Pro Calcio Tor Sapienza vs Latte Dolce: result 1-0 and analysis
Serie D - Girone GRegular Season - 13Kick-off on November 24, 2019 at 02:00 PM (UTC)Stadio Giorgio Castelli, Roma
Full time
What was the result of Pro Calcio Tor Sapienza vs Latte Dolce?
Pro Calcio Tor Sapienza won 1-0 on November 24, 2019.
What is the recent form of both teams?
Over their last 12 matches in all competitions, Pro Calcio Tor Sapienza have 2 wins, 3 draws and 7 defeats; Latte Dolce have 8, 1 and 3.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 17, 2019 | Ladispoli(away) | 1-4 | Serie D - Girone G |
| Nov 10, 2019 | Aprilia(home) | 0-3 | Serie D - Girone G |
| Nov 3, 2019 | Trastevere(away) | 2-5 | Serie D - Girone G |
| Oct 27, 2019 | Vis Artena(home) | 1-3 | Serie D - Girone G |
| Oct 20, 2019 | Arzachena(away) | 1-1 | Serie D - Girone G |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 16, 2019 | Ostia Mare(home) | 0-1 | Serie D - Girone G |
| Nov 9, 2019 | Lanusei(away) | 2-3 | Serie D - Girone G |
| Nov 2, 2019 | Budoni(away) | 4-2 | Serie D - Girone G |
| Oct 27, 2019 | Turris(home) | 0-0 | Serie D - Girone G |
| Oct 20, 2019 | Ladispoli(away) | 1-0 | Serie D - Girone G |
How many goals should we expect?
Pro Calcio Tor Sapienza score 1.3 and concede 2.8 goals per match over their last 12; Latte Dolce score 1.8 and concede 1.0.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Pro Calcio Tor Sapienza | 1.3 | 2.8 | 1 | 8/12 | 9/12 |
| Latte Dolce | 1.8 | 1.0 | 5 | 6/12 | 7/12 |
Over the last 12 matches in all competitions.
Where do both teams sit in the table?
The current season has no played match yet: final standings of the 2025-26 season.
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 6 | Latte Dolce | 34 | 14 | 6 | 14 | 49 | 49 | 48 |
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