San Francisco vs Sayaxché: result 1-0 and analysis
Primera DivisionAperturaKick-off on October 7, 2012 at 05:00 PM (UTC)
Full time
What was the result of San Francisco vs Sayaxché?
San Francisco won 1-0 on October 7, 2012.
What is the recent form of both teams?
Over their last 10 matches in all competitions, San Francisco have 5 wins, 2 draws and 3 defeats; Sayaxché have 6, 4 and 5.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 30, 2012 | Mixco(away) | 1-3 | Primera Division |
| Sep 23, 2012 | Sacachispas(away) | 1-0 | Primera Division |
| Sep 16, 2012 | Cobán Imperial(home) | 1-0 | Primera Division |
| Sep 9, 2012 | Zacapa(away) | 3-5 | Primera Division |
| Sep 2, 2012 | Quirigua(home) | 1-0 | Primera Division |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 30, 2012 | Guastatoya(home) | 1-3 | Primera Division |
| Sep 23, 2012 | Carchá(home) | 4-0 | Primera Division |
| Sep 16, 2012 | Achuapa(away) | 1-2 | Primera Division |
| Sep 9, 2012 | Mixco(away) | 1-4 | Primera Division |
| Sep 2, 2012 | Sacachispas(home) | 3-1 | Primera Division |
What does the head-to-head say?
In the last meeting: 0 San Francisco wins, 1 draw, 0 Sayaxché wins.
| Date | Score | Competition |
|---|---|---|
| Aug 5, 2012 | Sayaxché 1-1 San Francisco | Primera Division |
Last archived meeting: Sayaxché vs San Francisco on Aug 5, 2012
How many goals should we expect?
San Francisco score 1.6 and concede 1.3 goals per match over their last 10; Sayaxché score 1.5 and concede 1.4.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| San Francisco | 1.6 | 1.3 | 4 | 5/10 | 4/10 |
| Sayaxché | 1.5 | 1.4 | 6 | 8/15 | 9/15 |
Over the last 10 matches in all competitions.
A question about San Francisco vs Sayaxché?
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