Team TSM FEM on Valorant
Team TSM Female
-
United States
-
-
-
$ 31850
45
Win
27
Lose
62%
Win Rate
-
Rating
TSM FEM Roster
TSM FEM Achievements
Place | Date | Tournament | Prize |
---|---|---|---|
7
|
16.10.22 | $ 1 000 | |
5
|
07.08.22 | $ 250 | |
3rd |
10.07.22 | $ 7 000 | |
5
|
10.04.22 | $ 3 000 | |
2nd |
17.02.22 | $ 600 | |
1st |
06.12.21 | $ 3 000 | |
2nd |
21.11.21 | $ 2 500 | |
7
|
04.10.21 | $ 1 000 | |
2nd |
23.08.21 | $ 3 000 | |
2nd |
19.07.21 | $ 3 000 | |
5
|
27.06.21 | $ 3 000 |
Other players of TSM FEM Team
Recent TSM FEM matches at Valorant
Date | Rival | Score | Tournament | Date / Results | |
---|---|---|---|---|---|
12.10.22 |
TSM FEM
Team TSM Female |
0
:
2
|
VCT 2022 GC NA S3 |
Lose
|
|
12.10.22 |
TSM FEM
Team TSM Female |
0
:
2
|
VCT 2022 GC NA S3 |
Lose
|
|
08.10.22 |
TSM FEM
Team TSM Female |
2
:
1
|
VCT 2022 GC NA S3 |
Win
|
|
08.10.22 |
TSM FEM
Team TSM Female |
2
:
0
|
VCT 2022 GC NA S3 |
Win
|
|
28.09.22 |
TSM FEM
Team TSM Female |
0
:
1
|
KGCM 2022 September |
Lose
|
|
28.09.22 |
TSM FEM
Team TSM Female |
0
:
1
|
KGCM 2022 September |
Lose
|
|
27.09.22 |
TSM FEM
Team TSM Female |
0
:
1
|
NNDI |
Lose
|
|
27.09.22 |
TSM FEM
Team TSM Female |
1
:
0
|
NNDI |
Win
|
|
27.09.22 |
TSM FEM
Team TSM Female |
1
:
2
|
NNDI |
Lose
|
|
26.09.22 |
TSM FEM
Team TSM Female |
1
:
0
|
NNDI |
Win
|
Team Earnings
Data | Earnings |
---|---|
2022 | $ 11850 |
2021 | $ 20000 |
About The TSM FEM Team
- The current total win rate is 62%. Last year the winning percentage was 50%.
- In the last tournament VCT 2022 GC NA S3, team TSM FEM took 7st place, winning $ 1 000 of the total prize pool.
- TSM FEM Valorant team represents United States.
- The current roster of the TSM FEM team - athxna, mleQT, EllieTwitches, bungee, dodonut.
- The team's approximate total earnings are $ 31850.
- Their last match was on 12.10.22 and ended with a 2 - 0 result at the VCT 2022 GC NA S3 tournament. The opponents were the IMT team. The result for TSM FEM - Lose.
- For all time the team has played 72 matches and they won 45 of them (according to our data).