SCVU vs Vipers: result 3-1 and analysis
Premier LeagueRegular Season - 3Kick-off on September 17, 2013 at 01:30 PM (UTC)Mandela National Stadium, Kampala
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What was the result of SCVU vs Vipers?
SCVU won 3-1 on September 17, 2013.
What is the recent form of both teams?
Over their last 15 matches in all competitions, SCVU have 5 wins, 5 draws and 5 defeats; Vipers have 4, 7 and 4.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 13, 2013 | Entebbe(away) | 1-1 | Premier League |
| Aug 30, 2013 | Masaka LC(home) | 3-1 | Premier League |
| Aug 23, 2013 | Bright Stars(away) | 1-0 | Premier League |
| May 10, 2013 | Masaka LC(home) | 0-1 | Premier League |
| May 7, 2013 | BUL(away) | 1-0 | Premier League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 10, 2013 | Express(away) | 0-0 | Premier League |
| Aug 30, 2013 | Entebbe(home) | 1-1 | Premier League |
| Aug 20, 2013 | Masaka LC(away) | 2-0 | Premier League |
| May 10, 2013 | SC Villa(home) | 3-2 | Premier League |
| Apr 26, 2013 | URA(home) | 0-1 | Premier League |
What does the head-to-head say?
Across the last 2 meetings: 0 SCVU wins, 1 draw, 1 Vipers win.
| Date | Score | Competition |
|---|---|---|
| Feb 22, 2013 | Vipers 1-1 SCVU | Premier League |
| Jan 14, 2013 | SCVU 1-3 Vipers | Premier League |
How many goals should we expect?
SCVU score 0.9 and concede 0.8 goals per match over their last 15; Vipers score 0.9 and concede 0.8.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| SCVU | 0.9 | 0.8 | 6 | 5/15 | 3/15 |
| Vipers | 0.9 | 0.8 | 6 | 7/15 | 4/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 8 | Vipers | 4 | 2 | 1 | 1 | 8 | 2 | 7 |
A question about SCVU vs Vipers?
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