Ull/Kisa vs Levanger: result 1-3 and analysis
1. DivisionRegular Season - 3Kick-off on April 17, 2016 at 04:00 PM (UTC)Jessheim Stadion, Jessheim
Full time
What was the result of Ull/Kisa vs Levanger?
Levanger won 1-3 on April 17, 2016.
Goalscorers
- 22′Mats Andreas PeterssonLevanger
- 25′Jo Sondre AasLevanger
- 69′O. Kristian LangåsUll/Kisa
- 90′Mats Andreas PeterssonLevanger
Official lineup for Ull/Kisa vs Levanger
The starting elevens fielded at kick-off.
Ull/Kisa
- 5O. Kristian Langås
- 16K. Ødemarksbakken
- 7Jan Tore Amundsen
- 8T. Jørstad
- 11Christian Aas
- 14Nicolay Tveten Solberg
- 22M. Pawlik
- 27F. Krogstad
- 15T. Braaten
- 20V. Dragsnes
- 1A. Håkull-Vangen
Levanger
- 8Vegard Voll
- 9B. Stokke
- 10Jo Sondre Aas
- 11B. Bye
- 17Markus Carl Victor Astvald
- 6Mats Andreas Petersson
- 3T. Nordberg
- 5Rikard Nilsson
- 18Per Verner Vågan Rønning
- 25B. Sørmo
- 1Ola Rygg
What is the recent form of both teams?
Over their last 2 matches in all competitions, Ull/Kisa have 0 wins, 2 draws and 0 defeats; Levanger have 2, 0 and 0.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Apr 10, 2016 | Bryne(away) | 1-1 | 1. Division |
| Apr 3, 2016 | Sandnes ULF(home) | 1-1 | 1. Division |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Apr 10, 2016 | Sandefjord(home) | 1-0 | 1. Division |
| Apr 3, 2016 | Fredrikstad(away) | 3-2 | 1. Division |
How many goals should we expect?
Ull/Kisa score 1.0 and concede 1.0 goals per match over their last 2; Levanger score 2.0 and concede 1.0.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Ull/Kisa | 1.0 | 1.0 | 0 | 2/2 | 0/2 |
| Levanger | 2.0 | 1.0 | 1 | 1/2 | 1/2 |
Over the last 2 matches in all competitions.
A question about Ull/Kisa vs Levanger?
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