BATE II vs Bumprom: result 1-0 and analysis
1. DivisionRegular Season - 12Kick-off on June 23, 2024 at 03:00 PM (UTC)
Full time
What was the result of BATE II vs Bumprom?
BATE II won 1-0 on June 23, 2024.
Goalscorers
- 39′A. ZhvirblyaBATE II (o.g.)
What is the recent form of both teams?
Over their last 11 matches in all competitions, BATE II have 5 wins, 4 draws and 2 defeats; Bumprom have 6, 2 and 7.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jun 14, 2024 | ABFF U17(away) | 1-3 | 1. Division |
| Jun 8, 2024 | Slonim(home) | 0-0 | 1. Division |
| Jun 1, 2024 | Orsha(away) | 1-1 | 1. Division |
| May 24, 2024 | Lida(home) | 1-1 | 1. Division |
| May 18, 2024 | Dinamo Minsk II(away) | 2-1 | 1. Division |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Jun 15, 2024 | Shakhtyor Petrikov(home) | 3-0 | 1. Division |
| Jun 9, 2024 | Ostrovets FC(away) | 0-0 | 1. Division |
| Jun 2, 2024 | Molodechno-DYuSSh 4(home) | 2-1 | 1. Division |
| May 30, 2024 | Tekhnolog(away) | 2-0 | Coppa |
| May 25, 2024 | Baranovichi(away) | 1-2 | 1. Division |
How many goals should we expect?
BATE II score 1.5 and concede 1.3 goals per match over their last 11; Bumprom score 1.7 and concede 1.3.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| BATE II | 1.5 | 1.3 | 2 | 9/11 | 6/11 |
| Bumprom | 1.7 | 1.3 | 4 | 8/15 | 9/15 |
Over the last 11 matches in all competitions.
Where do both teams sit in the table?
BATE II sit 17th in 1. Division (17 points from 21 matches) and Bumprom sit 7th (34 points from 21 matches).
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 3 | Bumprom | 12 | 7 | 2 | 3 | 27 | 15 | 23 |
| 7 | Bumprom | 21 | 10 | 4 | 7 | 40 | 29 | 34 |
| 14 | BATE II | 11 | 2 | 5 | 4 | 16 | 22 | 11 |
| 17 | BATE II | 21 | 4 | 5 | 12 | 29 | 49 | 17 |
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