GrIFK vs Haka: result 2-1 and analysis
YkkönenRegular Season - 4Kick-off on May 28, 2016 at 01:00 PM (UTC)Kauniaisten Keskuskenttä, Grankulla
Full time
What was the result of GrIFK vs Haka?
GrIFK won 2-1 on May 28, 2016.
Goalscorers
- 26′Petter MeyerGrIFK
- 53′C. MatroneGrIFK
- 89′D. OmagbemiHaka
Official lineup for GrIFK vs Haka
The starting elevens fielded at kick-off.
GrIFK
- 9D. Langhoff
- 16Jonas Britschgi
- 17Petter Meyer
- 11C. Matrone
- 15Valmir Seferi
- 21Lassi Luoto
- 4Niko Perera
- 5T. Takamäki
- 7Joni Kaasinen
- 8Joonas Ilomäki
- 1J. Ström
Haka
- 9D. Omagbemi
- 7J. Ahonen
- 10J. Mäkelä
- 26Chris James
- 4E. Auvinen
- 5J. Saarinen
- 6M. Latosaari
- 20Cheyne Fowler
- 22E. Mbachu
- 34H. Malundama
- 35V. Kauppinen
What is the recent form of both teams?
Over their last 15 matches in all competitions, GrIFK have 8 wins, 2 draws and 5 defeats; Haka have 7, 4 and 4.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 23, 2016 | EIF(away) | 0-3 | Ykkönen |
| May 15, 2016 | Turku PS(home) | 1-0 | Ykkönen |
| May 8, 2016 | JJK(away) | 0-1 | Ykkönen |
| May 4, 2016 | Kooteepee(home) | 1-1 | Ykkönen |
| Apr 29, 2016 | KPV Kokkola(away) | 0-3 | Ykkönen |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 21, 2016 | AC Oulu(home) | 2-2 | Ykkönen |
| May 16, 2016 | EIF(away) | 1-1 | Ykkönen |
| May 8, 2016 | FF Jaro(away) | 0-0 | Ykkönen |
| May 4, 2016 | KPV Kokkola(home) | 0-3 | Ykkönen |
| Apr 29, 2016 | FC jazz(away) | 2-1 | Ykkönen |
How many goals should we expect?
GrIFK score 1.5 and concede 1.3 goals per match over their last 15; Haka score 2.0 and concede 1.5.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| GrIFK | 1.5 | 1.3 | 4 | 8/15 | 9/15 |
| Haka | 2.0 | 1.5 | 3 | 9/15 | 9/15 |
Over the last 15 matches in all competitions.
A question about GrIFK vs Haka?
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