Chief of Quantitative Intelligence
ALA-QUANT
Football predictions powered by mathematics. A plain-numbers tool that estimates how likely each result is, using real team form — not gut feeling, and not a "guaranteed win" gimmick.
1. Pick the league
Choose a league to load its teams.
Note: World Cup, Euros, and Champions League only have teams and standings listed during an active tournament window — outside that, the list may come back empty until the next tournament's squads are published.
Home team
Auto-fills once both teams are picked. You can still adjust it.
Auto-filled from current league standing. Leave at 1500 if unsure.
Away team
League setup
These numbers describe the league both teams play in, so the model knows what "average" looks like.
Most top European leagues sit around 1.3–1.5.
1.0 = no home advantage. 1.15–1.35 is typical.
Bookmaker's odds
Enter the odds you're seeing from a bookmaker, so the model can tell you whether it thinks the market has this match priced fairly.
Long-term strength (optional fine-tuning)
Recent form (above) can be misleading if a team had an easy run of opponents. This layer weighs in each team's overall strength score to balance that out.
Typically 60–100 points.
As a %. Most leagues sit around 24–28%.
100 = rely only on recent-form numbers above. 0 = rely only on strength scores. 60 is a reasonable middle ground.
Result: who's more likely to win
Most likely final scores
Is the model actually any good? (Backtest)
This re-runs the same Poisson + strength-score model against real matches that already happened, then checks how close its probabilities came to reality — the honest way to test a model, instead of just trusting it.
Pick a league above first.
How the numbers are worked out
1. Each team gets an "attack strength" and "defense weakness" score, based on how their goals compare to the league average.
2. Multiplying those together (with a boost for the home team) gives an expected number of goals for each side.
3. A standard statistical formula (
4. Adding up all the scores where the home team wins, draws, or loses gives you the three percentages above.
5. Comparing your numbers to the bookmaker's odds shows whether the model sees the match differently from the market — that gap is the closest thing to a real "edge," and it's usually small.
2. Multiplying those together (with a boost for the home team) gives an expected number of goals for each side.
3. A standard statistical formula (
Poisson distribution — the same method professional odds-setters use) turns those expected goals into a probability for every possible final score.4. Adding up all the scores where the home team wins, draws, or loses gives you the three percentages above.
5. Comparing your numbers to the bookmaker's odds shows whether the model sees the match differently from the market — that gap is the closest thing to a real "edge," and it's usually small.
Need a walkthrough?
New here? These two resources show exactly how to use every part of this page.