Tatabánya vs Soproni VSE: result 1-1 and analysis
NB IIRegular Season - 14Kick-off on November 19, 2011 at 12:00 PM (UTC)Grosics Gyula Stadion, Tatabánya
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What was the result of Tatabánya vs Soproni VSE?
Tatabánya and Soproni VSE drew 1-1 on November 19, 2011.
Goalscorers
- 31′Mico SmiljanicSoproni VSE
- 54′Ferenc BeresTatabánya
Official lineup for Tatabánya vs Soproni VSE
The starting elevens fielded at kick-off.
Tatabánya
- FFerenc Beres
- JJ. Farkas
- TTamas Mori
- II. Deme
- DD. Szekér
- DD. Milanovic
- AAttila Böjte
- LLászló Almási
- KK. Simkó
- VV. Farkas
- AAndrás Dombai
Soproni VSE
- LLaszlo Szabo
- CCsaba Csordás
- ZZ. Nagy
- BB. Lazić
- RR. Bausz
- GG. Bognár
- MMico Smiljanic
- DD. Tóth
- KK. Peric
- ZZoltan Csontos
- CC. Borsi
What is the recent form of both teams?
Over their last 15 matches in all competitions, Tatabánya have 8 wins, 2 draws and 5 defeats; Soproni VSE have 5, 1 and 8.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 12, 2011 | Bajai(away) | 3-2 | NB II |
| Nov 5, 2011 | Veszprém(home) | 0-0 | NB II |
| Oct 30, 2011 | Paksi SE II(home) | 3-0 | NB II |
| Oct 26, 2011 | Fehérvár FC(home) | 0-2 | Magyar Kupa |
| Oct 22, 2011 | MTK Budapest(away) | 0-1 | NB II |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Nov 12, 2011 | Paksi SE II(home) | 0-1 | NB II |
| Nov 5, 2011 | MTK Budapest(away) | 0-4 | NB II |
| Oct 29, 2011 | Dunaújváros-Pálhalma(home) | 1-2 | NB II |
| Oct 22, 2011 | Kozarmisleny FC(away) | 3-1 | NB II |
| Oct 15, 2011 | Ajka(home) | 2-1 | NB II |
How many goals should we expect?
Tatabánya score 1.4 and concede 0.9 goals per match over their last 15; Soproni VSE score 0.9 and concede 1.8.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Tatabánya | 1.4 | 0.9 | 5 | 6/15 | 7/15 |
| Soproni VSE | 0.9 | 1.8 | 3 | 5/14 | 7/14 |
Over the last 15 matches in all competitions.
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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