Match

Jägersburg vs Mülheim-Kärlich: result 1-1 and analysis

Oberliga - Relegation RoundRh/Pf/S - Relegation Round - 5Kick-off on April 8, 2023 at 01:30 PM (UTC)Alois-Omlor-Sportpark, Homburg

Jägersburg
1-1
Mülheim-Kärlich

Full time

What was the result of Jägersburg vs Mülheim-Kärlich?

Jägersburg and Mülheim-Kärlich drew 1-1 on April 8, 2023.

What is the recent form of both teams?

Over their last 15 matches in all competitions, Jägersburg have 4 wins, 5 draws and 6 defeats; Mülheim-Kärlich have 0, 5 and 10.

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

Jägersburg
DateOpponentScoreCompetition
Apr 1, 2023Alemannia Waldalgesheim(away)2-2Oberliga - Relegation Round
Mar 25, 2023Eisbachtal(home)2-0Oberliga - Relegation Round
Mar 18, 2023Pfeddersheim(away)2-5Oberliga - Relegation Round
Mar 11, 2023TuS Kirchberg(home)3-2Oberliga - Relegation Round
Feb 25, 2023SV Morlautern(home)0-0Oberliga - Rheinland-Pfalz / Saar
Mülheim-Kärlich
DateOpponentScoreCompetition
Apr 1, 2023Elversberg II(home)0-1Oberliga - Relegation Round
Mar 24, 2023SV Morlautern(away)0-5Oberliga - Relegation Round
Mar 18, 2023Dudenhofen(home)0-1Oberliga - Relegation Round
Mar 11, 2023Hertha Wiesbach(away)3-3Oberliga - Relegation Round
Feb 26, 2023TuS Koblenz(home)1-6Oberliga - Rheinland-Pfalz / Saar

How many goals should we expect?

Jägersburg score 1.6 and concede 2.1 goals per match over their last 15; Mülheim-Kärlich score 0.7 and concede 2.4.

ClubGoals scored / matchGoals conceded / matchClean sheetsBoth teams scoredOver 2.5 goals
Jägersburg1.62.1310/1510/15
Mülheim-Kärlich0.72.425/157/15

Over the last 15 matches in all competitions.

Where do both teams sit in the table?

Jägersburg sit 7th in Oberliga - Relegation Round (37 points from 22 matches) and Mülheim-Kärlich sit 11th (20 points from 22 matches).

2023 standings
#ClubPWDLGFGAPts
7Jägersburg22985566737
11Mülheim-Kärlich222713327820

A question about Jägersburg vs Mülheim-Kärlich?

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

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