Get R50 free + 25 spins
Get R50 free + 25 spins at Easybet for new accounts, no deposit needed; terms apply 18+Open account

Home / Bundesliga / Köln vs Augsburg

Köln vs Augsburg

Köln vs Augsburg kicks off at a time to be confirmed on Saturday, 10 April 2027 in the Bundesliga.

Kick-off: TBC · Saturday, 10 April 2027

The licensed books price this match in the days before kick-off; the boxes appear here then.

Bet on Köln vs Augsburg at Easybet

Easybet: R50 free bet and 25 free spins, no deposit needed for new accounts. Terms on their site.

Prediction from the model, not a tipster

Köln 30%Draw 24%Augsburg 46%

Augsburg: a lean at 46%. The model has Köln at 30%, the draw at 24% and Augsburg at 46%, from an expected margin of -0.3 goals to the home side. The licensed books price this match in the days before kick-off; the price appears here then.

Bet on Augsburg at Easybet

A rating model over past results; it can be wrong and is not advice. Its record.

Köln

13th in the Bundesliga · 4 pts from 4 · GD -3

  • L
  • D
  • L
  • W
  • L
  • L

Last 6: 1W 1D 4L, scored 8, conceded 17.

Augsburg

4th in the Bundesliga · 7 pts from 4 · GD +5

  • L
  • D
  • W
  • W
  • L
  • W

Last 6: 3W 1D 2L, scored 14, conceded 11.

Going into the match

Köln go into this 13th in the Bundesliga on 4 points from 4; Augsburg are 4th on 7.

Over their last 6 Köln have taken 4 points, scoring 8; Augsburg 10 points over their last 6, scoring 14.

Last 6 meetings: Köln 2, Augsburg 1, drawn 3. Both sides scored in 5, and 2 went over 2.5 goals.

Head to head

DateMatchCompetition
27 Feb 26Augsburg 2-0 KölnBundesliga
18 Oct 25Köln 1-1 AugsburgBundesliga
31 Mar 24Augsburg 1-1 KölnBundesliga
04 Nov 23Köln 1-1 AugsburgBundesliga
08 Apr 23Augsburg 1-3 KölnBundesliga
16 Oct 22Köln 3-2 AugsburgBundesliga

Bet on this match at Easybet

Next in the Bundesliga

Winners know when to stop. 18+. Gambling can be addictive. National Responsible Gambling Programme 0800 006 008 (free, 24 hours). Only bet with bookmakers licensed in South Africa. Predictions here are a model's output from past results; they can be wrong and are not advice.