SDJuShOR-3 vs Uzda: result 0-3 and analysis
2. DivisionRegular Season - 23Kick-off on October 7, 2017 at 12:00 PM (UTC)Stadyen Akademii Futbola, Pinsk
Full time
What was the result of SDJuShOR-3 vs Uzda?
Uzda won 0-3 on October 7, 2017.
What is the recent form of both teams?
Over their last 15 matches in all competitions, SDJuShOR-3 have 1 win, 0 draws and 14 defeats; Uzda have 5, 3 and 7.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 30, 2017 | Viktoriya Maryina Horka(away) | 0-3 | 2. Division |
| Sep 23, 2017 | Molodechno-DYuSSh 4(home) | 1-5 | 2. Division |
| Sep 16, 2017 | SMI Autotrans(away) | 2-5 | 2. Division |
| Sep 9, 2017 | YuA-Stroy-DYuSSh(home) | 0-4 | 2. Division |
| Sep 2, 2017 | Kletsk(away) | 1-3 | 2. Division |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Sep 30, 2017 | Lyuban'(home) | 3-2 | 2. Division |
| Sep 24, 2017 | Sbornaya TF(away) | 4-2 | 2. Division |
| Sep 16, 2017 | Oshmyany(home) | 0-4 | 2. Division |
| Sep 9, 2017 | Sputnik(away) | 0-3 | 2. Division |
| Sep 2, 2017 | SMI Autotrans(home) | 0-5 | 2. Division |
What does the head-to-head say?
In the last meeting: 0 SDJuShOR-3 wins, 0 draws, 1 Uzda win.
| Date | Score | Competition |
|---|---|---|
| Jun 24, 2017 | Uzda 3-0 SDJuShOR-3 | 2. Division |
How many goals should we expect?
SDJuShOR-3 score 0.9 and concede 4.6 goals per match over their last 15; Uzda score 2.0 and concede 2.7.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| SDJuShOR-3 | 0.9 | 4.6 | 0 | 8/15 | 14/15 |
| Uzda | 2.0 | 2.7 | 1 | 11/15 | 13/15 |
Over the last 15 matches in all competitions.
A question about SDJuShOR-3 vs Uzda?
Ask Footlab AI: lineups, absences, player form, stats, how the match could unfold.
Sign-in required. No odds displayed, no affiliation: the method is published and the accuracy is measured.
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