Kaya vs Manila Digger: result 3-1 and analysis
PFLRegular Season - 7Kick-off on June 1, 2024 at 09:45 AM (UTC)Rizal Memorial Stadium, Manila
Full time
What was the result of Kaya vs Manila Digger?
Kaya won 3-1 on June 1, 2024.
Goalscorers
- 18′J. GayosoKaya
- 26′?Manila Digger
- 67′?Kaya
- 79′?Kaya
What is the recent form of both teams?
Over their last 15 matches in all competitions, Kaya have 9 wins, 2 draws and 4 defeats; Manila Digger have 5, 1 and 6.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 29, 2024 | Tuloy(home) | 9-0 | PFL |
| May 8, 2024 | Philippine Air Force(home) | 9-0 | PFL |
| May 5, 2024 | Dynamic Herb Cebu(home) | 1-0 | PFL |
| Apr 27, 2024 | Taguig(away) | 0-0 | PFL |
| Apr 21, 2024 | Maharlika(away) | 10-1 | PFL |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 25, 2024 | Stallion(away) | 1-2 | PFL |
| May 19, 2024 | Tuloy(away) | 3-2 | PFL |
| May 11, 2024 | Ceres(home) | 2-4 | PFL |
| May 4, 2024 | Loyola(home) | 3-1 | PFL |
| Apr 27, 2024 | Mendiola(away) | 3-2 | PFL |
How many goals should we expect?
Kaya score 3.9 and concede 1.2 goals per match over their last 15; Manila Digger score 1.7 and concede 2.1.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Kaya | 3.9 | 1.2 | 7 | 7/15 | 11/15 |
| Manila Digger | 1.7 | 2.1 | 0 | 10/12 | 9/12 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
Kaya sit 3rd in PFL (48 points from 25 matches) and Manila Digger sit 1st (63 points from 26 matches).
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 1 | Manila Digger | 26 | 19 | 6 | 1 | 114 | 18 | 63 |
| 3 | Kaya | 25 | 15 | 3 | 7 | 76 | 21 | 48 |
A question about Kaya vs Manila Digger?
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