Saraburi vs Trat FC: result 1-3 and analysis
Thai League 2Matchday 30Kick-off on September 29, 2013 at 11:00 AM (UTC)Saraburi Stadium, Saraburi
Full time
What was the result of Saraburi vs Trat FC?
Trat FC won 1-3 on September 29, 2013.
What is the recent form of both teams?
Over their last 15 matches in all competitions, Saraburi have 6 wins, 5 draws and 4 defeats; Trat FC have 4, 4 and 7.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 21, 2013 | Ayutthaya(away) | 3-3 | Thai League 2 |
| Sep 14, 2013 | Air Force Central FC(home) | 2-1 | Thai League 2 |
| Sep 7, 2013 | Port FC(away) | 0-2 | Thai League 2 |
| Aug 31, 2013 | Nakhon Pathom(away) | 2-2 | Thai League 2 |
| Aug 24, 2013 | TTM Customs FC(home) | 3-0 | Thai League 2 |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 21, 2013 | Phuket(away) | 0-0 | Thai League 2 |
| Sep 15, 2013 | Ayutthaya(home) | 1-1 | Thai League 2 |
| Sep 8, 2013 | Rayong FC(home) | 1-3 | Thai League 2 |
| Sep 1, 2013 | Air Force Central FC(away) | 0-1 | Thai League 2 |
| Aug 24, 2013 | Nakhon Pathom(home) | 3-2 | Thai League 2 |
What does the head-to-head say?
In the last meeting: 0 Saraburi wins, 0 draws, 1 Trat FC win.
| Date | Score | Competition |
|---|---|---|
| May 29, 2013 | Trat FC 2-1 Saraburi | Thai League 2 |
How many goals should we expect?
Saraburi score 1.8 and concede 1.5 goals per match over their last 15; Trat FC score 1.4 and concede 2.1.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Saraburi | 1.8 | 1.5 | 4 | 9/15 | 8/15 |
| Trat FC | 1.4 | 2.1 | 2 | 9/15 | 11/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 9 | Trat FC | 34 | 12 | 8 | 14 | 49 | 53 | 44 |
A question about Saraburi vs Trat FC?
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