Atvidabergs FF vs Assyriska FF: result 2-1 and analysis
SuperettanRegular Season - 4Kick-off on April 24, 2016 at 02:00 PM (UTC)Kopparvallen, Åtvidaberg
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What was the result of Atvidabergs FF vs Assyriska FF?
Atvidabergs FF won 2-1 on April 24, 2016.
Goalscorers
- 31′Daniel SliperAssyriska FF
- 72′Ammar AhmedAtvidabergs FF
- 79′Shkodran MaholliAtvidabergs FF
Official lineup for Atvidabergs FF vs Assyriska FF
The starting elevens fielded at kick-off.
Atvidabergs FF
- 7Mahmoud Eid
- 9Shkodran Maholli
- 18M. Ćosić
- 6Ammar Ahmed
- 10Björn Mikael André Westerblad
- 14Simon Helg
- 2J. Birkfeldt
- 3M. Berg
- 4H. Holmgren
- 28S. Ramhorn
- 1Gustav Jansson
Assyriska FF
- 10M. Genc
- 24Aziz Corr Nyang
- 3S. Strand
- 8Daniel Sliper
- 9T. Waker
- 12F. Holster
- 5D. Durmaz
- 6Linus Malmborg
- 13G. Jarl
- 20Rani Haddad
- 1Robin Malmkvist
What is the recent form of both teams?
Over their last 3 matches in all competitions, Atvidabergs FF have 0 wins, 1 draw and 2 defeats; Assyriska FF have 1, 0 and 2.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Apr 17, 2016 | Ljungskile SK(away) | 2-2 | Superettan |
| Apr 10, 2016 | Sirius(home) | 0-3 | Superettan |
| Apr 2, 2016 | Varbergs BoIS FC(away) | 1-2 | Superettan |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Apr 17, 2016 | Orgryte IS(home) | 0-1 | Superettan |
| Apr 9, 2016 | IK Frej(away) | 1-2 | Superettan |
| Apr 3, 2016 | Dalkurd FF(home) | 2-1 | Superettan |
How many goals should we expect?
Atvidabergs FF score 1.0 and concede 2.3 goals per match over their last 3; Assyriska FF score 1.0 and concede 1.3.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Atvidabergs FF | 1.0 | 2.3 | 0 | 2/3 | 3/3 |
| Assyriska FF | 1.0 | 1.3 | 0 | 2/3 | 2/3 |
Over the last 3 matches in all competitions.
A question about Atvidabergs FF vs Assyriska FF?
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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