How AI Football Predictions Work

Football predictions have come a long way from tipsters working off gut feeling. Modern AI systems process thousands of data points before a match kicks off — and the results speak for themselves.

The data inputs

Our model ingests several layers of data for each fixture:

• Recent form — last 5–10 results for each team, weighted by recency
• Head-to-head record — historical results between the two sides, especially at the same venue
• Home / away performance — many teams perform dramatically differently at home vs away
• Goals scored and conceded — average goals per game in both contexts
• League position and season context — a mid-table team in December plays differently than a relegation battler in May

The Poisson model

At the core is a Poisson distribution model — a statistical approach that's been used by professional sports analysts for decades. The idea is simple: if we know how many goals a team typically scores and concedes, we can model the probability distribution of every possible scoreline.

From that distribution, we extract probabilities for:

• Home win / Draw / Away win (1X2)
• Over or Under 2.5 goals
• Both Teams to Score (BTTS)

Confidence scores

The confidence score shown on each tip is the model's probability estimate for that outcome. A tip showing 78% means the model assigns a 78% chance to that result based on available data.

We recommend only betting on tips with 65%+ confidence — our historical accuracy for that tier is 74.5%.

Limitations

No model is perfect. Late team news (injuries, suspensions), weather, and referee decisions can't be captured in pre-match data. Always use predictions as one input into your betting decisions, not the sole basis.