Viby vs Silkeborg KFUM: result 2-3 and analysis
Denmark Series - Group 4Group 4 - 7Kick-off on September 14, 2019 at 11:00 AM (UTC)Viby Idrætspark, Viby J
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What was the result of Viby vs Silkeborg KFUM?
Silkeborg KFUM won 2-3 on September 14, 2019.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Viby have 5 wins, 2 draws and 8 defeats; Silkeborg KFUM have 9, 0 and 6.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 7, 2019 | Djursland(away) | 3-3 | Denmark Series - Group 4 |
| Sep 3, 2019 | Fuglebakken KFUM(away) | 3-0 | DBU Pokalen |
| Aug 31, 2019 | Nørresundby(home) | 4-2 | Denmark Series - Group 4 |
| Aug 24, 2019 | Holstebro(home) | 2-4 | Denmark Series - Group 4 |
| Aug 17, 2019 | Kjellerup(away) | 1-7 | Denmark Series - Group 4 |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 6, 2019 | Kjellerup(home) | 1-6 | Denmark Series - Group 4 |
| Aug 31, 2019 | Lyseng(away) | 4-1 | Denmark Series - Group 4 |
| Aug 27, 2019 | VRI(home) | 3-1 | Denmark Series - Group 4 |
| Aug 23, 2019 | Skjold Sæby(home) | 3-0 | Denmark Series - Group 4 |
| Aug 17, 2019 | Aarhus Fremad II(away) | 1-2 | Denmark Series - Group 4 |
How many goals should we expect?
Viby score 2.6 and concede 2.9 goals per match over their last 15; Silkeborg KFUM score 2.3 and concede 1.9.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Viby | 2.6 | 2.9 | 2 | 12/15 | 15/15 |
| Silkeborg KFUM | 2.3 | 1.9 | 2 | 10/15 | 13/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 7 | Viby | 18 | 5 | 4 | 9 | 29 | 42 | 19 |
A question about Viby vs Silkeborg KFUM?
Ask Footlab AI: lineups, absences, player form, stats, how the match could unfold.
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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