Lernayin Artsakh II vs Syunik: prediction and stats
First LeagueRegular Season - 32Kick-off on May 23, 2023 at 01:00 PM (UTC)Sisian Stadium, Sisian
Match cancelled
What is the recent form of both teams?
Over their last 15 matches in all competitions, Lernayin Artsakh II have 0 wins, 2 draws and 13 defeats; Syunik have 4, 2 and 9.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Dec 1, 2022 | Banants II(home) | 1-2 | First League |
| Nov 25, 2022 | Pyunik II(away) | 1-5 | First League |
| Nov 21, 2022 | Ararat-Armenia II(away) | 2-2 | First League |
| Nov 11, 2022 | Ararat II(home) | 0-2 | First League |
| Nov 5, 2022 | Alashkert II(away) | 1-4 | First League |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| May 18, 2023 | Ararat II(home) | 2-0 | First League |
| May 14, 2023 | West Armenia(away) | 1-3 | First League |
| May 9, 2023 | Mika(home) | 0-1 | First League |
| May 4, 2023 | Gandzasar(away) | 1-1 | First League |
| Apr 28, 2023 | BKMA II(home) | 0-1 | First League |
What does the head-to-head say?
Across the last 1 meetings: 0 Lernayin Artsakh II win(s), 0 draw(s), 1 Syunik win(s).
| Date | Score | Competition |
|---|---|---|
| Sep 29, 2022 | Lernayin Artsakh II 2-5 Syunik | First League |
Last archived meeting: Lernayin Artsakh II vs Syunik on Sep 29, 2022
How many goals should we expect?
Lernayin Artsakh II score 1.1 and concede 3.3 goals per match over their last 15; Syunik score 1.1 and concede 1.6.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Lernayin Artsakh II | 1.1 | 3.3 | 0 | 11/15 | 11/15 |
| Syunik | 1.1 | 1.6 | 3 | 8/15 | 8/15 |
Over the last 15 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 4 | Syunik | 30 | 22 | 1 | 7 | 83 | 26 | 67 |
How does Footlab 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. Its accuracy is measured continuously on the predictions actually published. Methodology and measured accuracy