system
2
3use super::util::{standard_logistic_cdf, Player, Rating, RatingSystem, TANH_MULTIPLIER};
4use rayon::prelude::*;
5
6#[derive(Debug)]
7pub struct Glicko {
8    pub beta: f64,
9    pub sig_drift: f64,
10}
11
12impl Default for Glicko {
13    fn default() -> Self {
14        Self {
15            beta: 400. * TANH_MULTIPLIER / std::f64::consts::LN_10,
16            sig_drift: 35.,
17        }
18    }
19}
20
21impl Glicko {
22    fn win_probability(&self, sig_perf: f64, player: &Rating, foe: &Rating) -> f64 {
23        let z = (player.mu - foe.mu) / foe.sig.hypot(sig_perf);
24        standard_logistic_cdf(z)
25    }
26}
27
28impl RatingSystem for Glicko {
29    fn round_update(&self, contest_weight: f64, mut standings: Vec<(&mut Player, usize, usize)>) {
30        let sig_perf = self.beta / contest_weight.sqrt();
31        let all_ratings: Vec<(Rating, usize, f64)> = standings
32            .par_iter_mut()
33            .map(|(player, lo, _)| {
34                player.add_noise_and_collapse(self.sig_drift);
35                let g = 1f64.hypot(player.approx_posterior.sig / sig_perf).recip();
36                (player.approx_posterior, *lo, g)
37            })
38            .collect();
39
40        let gli_q = TANH_MULTIPLIER / sig_perf;
41        standings.into_par_iter().for_each(|(player, my_lo, _)| {
42            let my_rating = &player.approx_posterior;
43            let mut info = 0.;
44            let mut update = 0.;
45            for (rating, lo, g) in &all_ratings {
46                let outcome = match my_lo.cmp(lo) {
47                    std::cmp::Ordering::Less => 1.,
48                    std::cmp::Ordering::Equal => 0.5,
49                    std::cmp::Ordering::Greater => 0.,
50                };
51                let probability = self.win_probability(sig_perf, my_rating, rating);
52                // Equivalently, let probability =
53                //  (1f64 + (gli_q * g * (rating.mu - my_rating.mu)).exp()).recip();
54
55                info += g * g * probability * (1. - probability);
56                update += g * (outcome - probability);
57            }
58            // Treat the round as one highly informative match
59            info = 0.25;
60            update /= all_ratings.len() as f64;
61
62            // Compute new rating deviation
63            info *= gli_q * gli_q;
64            let sig = (my_rating.sig.powi(-2) + info).recip().sqrt();
65
66            // Compute new rating
67            update *= gli_q * sig * sig;
68            let mu = my_rating.mu + update;
69
70            player.update_rating(Rating { mu, sig }, 0.);
71        });
72    }
73}
multi_skill/systems/glicko.rs

multi_skill/systems/
glicko.rs
