AI prediction · locked November 6, 2026 2:30 pm
Model pick 2–1
Result after 90 minutes: 1–2. The model tipped 2–1 and scored 0 points.
Most likely scores
Why
The model expects 1. FC Köln to score 2.09 goals: league home average 2.12 × 1. FC Köln home attack 0.87 × Leverkusen away defence 1.13.
It expects Leverkusen to score 1.45 goals: league away average 1.38 × Leverkusen away attack 1.20 × 1. FC Köln home defence 0.88.
Based on 4 home games for 1. FC Köln, 4 away games for Leverkusen and 72 finished matches in the competition.
Attack above 1.00 means scoring more than the league average; defence above 1.00 means conceding more than average (weaker).
Most likely outcome: 1. FC Köln (52%). This is an estimate, not a certainty.
Form & stats this competition season
| 1. FC Köln | Leverkusen | |
|---|---|---|
| Table position | #15 · 9 pts | #5 · 16 pts |
| Last 5 (newest first) | LDDLW | WLLWW |
| Home W-D-L (goals) | 2-2-1 (7:6) | 3-0-1 (10:5) |
| Away W-D-L (goals) | 0-1-3 (3:8) | 2-1-2 (10:12) |
| Goals per game at home (for/against) | 1.40 / 1.20 | 2.50 / 1.25 |
| Goals per game away (for/against) | 0.75 / 2.00 | 2.00 / 2.40 |
| xG | n/a | n/a |
xG is not available from the current data source (football-data.org). Competition averages: 2.12 home and 1.36 away goals per match (81 finished matches).
Model assumptions & limitations
Assumptions
- Goals for each team follow independent Poisson distributions.
- Expected goals (λ) = league average home/away goals × attack strength × opponent defence strength.
- Strengths use only this competition and season: home form for the home side, away form for the away side. If either team has too few home/away games, overall form (all games) is used instead and this is stated in the explanation.
- Strengths are shrunk towards the league average when few games have been played.
- Predictions are for the score after regular time (90 minutes plus stoppage time).
- The model's tipped score is the one with the highest expected points under the game's scoring system, not necessarily the most likely single score.
Limitations
- No injuries, suspensions, line-ups, transfers, rotation or motivation are considered.
- No xG or shot data is used – the current data source does not provide it.
- Results in other competitions (e.g. domestic league for Champions League teams) are not included.
- Independent Poisson models tend to slightly underestimate draws.
- Probabilities are estimates with considerable uncertainty, not facts or guarantees.