Muscat vs Saham: result 3-1 and analysis
Professional LeagueRegular Season - 3Kick-off on September 21, 2017 at 01:35 PM (UTC)Sultan Qaboos Sport Complex, Masqaṭ (Muscat)
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What was the result of Muscat vs Saham?
Muscat won 3-1 on September 21, 2017.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Muscat have 5 wins, 7 draws and 3 defeats; Saham have 4, 5 and 6.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 17, 2017 | Sohar(away) | 1-1 | Professional League |
| Sep 13, 2017 | Fanja(home) | 2-2 | Professional League |
| May 19, 2017 | Al Suwaiq(away) | 2-2 | Professional League |
| May 15, 2017 | Oman Club(home) | 1-1 | Professional League |
| May 9, 2017 | Fanja(home) | 1-3 | Professional League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 17, 2017 | Al-Nahda(home) | 4-2 | Professional League |
| Sep 13, 2017 | Al Suwaiq(away) | 1-2 | Professional League |
| May 19, 2017 | Al-Nahda(home) | 3-3 | Professional League |
| May 15, 2017 | Al Oruba(away) | 2-2 | Professional League |
| May 10, 2017 | Jaalan(home) | 5-3 | Professional League |
What does the head-to-head say?
Across the last 4 meetings: 1 Muscat win(s), 1 draw(s), 2 Saham win(s).
| Date | Score | Competition |
|---|---|---|
| Feb 24, 2017 | Muscat 3-1 Saham | Professional League |
| Sep 27, 2016 | Saham 1-1 Muscat | Professional League |
| Apr 10, 2016 | Muscat 1-2 Saham | Professional League |
| Dec 10, 2015 | Saham 1-0 Muscat | Professional League |
How many goals should we expect?
Muscat score 1.6 and concede 1.3 goals per match over their last 15; Saham score 1.8 and concede 1.7.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Muscat | 1.6 | 1.3 | 2 | 12/15 | 7/15 |
| Saham | 1.8 | 1.7 | 1 | 12/15 | 10/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 11 | Saham | 26 | 7 | 5 | 14 | 22 | 44 | 26 |
How does Footlab 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. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy