Every prediction runs through three layers that are combined into a final result.

Data collection Last 10 matches each team + H2H history fetched from the API

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Weighted team profiles Decay weighting (0.9ⁿ) — recent games count more

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Poisson model Expected goals → 6×6 score matrix → Win / Draw / Loss %

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Rule-based markets Goals, corners, BTTS, and combo-market angles with confidence scores

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ML overlay (optional) Random Forest trained on real historical matches

What data is collected

Last 10 home team matches from the API
Last 10 away team matches
Last 10 head-to-head meetings between the two teams

Data providers (35+ leagues)

Provider	Leagues	Key required
ESPN (unofficial)	11 leagues — PL, La Liga, Bundesliga, Serie A, Ligue 1, MLS, Eredivisie, Primeira Liga, Super Lig, Champions League, Europa League	No
OpenLigaDB	3 German leagues — Bundesliga, 2. Bundesliga, 3. Liga	No
football-data.org	Top European leagues — PL, Bundesliga, Serie A, La Liga, Ligue 1, Eredivisie, Primeira Liga, Brasileirão, CL, EL	Free tier
API-Football.com	Championship, Ligue 2 + extras	Yes
TheSportsDB	MLS, Brasileirão, and others	Free tier

Exponential decay weighting

Matches are sorted oldest → newest. Each gets a weight of 0.9ⁿ, so the most recent game counts the most:

weights = [0.9⁴, 0.9³, 0.9², 0.9¹, 0.9⁰] = [0.656, 0.729, 0.810, 0.900, 1.000]

A goal last week is worth ~52% more than a goal from 5 games ago — the model reacts faster to streaks.

avgGoals = Σ(weight × goals) ÷ Σ(weights)

Daily Picks & Weekly Picks

The Today's Games landing page and Daily/Weekly Picks features automatically scan all 35+ leagues, predict every fixture, and surface the highest-confidence bets across all markets (goals, BTTS, corners, outcome). No manual league selection needed.

Step 1 — Expected goals (λ)

Using a Dixon-Coles / Maher inspired formula:

λ_home = homeAttack × awayDefence × 1.1
λ_away = awayAttack × homeDefence

The 1.1 multiplier captures home advantage (+10%). If venue-specific stats are available it drops to 1.03 since the advantage is already in the split averages.

Step 2 — Poisson formula

Probability of scoring exactly k goals given expected rate λ:

P(X = k) = e^−λ × λ^k / k!

Example — λ = 1.8 expected goals:

P(0 goals) = e⁻¹·⁸ × 1.8⁰ / 0! = 16.5% P(1 goal) = e⁻¹·⁸ × 1.8¹ / 1! = 29.8% P(2 goals) = e⁻¹·⁸ × 1.8² / 2! = 26.8% P(3 goals) = e⁻¹·⁸ × 1.8³ / 3! = 16.1%

Step 3 — Score matrix (6×6)

Home and away goals are treated as independent, so every scoreline probability is:

P(2–1) = P(home = 2) × P(away = 1)

This creates a 6×6 grid. The cell with the highest value = most likely score.

Step 4 — Sum the matrix

Home Win = Σ P(h,a) where h > a Draw = Σ P(h,a) where h = a (diagonal) Away Win = Σ P(h,a) where h < a

Over / Under 2.5 goals

Under 2.5 = P(total ≤ 2) = P(0) + P(1) + P(2)
Over 2.5 = 1 − Under 2.5

Both Teams to Score (BTTS)

BTTS Yes = P(home ≥ 1) × P(away ≥ 1)
= (1 − P(home=0)) × (1 − P(away=0))

Corners & Combo Markets

Market	Rule	Confidence
Corners O/U 9.5	Over if combined avg > 9.5	min(│diff│ / 4, 1)
Goals O/U 2.5	Over if combined avg > 2.5	min(│diff│ / 1.5, 1)
GG & Over 2.5	Summed directly from scoreline probabilities where both teams score and total goals ≥ 3	Derived from the corrected score matrix
1/2 & Over 2.5	Summed from scorelines that land on a win and also clear the total-goals line	Derived from the corrected score matrix

Confidence = how far from the line. Combined avg 4.5 → confidence 1.0. Combined avg 2.6 → confidence 0.07.

Overall confidence level

< 5 matches for either team → LOW 5 – 8 matches → MEDIUM > 8 matches for both teams → HIGH

An optional Python ML microservice runs alongside the Poisson engine and gives a second opinion on each market.

What it predicts

Over / Under 2.5 goals
Both Teams to Score (BTTS)
Corners Over / Under 9.5

How it's trained

Features fed into the model:

home_avg_goals_for / against away_avg_goals_for / against home_avg_corners away_avg_corners home_form_rating (0–1) away_form_rating (0–1) home_win_rate away_win_rate h2h_avg_total_goals h2h_played poisson_over25_prob poisson_btts_prob

Training data is built from real historical matches (PL, Bundesliga, Serie A, La Liga, Ligue 1 — 4 seasons). The rolling window ensures only pre-match information is used, so there is no data leakage.

Algorithm

Random Forest (200 trees, depth 8) — one classifier per target market. Evaluated with 5-fold cross-validation AUC. XGBoost is also available via the /train endpoint.

To enable

# Terminal 1 — start the ML service cd ml && uvicorn app:app --port 8000 # Terminal 2 — build real training data (first time only) node scripts/build-training-data.js # .env — enable ML predictions ML_SERVICE_URL=http://localhost:8000

Today's Board

Football picks, without the noise.

Pick style

Performance Snapshot

Tracking settled ticket legs from recent days.

Best Leagues

Confidence Bands

All Games

Every game on the board, sorted for fast scanning.

Scanning today's fixtures across all leagues…

First scan takes 1–3 minutes, then cached and refreshed every 5 hours.

0 / 0

Market Predictions Poisson-backed probabilities

⚽

Total Goals Over / Under 2.5

Over 2.5

Under 2.5

🎯

Both Teams Score BTTS

Yes

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Corners Over / Under 9.5

Over 9.5

Under 9.5

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Half-Time Goals Avg HT goals expected

Home HT

Away HT

HT Total

Safe Bets All markets at a glance

Double Chance

1X —

X2 —

12 —

Total Goals

O 0.5

—

O 1.5

—

O 2.5

—

Team Goals

Home

0.5+

—

1.5+

—

Away

0.5+

—

1.5+

—

Risky Takes Higher-odds combo angles from the score model

GG & Over 2.5 —

Best when both attacks look live and the total-goals environment is open.

1 & Over 2.5 —

Leans into a home edge while still asking for a proper goal count.

2 & Over 2.5 —

Useful when the away side can carry the match but the model still sees goals.

1 & GG —

A home-win angle for matches where both teams are still expected to score.

2 & GG —

The away-team version when the visitors can win but still concede one.

Over 3.5 Goals —

The pure high-variance total for fixtures with strong shootout potential.

Match Outcome Poisson model

Most Likely Score

xG vs xG

Top 3 Correct Scores

Home Win

Draw

Away Win

Team Stats Comparison

Stat (per game)	Home	Away

Recent Form (last 5)

Head to Head

Date	Home	Score	Away	Result

🗂 Last 10 Matches

Date	H/A	Opponent	Score	Result	Corners	Shots

Date	H/A	Opponent	Score	Result	Corners	Shots

Predictions

🏀

Total Points

Over / Under

🏆

Winner

Match Winner

Statistical Model

Expected Points

Home

—

Away

Total Expected

Win Probability

Home Win

Away Win

Recent Form (last 5)

Head to Head

Date	Home	Pts	Away	Result

Football picks, without the noise.

Performance Snapshot

Best Leagues

Confidence Bands

Risky Combo Radar 🔥

All Games

How It Works

Premier League

League Table

⚽ Top Scorers

📊 Bookmaker Odds

Market Predictions Poisson-backed probabilities

Safe Bets All markets at a glance

Risky Takes Higher-odds combo angles from the score model

Match Outcome Poisson model

Team Stats Comparison

Recent Form (last 5)

Head to Head

🚑 Injuries & Suspensions

Strength Matchup What the model is using

🗂 Last 10 Matches

📊 Bookmaker Odds

Predictions

Statistical Model

Recent Form (last 5)

Head to Head

🎟 Today Tickets