Muscat vs Saham: result 1-2 and analysis
Professional LeagueRegular Season - 24Kick-off on April 10, 2016 at 03:30 PM (UTC)Al-Seeb Stadium, Seeb
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What was the result of Muscat vs Saham?
Saham won 1-2 on April 10, 2016.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Muscat have 5 wins, 6 draws and 4 defeats; Saham have 4, 6 and 5.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Apr 2, 2016 | Al-Musannah(away) | 2-0 | Professional League |
| Mar 12, 2016 | Dhofar(home) | 4-2 | Professional League |
| Mar 3, 2016 | Sur(away) | 0-0 | Professional League |
| Feb 20, 2016 | Salalah(home) | 2-1 | Professional League |
| Feb 11, 2016 | Sohar(away) | 0-1 | Professional League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Apr 2, 2016 | Al Suwaiq(away) | 1-3 | Professional League |
| Mar 12, 2016 | Al Oruba(home) | 0-0 | Professional League |
| Mar 4, 2016 | Al-Musannah(away) | 3-1 | Professional League |
| Feb 20, 2016 | Dhofar(home) | 1-0 | Professional League |
| Feb 11, 2016 | Sur(away) | 2-2 | Professional League |
What does the head-to-head say?
In the last meeting: 0 Muscat wins, 0 draws, 1 Saham win.
| Date | Score | Competition |
|---|---|---|
| Dec 10, 2015 | Saham 1-0 Muscat | Professional League |
How many goals should we expect?
Muscat score 1.1 and concede 0.9 goals per match over their last 15; Saham score 0.9 and concede 1.1.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Muscat | 1.1 | 0.9 | 4 | 8/15 | 4/15 |
| Saham | 0.9 | 1.1 | 5 | 7/15 | 5/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 2 | Saham | 4 | 3 | 0 | 1 | 4 | 2 | 9 |
A question about Muscat vs Saham?
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