AI prediction · locked November 6, 2026 7:45 pm
Model pick 0–1
Result after 90 minutes: 2–2. The model tipped 0–1 and scored 0 points.
Most likely scores
Why
The model expects Fiorentina to score 1.14 goals: league home average 1.62 × Fiorentina home attack 0.81 × Juventus away defence 0.87.
It expects Juventus to score 1.66 goals: league away average 1.56 × Juventus away attack 0.88 × Fiorentina home defence 1.21.
Based on 5 home games for Fiorentina, 5 away games for Juventus and 100 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: Juventus (50%). This is an estimate, not a certainty.
Form & stats this competition season
| Fiorentina | Juventus | |
|---|---|---|
| Table position | #15 · 9 pts | #7 · 18 pts |
| Last 5 (newest first) | DWDLL | DWLWL |
| Home W-D-L (goals) | 0-3-3 (7:13) | 3-1-1 (7:4) |
| Away W-D-L (goals) | 2-0-3 (10:12) | 2-2-2 (8:8) |
| Goals per game at home (for/against) | 1.17 / 2.17 | 1.40 / 0.80 |
| Goals per game away (for/against) | 2.00 / 2.40 | 1.33 / 1.33 |
| xG | n/a | n/a |
xG is not available from the current data source (football-data.org). Competition averages: 1.60 home and 1.51 away goals per match (110 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.