OPTIBET Baltic Women`s Volleyball League 24/25

Baltic Women`s Volleyball League 24/25

OPTIBET Baltic Women`s Volleyball League 24/25 Best players MIDDLE BLOCKER
PlayerPlayedServeServeBlockBlockAttackAttackRanking
  MS#=/TotSv ind.Sv ind.#=/TotBl ind.Bl ind.#=/TotSp ind.Sp ind.Index

1

Struka Karmena
(RSU/MSG)

1

3

2

2

1

7

0.0261

0.0261

2

0

0

5

0.0174

0.0174

8

0

1

9

2.3333

2.3333

0.67089

2

Moor Kristel
(Rae Spordikool/VIASTON)

2

6

6

2

1

25

0.0256

0.0256

3

0

0

3

0.011

0.011

11

3

2

33

1.0909

1.0909

0.62661

3

Kiviloo Liis
(Rae Spordikool/VIASTON)

3

9

5

1

4

36

0.0225

0.0225

2

2

0

5

0.005

0.005

28

3

3

44

4.5

4.5

0.568

4

Kasperavičiūtė Gabija
(Kaunas-VDU)

3

11

4

3

1

35

0.0096

0.0096

11

6

1

27

0.0212

0.0212

25

4

1

50

4.4

4.4

0.55941

5

Sola Līva
(Riga Volleyball School/LU)

5

17

7

6

2

49

0.0107

0.0107

14

15

1

47

0.0166

0.0166

10

2

1

28

4.25

4.25

0.53832

6

Kask Mirjam Karoliine Kask
(TalTech/Macta Beauty)

3

14

6

6

1

54

0.012

0.012

6

11

0

29

0.0103

0.0103

17

5

2

46

3.0435

3.0435

0.50618

7

Urbāne Monta
(VK Jelgava)

2

6

2

1

0

12

0.0081

0.0081

4

5

0

15

0.0162

0.0162

4

3

0

19

0.3158

0.3158

0.50553

8

Talvar Anu
(TalTech/Macta Beauty)

3

14

3

5

3

52

0.0103

0.0103

7

9

1

34

0.012

0.012

7

2

0

18

3.8889

3.8889

0.50381

9

Arak Mari
(TÜ/Bigbank)

2

7

1

3

2

28

0.0096

0.0096

4

3

0

14

0.0129

0.0129

5

2

0

13

1.6154

1.6154

0.49989

10

Tugedam Karmen
(Audentes SG/NK)

3

10

2

7

2

24

0.0097

0.0097

5

0

0

6

0.0121

0.0121

8

6

2

27

0

0

0.49254

11

Piantkovska Yana
(Jonavos ,,Aušrinė'' )

4

14

4

2

0

44

0.0068

0.0068

6

7

0

17

0.0103

0.0103

16

5

1

29

4.8276

4.8276

0.46386

12

Valionytė Vaiva
(Kaunas-VDU)

3

9

2

2

3

41

0.0096

0.0096

3

5

0

12

0.0058

0.0058

13

1

1

22

4.5

4.5

0.45848

13

Soodla Heleri
(Audentes SG/NK)

4

13

0

2

3

26

0.005

0.005

3

4

0

13

0.005

0.005

12

5

0

26

3.5

3.5

0.4111

14

Liepa Samanta
(Riga Volleyball School/LU)

5

14

6

10

1

37

0.0083

0.0083

1

8

0

14

0.0012

0.0012

5

1

0

12

4.6667

4.6667

0.40859

15

Juršāne-Piņķe Alise
(RSU/MSG)

1

2

1

0

0

2

0.0087

0.0087

0

0

0

1

0

0

2

0

0

3

1.3333

1.3333

0.40709

16

Rozīte Simona
(VK Jelgava)

1

3

0

0

1

11

0.0076

0.0076

0

1

0

2

0

0

6

0

1

11

1.3636

1.3636

0.39752

17

Ivanova Anželika
(RSU/MSG)

1

1

0

1

0

5

0

0

1

0

0

2

0.0087

0.0087

1

0

0

3

0.3333

0.3333

0.38579

18

Stamm Kertu
(Audentes SG/NK)

2

4

0

2

0

8

0

0

2

1

0

4

0.0062

0.0062

3

1

0

6

1.3333

1.3333

0.37242

19

Mitiakina Simona
(Riga Volleyball School/LU)

4

12

1

8

1

22

0.0028

0.0028

2

5

1

11

0.0028

0.0028

4

0

0

7

6.8571

6.8571

0.3722

20

Galubaitė Roberta
(Kaunas-VDU)

3

7

0

1

1

18

0.0019

0.0019

0

0

0

3

0

0

7

0

1

12

3.5

3.5

0.35403

21

Sild Kristel
(TÜ/Bigbank)

1

1

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

2

0

0

0.33246

22

Bukovska Adriana
(VK Jelgava)

2

2

0

1

0

3

0

0

0

1

0

2

0

0

0

1

0

4

-0.5

-0.5

0.3316

23

Maļina Anabella
(Riga Volleyball School/LU)

5

7

0

5

0

6

0

0

0

1

0

1

0

0

2

3

1

12

-1.1667

-1.1667

0.33046

Ranking Calculation

Middle-Blocker

the ranking takes into account:

  • Serve Index (Sv ind.): positive serves divided the total points of both teams (ranking is available only if the player has made at least one serve per set)

  • Attack Index (Sp ind.): positive attacks minus negative attacks divided the total attacks (ranking is available only if the player has made at least three attacks per set)

  • Block Index (Bl ind.): positive blocks divided the total points of both teams

The final ranking is based on the final “index” which determines the impact of the role on the game, in other words the importance of the role towards the win probability. This final Index is calculated considering the indexes for each single skill (“ind.” columns) and a coefficient which indicates the “importance” of the role to determine the probability of success for the team. Each single skill index is calculated considering the positive and negative skills based on the number of points played from the teams and multiplied for a coefficient which indicates the importance of the skill for that role to determine the probability of success for the team. The icons next to each skill column give an idea about the “weight” of the skill determining the probability of success for the team in this role. The final Index is calculated also considering the following criteria:

  • Minimum number of Serves per set:  1

  • Minimum number of Spikes per set:  1

Serve

  • # serve ace

  • / half point

  • = serve error

Attack

  • # point

  • / blocked

  • = error

Block

  • # point

  • / Net touch

  • = hand out

Filters applied

  • Minimum number of Matches played:  1