108 lines
2.8 KiB
Rust
108 lines
2.8 KiB
Rust
extern crate blas_src;
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use std::cmp::Ordering;
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use std::collections::HashSet;
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use std::fs;
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use std::io::{self, BufRead};
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use kickscore as ks;
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use time::{macros::date, Date};
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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let reader = fs::File::open("data/nba.csv").map(io::BufReader::new)?;
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let mut teams = HashSet::new();
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let mut observations = Vec::new();
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let cutoff = date!(2019 - 06 - 01);
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let format = time::format_description::parse("[year]-[month]-[day]").unwrap();
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for line in reader.lines().skip(1) {
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let line = line?;
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let data = line.split(',').collect::<Vec<_>>();
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assert!(data.len() == 5);
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let t = Date::parse(data[0], &format)?;
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if t > cutoff {
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break;
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}
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teams.insert(data[1].to_string());
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teams.insert(data[2].to_string());
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if data[3].is_empty() || data[4].is_empty() {
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continue;
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}
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let t = t.midnight().assume_utc().unix_timestamp() as f64;
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let score_1: u16 = data[3].parse()?;
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let score_2: u16 = data[4].parse()?;
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match score_1.cmp(&score_2) {
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Ordering::Greater => {
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observations.push((data[1].to_string(), data[2].to_string(), t));
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}
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Ordering::Less => {
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observations.push((data[2].to_string(), data[1].to_string(), t));
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}
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_ => panic!("there shouldn't be any tie games"),
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}
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}
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let seconds_in_year = 365.25 * 24.0 * 60.0 * 60.0;
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let mut model = ks::BinaryModel::new(ks::BinaryModelObservation::Probit);
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for team in teams {
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let kernel: Vec<Box<dyn ks::Kernel>> = vec![
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Box::new(ks::kernel::Constant::new(0.03)),
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Box::new(ks::kernel::Matern32::new(0.138, 1.753 * seconds_in_year)),
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];
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model.add_item(&team, Box::new(kernel));
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}
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for (winner, loser, t) in observations {
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model.observe(&[&winner], &[&loser], t);
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}
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model.fit();
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println!("Probability that CHI beats BOS...");
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let (p_win, _) = model.probabilities(
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&["CHI"],
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&["BOS"],
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date!(1996 - 01 - 01)
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.midnight()
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.assume_utc()
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.unix_timestamp() as f64,
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);
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println!(" ... in 1996: {:.2}%", 100.0 * p_win);
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let (p_win, _) = model.probabilities(
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&["CHI"],
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&["BOS"],
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date!(2001 - 01 - 01)
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.midnight()
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.assume_utc()
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.unix_timestamp() as f64,
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);
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println!(" ... in 2001: {:.2}%", 100.0 * p_win);
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let (p_win, _) = model.probabilities(
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&["CHI"],
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&["BOS"],
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date!(2020 - 01 - 01)
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.midnight()
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.assume_utc()
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.unix_timestamp() as f64,
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);
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println!(" ... in 2020: {:.2}%", 100.0 * p_win);
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Ok(())
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}
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