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PerspectivesGuide CompletJune 23, 202610 min de lecture

Pronostics de Score IA Football — Comment les Modeles de Poisson Predisent les Scores Exacts

Comment l'IA predit les scores exacts au football avec les modeles Dixon-Coles et distribution de Poisson. Matrices de probabilite et resultats les plus frequents.

OddsFlow

OddsFlow AI Research

OddsFlow Team

June 23, 2026
Pronostics de Score IA Football — Comment les Modeles de Poisson Predisent les Scores Exacts

AI Football Score Predictions — How Poisson Models Forecast Exact Scores

Correct score prediction is one of football's hardest challenges. Football is a low-scoring, high-variance sport — a single goal changes everything. But with the right statistical approach, specifically Dixon-Coles and Poisson distribution models, AI can calculate the probability of every possible scoreline.

Why Score Prediction Is Hard

In a Premier League season (380 matches), 60+ different scorelines appear. Even the most common score (1-0) only occurs ~14% of the time. Humans bias toward "logical" scores; AI calculates objective probabilities for ALL possibilities.

How OddsFlow AI Predicts Scores

Step 1: Dixon-Coles Model

Calculates each team's attack strength and defense strength from 5 seasons of data plus 66 player attributes. Includes home advantage and low-score correction factors.

Step 2: Poisson Distribution

Converts expected goals (xG) into probabilities for each goal count (0, 1, 2, 3, 4, 5+).

Example: Arsenal xG = 1.8:

  • P(0 goals) = 16.5%, P(1) = 29.8%, P(2) = 26.8%, P(3) = 16.1%, P(4) = 7.2%

Step 3: Score Probability Matrix

Combining both teams' goal probabilities creates a full score matrix:

Arsenal (xG 1.8) vs Man United (xG 1.1):

MU 0MU 1MU 2MU 3
ARS 05.5%6.0%3.3%1.2%
ARS 19.8%10.8%5.9%2.2%
ARS 28.9%9.8%5.4%2.0%
ARS 35.3%5.8%3.2%1.2%
Most likely scores: 1-1 (10.8%) and 2-1 (9.8%). Note even the top score is only ~11%.

Step 4: Monte Carlo Simulations

10,000 simulations account for randomness — red cards, injuries, late goals, and goal correlation effects.

Most Common Premier League Scores (2024/25)

RankScorePercentage
11-013.7%
21-111.8%
32-110.8%
42-08.2%
50-07.4%
The top 3 scores (1-0, 1-1, 2-1) cover 36.3% of all matches.

Correct Score vs Asian Handicap vs Over/Under

MarketHit RateTypical OddsBest For
Correct Score8-14%@6.00-15.00Fun bets, small stakes
Asian Handicap48-55%@1.80-2.00Consistent profit
Over/Under45-58%@1.75-2.10Total goals analysis
Best practice: Use the score matrix as a guide for AH/O/U decisions, not as a standalone correct score bet.

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*Predictions by OddsFlow's Dixon-Coles model with 10,000 Monte Carlo simulations. Score data from 2024/25 Premier League. Always bet responsibly.*

#prediksi skor#correct score#Poisson#AI predictions#prediksi bola#Dixon-Coles#probability matrix

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