Manchester United vs Nottingham Forest Prediction — Model Probabilities
Edge (pp): green = we see more probability than the market · gold = essentially even (±0.5pp) · red = we see less. The Decision above outranks any single row: the engine backs well-supported edges, not merely big ones.
Manchester United v Nottingham Forest — Team-Based Signals
Manchester United vs Nottingham Forest Head-to-Head — Last 6 Meetings
2-1
HT 0-0
Manchester United
İNP
3-2
HT 1-2
Nottingham Forest
İNP
0-2
HT 0-1
Manchester United
İNP
2-0
HT 0-0
Nottingham Forest
İNLK
0-3
HT 0-2
Manchester United
İNLK
Manchester United 2-3 Nottingham Forest — Actual Match Statistics
Nottingham Forest edged past Manchester United 3-2 away from home. The game was level at 1-1 at the break, so the decisive goals came after it. 5 goals made it one of the day's more open games.
Our pre-match lean was Manchester United at 48%, but a lead that slim is closer to a coin-flip than a call — and it fell the other way.
Over 2.5 goals sat near a coin-flip at 50% and both teams to score was close to even at 47%.
On the day, Manchester United had 71% of the ball to Nottingham Forest's 29%, shots finished 17-11, expected goals 1.60 to 0.83 — on chances, Manchester United could feel hard done by.
For context: across their last 6 meetings in our records, Manchester United had the better of it with 5 wins. Those games averaged 2.00 goals for Manchester United and 0.67 for Nottingham Forest.
What was the final score of Manchester United vs Nottingham Forest?
Manchester United 2-3 Nottingham Forest in the England - Premier League on 07/12/2024. The analysis above shows what our model predicted before kick-off next to what actually happened.
What did the model predict for Manchester United vs Nottingham Forest?
Before kick-off our model rated a Manchester United win the most likely single outcome at about 48%, with the full match-result split Manchester United 48%, draw 26%, Nottingham Forest 26%. The final score 2-3 went against our main leads this time — single matches are noisy by nature; our accuracy is measured across thousands of predictions, not one result.