RUS Binche vs Verlaine: result 3-2 and analysis
Second Amateur Division - ACFFACFF - 8Kick-off on October 15, 2023 at 01:00 PM (UTC)Stade Aimé Vachaudez, Binche
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What was the result of RUS Binche vs Verlaine?
RUS Binche won 3-2 on October 15, 2023.
What is the recent form of both teams?
Over their last 13 matches in all competitions, RUS Binche have 5 wins, 4 draws and 4 defeats; Verlaine have 3, 4 and 8.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 8, 2023 | Union Saint-Gilloise II(home) | 3-2 | Second Amateur Division - ACFF |
| Sep 30, 2023 | Jette(away) | 1-0 | Second Amateur Division - ACFF |
| Sep 24, 2023 | Rebecq(home) | 0-0 | Second Amateur Division - ACFF |
| Sep 20, 2023 | Albert Quévy-Mons(away) | 0-2 | Second Amateur Division - ACFF |
| Sep 10, 2023 | Warnant(home) | 2-2 | Second Amateur Division - ACFF |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 7, 2023 | La Louvière Centre(home) | 0-1 | Second Amateur Division - ACFF |
| Oct 1, 2023 | Warnant(away) | 1-1 | Second Amateur Division - ACFF |
| Sep 23, 2023 | Rochefort(home) | 2-1 | Second Amateur Division - ACFF |
| Sep 16, 2023 | Ganshoren(away) | 0-3 | Second Amateur Division - ACFF |
| Sep 9, 2023 | Rebecq(home) | 2-2 | Second Amateur Division - ACFF |
How many goals should we expect?
RUS Binche score 1.9 and concede 1.3 goals per match over their last 13; Verlaine score 1.0 and concede 2.2.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| RUS Binche | 1.9 | 1.3 | 4 | 7/13 | 8/13 |
| Verlaine | 1.0 | 2.2 | 2 | 7/15 | 9/15 |
Over the last 13 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 11 | RUS Binche | 12 | 4 | 4 | 4 | 19 | 23 | 16 |
A question about RUS Binche vs Verlaine?
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How does Footlab AI compute this prediction?
The model combines both teams' Elo ratings (anchored by competition pool), their recent form weighted by opponent strength, home advantage and a Poisson matrix for scorelines. When bookmakers publish odds for the match, their consensus is built into the calculation: that is what proved most accurate on past matches. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy