Montebelluna vs San Luigi: result 1-1 and analysis
Serie D - Girone CRegular Season - 13Kick-off on November 10, 2019 at 01:30 PM (UTC)Stadio San Vigilio, Montebelluna
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What was the result of Montebelluna vs San Luigi?
Montebelluna and San Luigi drew 1-1 on November 10, 2019.
What is the recent form of both teams?
Over their last 12 matches in all competitions, Montebelluna have 2 wins, 7 draws and 3 defeats; San Luigi have 1, 2 and 9.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 3, 2019 | Este(away) | 0-0 | Serie D - Girone C |
| Oct 30, 2019 | Delta Porto Tolle(home) | 1-0 | Serie D - Girone C |
| Oct 26, 2019 | Vigasio(away) | 3-1 | Serie D - Girone C |
| Oct 20, 2019 | Union Feltre(home) | 1-1 | Serie D - Girone C |
| Oct 13, 2019 | Villafranca(home) | 0-0 | Serie D - Girone C |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 3, 2019 | Caldiero Terme(home) | 2-0 | Serie D - Girone C |
| Oct 30, 2019 | Villafranca(away) | 0-0 | Serie D - Girone C |
| Oct 26, 2019 | Legnago Salus(home) | 1-2 | Serie D - Girone C |
| Oct 20, 2019 | Chions(away) | 1-3 | Serie D - Girone C |
| Oct 13, 2019 | Adriese(home) | 1-6 | Serie D - Girone C |
How many goals should we expect?
Montebelluna score 0.8 and concede 1.0 goals per match over their last 12; San Luigi score 1.1 and concede 2.5.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Montebelluna | 0.8 | 1.0 | 4 | 5/12 | 3/12 |
| San Luigi | 1.1 | 2.5 | 2 | 9/12 | 8/12 |
Over the last 12 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 16 | San Luigi | 35 | 8 | 10 | 17 | 34 | 57 | 34 |
A question about Montebelluna vs San Luigi?
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