Rudes vs NK Zagreb: result 0-0 and analysis
First NLRegular Season - 12Kick-off on October 22, 2016 at 01:00 PM (UTC)Igralište NK Rudeš, Zagreb
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What was the result of Rudes vs NK Zagreb?
Rudes and NK Zagreb drew 0-0 on October 22, 2016.
What is the recent form of both teams?
Over their last 11 matches in all competitions, Rudes have 6 wins, 3 draws and 2 defeats; NK Zagreb have 4, 5 and 2.
The competition is shown for every match: in pre-season, several of these games are friendlies.
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 16, 2016 | HNK Gorica(home) | 1-1 | First NL |
| Oct 8, 2016 | Sesvete(away) | 0-0 | First NL |
| Oct 2, 2016 | Imotski(home) | 1-0 | First NL |
| Sep 28, 2016 | Lucko(home) | 7-1 | First NL |
| Sep 24, 2016 | Dugopolje(away) | 0-0 | First NL |
| Date | Opponent | Score | Competition |
|---|---|---|---|
| Oct 16, 2016 | Dinamo Zagreb II(away) | 1-1 | First NL |
| Oct 8, 2016 | Sibenik(home) | 1-0 | First NL |
| Oct 2, 2016 | Hrvatski Dragovoljac(away) | 0-1 | First NL |
| Sep 28, 2016 | Novigrad(home) | 0-0 | First NL |
| Sep 24, 2016 | Solin(home) | 1-1 | First NL |
What does the head-to-head say?
In the last meeting: 0 Rudes wins, 0 draws, 1 NK Zagreb win.
| Date | Score | Competition |
|---|---|---|
| Aug 20, 2016 | NK Zagreb 5-1 Rudes | First NL |
How many goals should we expect?
Rudes score 1.8 and concede 1.1 goals per match over their last 11; NK Zagreb score 1.5 and concede 1.0.
| Club | Goals scored / match | Goals conceded / match | Clean sheets | Both teams scored | Over 2.5 goals |
|---|---|---|---|---|---|
| Rudes | 1.8 | 1.1 | 5 | 6/11 | 5/11 |
| NK Zagreb | 1.5 | 1.0 | 3 | 7/11 | 5/11 |
Over the last 11 matches in all competitions.
Where do both teams sit in the table?
| # | Club | P | W | D | L | GF | GA | Pts |
|---|---|---|---|---|---|---|---|---|
| 1 | Rudes | 33 | 19 | 8 | 6 | 49 | 29 | 65 |
A question about Rudes vs NK Zagreb?
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