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Half-time scoregrid fitting using univariate Poisson
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@ekoutanov
ekoutanov committed Dec 10, 2023
1 parent 777a92a commit c3e9772
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Showing 11 changed files with 591 additions and 119 deletions.
3 changes: 2 additions & 1 deletion brumby-soccer/benches/cri_interval.rs
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
use criterion::{criterion_group, criterion_main, Criterion};

use brumby_soccer::interval;
use brumby_soccer::interval::{IntervalConfig, PruneThresholds, BivariateProbs, TeamProbs};
use brumby_soccer::interval::{IntervalConfig, PruneThresholds, BivariateProbs, TeamProbs, UnivariateProbs};

fn criterion_benchmark(c: &mut Criterion) {
fn run(intervals: u8, max_total_goals: u16) -> usize {
Expand All @@ -11,6 +11,7 @@ fn criterion_benchmark(c: &mut Criterion) {
team_probs: TeamProbs {
h1_goals: BivariateProbs { home: 0.25, away: 0.25, common: 0.25 },
h2_goals: BivariateProbs { home: 0.25, away: 0.25, common: 0.25 },
assists: UnivariateProbs { home: 1.0, away: 1.0 },
},
player_probs: vec![],
prune_thresholds: PruneThresholds {
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3 changes: 2 additions & 1 deletion brumby-soccer/benches/cri_isolate.rs
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
use brumby_soccer::domain::{OfferType, OutcomeType, Player, Side};
use criterion::{criterion_group, criterion_main, Criterion};

use brumby_soccer::interval::{explore, Exploration, IntervalConfig, BivariateProbs, PruneThresholds, PlayerProbs, TeamProbs};
use brumby_soccer::interval::{explore, Exploration, IntervalConfig, BivariateProbs, PruneThresholds, PlayerProbs, TeamProbs, UnivariateProbs};
use brumby_soccer::interval::query::isolate;

fn criterion_benchmark(c: &mut Criterion) {
Expand All @@ -13,6 +13,7 @@ fn criterion_benchmark(c: &mut Criterion) {
team_probs: TeamProbs {
h1_goals: BivariateProbs { home: 0.25, away: 0.25, common: 0.25 },
h2_goals: BivariateProbs { home: 0.25, away: 0.25, common: 0.25 },
assists: UnivariateProbs { home: 1.0, away: 1.0 },
},
player_probs: vec![(player, PlayerProbs { goal: Some(0.25), assist: None })],
prune_thresholds: PruneThresholds {
Expand Down
81 changes: 54 additions & 27 deletions brumby-soccer/src/bin/soc_prices.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -13,14 +13,12 @@ use brumby::hash_lookup::HashLookup;
use brumby::market::{Market, Overround, OverroundMethod, PriceBounds};
use brumby::probs::SliceExt;
use brumby_soccer::data::{download_by_id, ContestSummary, SoccerFeedId};
use brumby_soccer::domain::{
FittingErrors, Offer, OfferType, OutcomeType, Over, Period, Player, Score, Side,
};
use brumby_soccer::fit::ErrorType;
use brumby_soccer::domain::{FittingErrors, Offer, OfferType, OutcomeType, Over, Period, Score};
use brumby_soccer::fit::{away_booksum, home_booksum, ErrorType};
use brumby_soccer::interval::query::isolate;
use brumby_soccer::interval::{
explore, BivariateProbs, Expansions, Exploration, IntervalConfig, PlayerProbs, PruneThresholds,
TeamProbs,
TeamProbs, UnivariateProbs,
};
use brumby_soccer::{fit, print};

Expand Down Expand Up @@ -82,11 +80,11 @@ async fn main() -> Result<(), Box<dyn Error>> {
info!("offered {offer_type:?}");
}

let ft_correct_score_prices =
contest.offerings[&OfferType::CorrectScore(Period::FullTime)].clone();
let ft_h2h_prices = contest.offerings[&OfferType::HeadToHead(Period::FullTime)].clone();
let ft_goals_ou_prices =
contest.offerings[&OfferType::TotalGoals(Period::FullTime, Over(2))].clone();
let ft_correct_score_prices =
contest.offerings[&OfferType::CorrectScore(Period::FullTime)].clone();
let h1_h2h_prices = contest.offerings[&OfferType::HeadToHead(Period::FirstHalf)].clone();
let h1_goals_ou_prices =
contest.offerings[&OfferType::TotalGoals(Period::FirstHalf, Over(2))].clone();
Expand All @@ -98,11 +96,13 @@ async fn main() -> Result<(), Box<dyn Error>> {
let anytime_assist = contest.offerings[&OfferType::AnytimeAssist].clone();

let ft_h2h = fit_offer(OfferType::HeadToHead(Period::FullTime), &ft_h2h_prices, 1.0);
// println!("ft_h2h: {ft_h2h:?}");
let ft_goals_ou = fit_offer(
OfferType::TotalGoals(Period::FullTime, Over(2)),
&ft_goals_ou_prices,
1.0,
);
// println!("ft_goals_ou: {ft_goals_ou:?}");
let ft_correct_score = fit_offer(
OfferType::CorrectScore(Period::FullTime),
&ft_correct_score_prices,
Expand Down Expand Up @@ -137,13 +137,21 @@ async fn main() -> Result<(), Box<dyn Error>> {

let mut adj_optimal_h1 = [0.0; 3];
let mut adj_optimal_h2 = [0.0; 3];
for (i, orig_h1) in h1_search_outcome.optimal_values.iter().enumerate() {
// only adjust the home and away scoring probs; common prob is locked to the full-time one
for (i, orig_h1) in h1_search_outcome.optimal_values.iter().take(2).enumerate() {
let orig_h2 = h2_search_outcome.optimal_values[i];
let ft = ft_search_outcome.optimal_values[i];
let avg_h1_h2 = (orig_h1 + orig_h2) / 2.0;
adj_optimal_h1[i] = orig_h1 / (avg_h1_h2 / ft);
adj_optimal_h2[i] = orig_h2 / (avg_h1_h2 / ft);
if avg_h1_h2 > 0.0 {
adj_optimal_h1[i] = orig_h1 / (avg_h1_h2 / ft);
adj_optimal_h2[i] = orig_h2 / (avg_h1_h2 / ft);
} else {
adj_optimal_h1[i] = ft;
adj_optimal_h2[i] = ft;
}
}
adj_optimal_h1[2] = ft_search_outcome.optimal_values[2];
adj_optimal_h2[2] = ft_search_outcome.optimal_values[2];
println!("adjusted optimal_h1={adj_optimal_h1:?}, optimal_h2={adj_optimal_h2:?}");
// let adj_optimal_h1 = h1_search_outcome.optimal_values;
// let adj_optimal_h2 = h2_search_outcome.optimal_values;
Expand Down Expand Up @@ -386,19 +394,6 @@ async fn main() -> Result<(), Box<dyn Error>> {
FIRST_GOALSCORER_BOOKSUM,
);
let anytime_gs = fit_offer(OfferType::AnytimeGoalscorer, &anytime_gs, 1.0);
let home_goalscorer_booksum = first_gs
.market
.probs
.iter()
.zip(first_gs.outcomes.items().iter())
.filter(|(_, outcome_type)| {
matches!(
outcome_type,
OutcomeType::Player(Player::Named(Side::Home, _))
)
})
.map(|(prob, _)| prob)
.sum::<f64>();

// println!("scoregrid:\n{}sum: {}", scoregrid.verbose(), scoregrid.flatten().sum());
let draw_prob = isolate(
Expand Down Expand Up @@ -462,6 +457,10 @@ async fn main() -> Result<(), Box<dyn Error>> {
team_probs: TeamProbs {
h1_goals: BivariateProbs::from(adj_optimal_h1.as_slice()),
h2_goals: BivariateProbs::from(adj_optimal_h2.as_slice()),
assists: UnivariateProbs {
home: 1.0,
away: 1.0,
},
},
player_probs: vec![(
player.clone(),
Expand Down Expand Up @@ -497,7 +496,8 @@ async fn main() -> Result<(), Box<dyn Error>> {

// fitted_first_goalscorer_probs.push(draw_prob);
// fitted_first_goalscorer_probs.normalise(FIRST_GOALSCORER_BOOKSUM);
fitted_first_goalscorer_probs.push(FIRST_GOALSCORER_BOOKSUM - fitted_first_goalscorer_probs.sum());
fitted_first_goalscorer_probs
.push(FIRST_GOALSCORER_BOOKSUM - fitted_first_goalscorer_probs.sum());

let fitted_first_goalscorer = Offer {
offer_type: OfferType::FirstGoalscorer,
Expand Down Expand Up @@ -536,6 +536,10 @@ async fn main() -> Result<(), Box<dyn Error>> {
team_probs: TeamProbs {
h1_goals: BivariateProbs::from(adj_optimal_h1.as_slice()),
h2_goals: BivariateProbs::from(adj_optimal_h2.as_slice()),
assists: UnivariateProbs {
home: 1.0,
away: 1.0,
},
},
player_probs: vec![(
player.clone(),
Expand Down Expand Up @@ -605,17 +609,32 @@ async fn main() -> Result<(), Box<dyn Error>> {
.map(|price| 1.0 / price)
.sum::<f64>();

let per_outcome_overround = (anytime_goalscorer_overround.value - 1.0) / anytime_gs.outcomes.len() as f64;
let per_outcome_overround =
(anytime_goalscorer_overround.value - 1.0) / anytime_gs.outcomes.len() as f64;

let anytime_assist = fit_offer(
OfferType::AnytimeAssist,
&anytime_assist,
sample_anytime_assist_booksum / (1.0 + per_outcome_overround * anytime_assist.len() as f64)
sample_anytime_assist_booksum / (1.0 + per_outcome_overround * anytime_assist.len() as f64),
);

let home_goalscorer_booksum = home_booksum(&fitted_anytime_goalscorer);
let away_goalscorer_booksum = away_booksum(&fitted_anytime_goalscorer);
println!("partial goalscorer booksums: home: {home_goalscorer_booksum:.3}, away: {away_goalscorer_booksum:.3}");

let home_assister_booksum = home_booksum(&anytime_assist);
let away_assister_booksum = away_booksum(&anytime_assist);
println!("partial assister booksums: home: {home_assister_booksum:.3}, away: {away_assister_booksum:.3}");
let assist_probs = UnivariateProbs {
home: home_assister_booksum / home_goalscorer_booksum,
away: away_assister_booksum / away_goalscorer_booksum,
};
println!("assist_probs: {assist_probs:?}");

let fitted_assist_probs = fit::fit_anytime_assist_all(
&BivariateProbs::from(adj_optimal_h1.as_slice()),
&BivariateProbs::from(adj_optimal_h2.as_slice()),
&assist_probs,
&anytime_assist,
draw_prob,
anytime_assist.market.fair_booksum(),
Expand All @@ -631,6 +650,7 @@ async fn main() -> Result<(), Box<dyn Error>> {
team_probs: TeamProbs {
h1_goals: BivariateProbs::from(adj_optimal_h1.as_slice()),
h2_goals: BivariateProbs::from(adj_optimal_h2.as_slice()),
assists: assist_probs.clone(),
},
player_probs: vec![(
player.clone(),
Expand Down Expand Up @@ -981,7 +1001,14 @@ fn explore_scores(h1_goals: BivariateProbs, h2_goals: BivariateProbs) -> Explora
explore(
&IntervalConfig {
intervals: INTERVALS as u8,
team_probs: TeamProbs { h1_goals, h2_goals },
team_probs: TeamProbs {
h1_goals,
h2_goals,
assists: UnivariateProbs {
home: 1.0,
away: 1.0,
},
},
player_probs: vec![],
prune_thresholds: PruneThresholds {
max_total_goals: MAX_TOTAL_GOALS_FULL,
Expand Down
6 changes: 3 additions & 3 deletions brumby-soccer/src/data.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -69,9 +69,9 @@ impl From<ContestModel> for ContestSummary {
offerings.insert(
OfferType::FirstGoalscorer,
HashMap::from_iter(players.into_iter().map(|player| {
if player.side.is_none() {
println!("PLAYER {player:?}");
}
// if player.side.is_none() {
// println!("PLAYER {player:?}");
// }
let OutcomeOdds(outcome_type, odds) = OutcomeOdds::from(player);
(outcome_type, odds)
})),
Expand Down
7 changes: 7 additions & 0 deletions brumby-soccer/src/domain.rs
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
use std::iter::{Filter, Zip};
use std::slice::Iter;
use brumby::hash_lookup::HashLookup;
use brumby::market::Market;

Expand Down Expand Up @@ -77,6 +79,11 @@ pub struct Offer {
pub outcomes: HashLookup<OutcomeType>,
pub market: Market,
}
impl Offer {
pub fn filter_outcomes_with_probs<F>(&self, filter: F) -> Filter<Zip<Iter<OutcomeType>, Iter<f64>>, F> where F: FnMut(&(&OutcomeType, &f64)) -> bool{
self.outcomes.items().iter().zip(self.market.probs.iter()).filter(filter)
}
}

#[derive(Debug)]
pub struct FittingErrors {
Expand Down
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