Kiruna FF vs Ytterhogdal: result 1-1 and analysis
Division 2 - NorrlandNorrland - 7Kick-off on May 14, 2022 at 11:00 AM (UTC)Skyttis IP konstgräs, Örnsköldsvik
Full time
What was the result of Kiruna FF vs Ytterhogdal?
Kiruna FF and Ytterhogdal drew 1-1 on May 14, 2022.
What is the recent form of both teams?
Over their last 5 matches in all competitions, Kiruna FF have 3 wins, 0 draws and 2 defeats; Ytterhogdal have 5, 2 and 8.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 7, 2022 | Stöde(home) | 0-1 | Division 2 - Norrland |
| Apr 30, 2022 | Bergnäsets(away) | 3-2 | Division 2 - Norrland |
| Apr 23, 2022 | IFK Östersund(home) | 2-1 | Division 2 - Norrland |
| Apr 14, 2022 | Sandviks(away) | 3-1 | Division 2 - Norrland |
| Apr 2, 2022 | Friska Viljor(away) | 0-2 | Division 2 - Norrland |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 7, 2022 | Älgarna(home) | 5-2 | Division 2 - Norrland |
| Apr 28, 2022 | Stöde(away) | 2-1 | Division 2 - Norrland |
| Apr 23, 2022 | IFK Luleå(home) | 0-2 | Division 2 - Norrland |
| Apr 15, 2022 | Bergnäsets(away) | 3-1 | Division 2 - Norrland |
| Apr 9, 2022 | Skellefteå(home) | 0-0 | Division 2 - Norrland |
How many goals should we expect?
Kiruna FF score 1.6 and concede 1.4 goals per match over their last 5; Ytterhogdal score 1.1 and concede 2.9.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Kiruna FF | 1.6 | 1.4 | 0 | 3/5 | 3/5 |
| Ytterhogdal | 1.1 | 2.9 | 3 | 7/15 | 9/15 |
Over the last 5 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 5 | Kiruna FF | 12 | 4 | 3 | 5 | 19 | 19 | 17 |
A question about Kiruna FF vs Ytterhogdal?
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