Match

Shkendija vs Dacia: result 3-0 and analysis

UEFA Europa League1st Qualifying RoundKick-off on June 29, 2017 at 04:00 PM (UTC)Ecolog Arena, Tetovo

Shkendija
3-0
Dacia

Full time

What was the result of Shkendija vs Dacia?

Shkendija won 3-0 on June 29, 2017.

Goalscorers

  • 7′B. IbraimiShkendija (pen.)
  • 11′M. RadeskiShkendija
  • 75′M. RadeskiShkendija

Official lineup for Shkendija vs Dacia

The starting elevens fielded at kick-off.

Shkendija

  1. 7B. Ibraimi
  2. 14M. Radeski
  3. 18Stênio Júnior
  1. 5A. Alimi
  2. 10F. Hasani
  3. 11S. Vujčić
  4. 77B. Todorovski
  1. 4E. Bejtulai
  2. 6A. Cuculi
  3. 13Ivan Čeliković
  1. 1K. Zahov

Dacia

  1. 8B. Punoševac
  2. 21Amâncio José Pinto Fortes
  3. 26Z. Olisaev
  1. 7A. Paşcenco
  2. 17C. Diarra
  3. 19Maksym Gavrylenko
  1. 13Seoane
  2. 24A. Mamah
  3. 25S. Drašković
  4. 28C. Jalbă
  1. 16D. Railean
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What is the recent form of both teams?

Over their last 15 matches in all competitions, Shkendija have 8 wins, 5 draws and 2 defeats; Dacia have 12, 2 and 1.

The competition is shown for every match: in pre-season, several of these games are friendlies.

Shkendija
DateOpponentScoreCompetition
May 31, 2017Pelister(home)3-1First League
May 28, 2017Renova(away)4-4First League
May 21, 2017FK Rabotnicki(home)2-2First League
May 14, 2017Makedonija GjP(away)3-2First League
May 7, 2017Bregalnica Štip(home)3-0First League
Dacia
DateOpponentScoreCompetition
May 30, 2017Sheriff Tiraspol(away)2-2Super Liga
May 21, 2017Saxan(away)3-2Super Liga
May 12, 2017Dinamo-Auto(home)4-1Super Liga
May 7, 2017Speranţa Nisporeni(away)1-0Super Liga
May 3, 2017Academia(away)4-0Super Liga

How many goals should we expect?

Shkendija score 2.3 and concede 1.1 goals per match over their last 15; Dacia score 2.1 and concede 0.6.

ClubGoals scored / matchGoals conceded / matchClean sheetsBoth teams scoredOver 2.5 goals
Shkendija2.31.1310/159/15
Dacia2.10.686/158/15

Over the last 15 matches in all competitions.

A question about Shkendija vs Dacia?

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

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