Optibet Baltic Women`s Volleyball League 22/23

Optibet Baltic Women`s Volleyball League 22/23

Optibet Baltic Women`s Volleyball League 22/23 Best players SETTER
PlayerPlayed
  MatchesSetsSetter Efficiency
('#' - '/' - '=') / TOT

1

Gailiūtė Morta
(Jonavos ,,Aušrinė'' )

9

12

37.50%
(19 - 3 - 1) / 40

2

Zui Kateryna
(Kaunas-VDU)

12

27

34.38%
(43 - 6 - 4) / 96

3

Varlõgina Melissa
(TalTech/Tradehouse)

18

62

33.33%
(184 - 19 - 32) / 399

4

Sebežiova Akvile
(Jonavos ,,Aušrinė'' )

16

59

31.21%
(203 - 35 - 26) / 455

5

Pravdinskaitė Karolina
(Kaunas-VDU)

17

60

31.15%
(208 - 24 - 41) / 459

6

Reknere Elza
(RVS/LU)

18

71

28.80%
(219 - 30 - 45) / 500

7

Pulina Agnese
(VK Jelgava)

8

33

27.88%
(95 - 14 - 18) / 226

8

Vahula Häli
(Audentes SG/NK)

10

31

27.08%
(67 - 9 - 19) / 144

9

Bērziņa Jana
(VK Jelgava)

13

29

25.71%
(57 - 10 - 11) / 140

10

Loos Helena
(TalTech/Tradehouse)

3

8

25.71%
(14 - 3 - 2) / 35

11

Starkopf Ann
(TÜ/Bigbank)

14

52

24.79%
(186 - 29 - 40) / 472

12

Linde Ruta
(RSU / MSĢ )

13

32

24.08%
(73 - 13 - 14) / 191

13

Kaur Laura
(Võru VK)

12

39

21.98%
(29 - 5 - 4) / 91

14

Räim Roosmarii
(Võru VK)

14

54

21.35%
(123 - 26 - 21) / 356

15

Laanemets Kaili
(TÜ/Bigbank)

7

17

20.90%
(22 - 3 - 5) / 67

16

Toom Roosi Brigita
(Audentes SG/NK)

14

48

18.62%
(141 - 30 - 46) / 349

17

Petrova Ieva
(SuFA klubs/DU )

5

16

17.95%
(13 - 5 - 1) / 39

18

Brokane Eva Elizabete
(SuFA klubs/DU )

14

56

15.25%
(138 - 32 - 45) / 400

19

Vavere Laura Kristine
(RSU / MSĢ )

16

52

14.86%
(98 - 25 - 29) / 296

20

Visočanska Sanda
(VK Jelgava)

8

22

12.24%
(55 - 17 - 20) / 147

21

Dimitrijeva Darja
(SuFA klubs/DU )

8

27

11.76%
(20 - 7 - 3) / 85

22

Postnova Anastasija
(RSU / MSĢ )

5

12

5.88%
(12 - 7 - 2) / 51

23

Leveika Ginta
(VK Jelgava)

10

21

1.92%
(26 - 10 - 14) / 104

24

Valionytė Vaiva
(Kaunas-VDU)

1

3

0.00%
(2 - 0 - 2) / 6

25

Laurecka Dagmara Helena
(SuFA klubs/DU )

1

4

0.00%
(1 - 1 - 0) / 3

Ranking Calculation

Setter

This ranking is based on Setter data for each player with a minimum of 1 played matches and at least 10% of all sets of the team.