Team AST FEM on CS:GO
Astralis Women
-
Denmark
-
-
-
$ 2600
10
Win
22
Lose
31%
Win Rate
-
Rating
AST FEM Roster
aurora
Aurora Lyngdal
Josefine
Josefine Jensen
marie
Marie Ellen Toft Sørensen
Ismo
Isabella Ferslev
anja
Anja Soelberg
AST FEM Achievements
Place | Date | Tournament | Prize |
---|---|---|---|
11
|
31.05.24 |
ESL Impact S5 |
$ 850 |
9
|
04.06.23 | $ 750 | |
2nd |
16.01.23 |
EICC Winter 2023 #1 |
$ 250 |
7
|
27.11.22 | $ 750 |
Other players of AST FEM Team
Recent AST FEM matches at CS:GO
Date | Rival | Score | Tournament | Date / Results | |
---|---|---|---|---|---|
19.04.24 |
AST FEM
Astralis Women |
ENCE FEM ENCE Athena |
-
|
ESL Impact S5 |
Lose
|
14.04.24 |
AST FEM
Astralis Women |
0
:
2
|
EPL FEM S1 |
Lose
|
|
13.04.24 |
AST FEM
Astralis Women |
0
:
2
|
EPL FEM S1 |
Lose
|
|
12.04.24 |
AST FEM
Astralis Women |
PER FEM Permitta W |
-
|
EPL FEM S1 |
Win
|
11.04.24 |
AST FEM
Astralis Women |
Pigeons Team Pigeons |
-
|
ESL Impact S5 |
Lose
|
10.04.24 |
AST FEM
Astralis Women |
Pigeons Team Pigeons |
-
|
EPL FEM S1 |
Lose
|
08.04.24 |
AST FEM
Astralis Women |
PER FEM Permitta W |
-
|
EPL FEM S1 |
Win
|
03.04.24 |
AST FEM
Astralis Women |
0
:
2
|
ESL Impact S5 |
Lose
|
|
21.03.24 |
AST FEM
Astralis Women |
0
:
2
|
ESL Impact S5 |
Lose
|
|
06.03.24 |
AST FEM
Astralis Women |
0
:
2
|
ESL Impact S5 |
Lose
|
Team Earnings
Data | Earnings |
---|---|
2024 | $ 850 |
2023 | $ 1000 |
2022 | $ 750 |
About The AST FEM Team
- In the last tournament ESL Impact S5, team AST FEM took 11st place, winning $ 850 of the total prize pool.
- The current total win rate is 31%. Last year the winning percentage was 20%.
- The current roster of the AST FEM team - aurora, Josefine, marie, Ismo, anja.
- The team's approximate total earnings are $ 2600.
- Their last match was on 19.04.24 and ended with a 2 - 1 result at the ESL Impact S5 tournament. The opponents were the ENCE FEM team. The result for AST FEM - Lose.
- AST FEM CS:GO team represents Denmark.
- For all time the team has played 32 matches and they won 10 of them (according to our data).