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 MIDDLE BLOCKER
PlayerPlayedServeServeBlockBlockAttackAttackRanking
  MS#=/TotSv ind.Sv ind.#=/TotBl ind.Bl ind.#=/TotSp ind.Sp ind.Index

1

Põld Kätriin
(TalTech/Tradehouse)

23

79

25

28

11

264

0.0101

0.0101

56

61

4

207

0.0157

0.0157

160

19

13

319

31.6991

31.6991

0.58052

2

Gabranova Kristine
(SUFA klubs/DU)

16

67

30

16

11

223

0.0148

0.0148

32

59

0

204

0.0116

0.0116

105

14

19

292

16.5205

16.5205

0.5662

3

Struka Karmena
(RSU/MSG)

20

75

26

25

11

266

0.0113

0.0113

52

37

1

135

0.016

0.016

138

33

21

369

17.0732

17.0732

0.56494

4

Hollas Salme Adeele
(Barrus/Võru VK)

16

60

18

14

7

213

0.0099

0.0099

41

30

0

125

0.0162

0.0162

169

34

23

455

14.7692

14.7692

0.54919

5

Kasperavičiūtė Gabija
(Kaunas-VDU)

20

67

19

33

4

188

0.0073

0.0073

56

21

0

118

0.0179

0.0179

93

24

7

211

19.6872

19.6872

0.54697

6

Pill Liisbet
(TÜ/Bigbank)

21

78

21

47

13

230

0.0098

0.0098

49

63

1

176

0.0141

0.0141

110

23

19

256

20.7187

20.7187

0.54601

7

Arak Mari
(TÜ/Bigbank)

21

77

19

27

18

264

0.0106

0.0106

41

54

1

164

0.0118

0.0118

100

25

12

212

22.8821

22.8821

0.54274

8

Vagele Junora
(Riga Volleyball School/LU)

23

72

26

41

12

169

0.0097

0.0097

42

44

1

151

0.0107

0.0107

72

21

10

156

18.9231

18.9231

0.51936

9

Peit Eliisa
(TalTech/Tradehouse)

14

44

21

13

3

168

0.0106

0.0106

21

16

1

74

0.0093

0.0093

63

15

5

153

12.366

12.366

0.50591

10

Karvelytė Aurėja
(Jonavos ,,Aušrinė'' )

18

64

19

30

5

260

0.0083

0.0083

32

23

1

88

0.0111

0.0111

47

8

10

124

14.9677

14.9677

0.50221

11

Galubaitė Roberta
(Jonavos ,,Aušrinė'' )

10

35

7

6

0

97

0.0046

0.0046

25

11

0

51

0.0165

0.0165

31

4

4

63

12.7778

12.7778

0.50033

12

Urbāne Monta
(VK Jelgava)

10

38

3

10

2

98

0.0031

0.0031

31

15

0

91

0.019

0.019

76

14

10

179

11.0391

11.0391

0.4999

13

Sauša Inga
(SUFA klubs/DU)

1

4

0

1

1

8

0.0058

0.0058

3

14

0

30

0.0174

0.0174

7

1

2

17

0.9412

0.9412

0.49456

14

Zaķe Ginta
(VK Jelgava)

5

19

9

7

4

54

0.0141

0.0141

5

3

0

28

0.0054

0.0054

9

2

1

40

2.85

2.85

0.49281

15

Motiejūnaitė Kotryna
(Kaunas-VDU)

20

69

13

29

1

215

0.0044

0.0044

33

35

1

128

0.0103

0.0103

114

18

9

219

27.411

27.411

0.4856

16

Juršāne-Piņķe Alise
(RSU/MSG)

12

44

12

27

6

108

0.0094

0.0094

19

17

1

58

0.0099

0.0099

39

14

11

132

4.6667

4.6667

0.48457

17

Valionytė Vaiva
(Kaunas-VDU)

20

51

19

19

6

152

0.0079

0.0079

23

19

0

75

0.0073

0.0073

59

6

5

138

17.7391

17.7391

0.4787

18

Rozīte Simona
(VK Jelgava)

20

73

11

23

8

282

0.0056

0.0056

39

34

1

147

0.0115

0.0115

73

16

19

199

13.9397

13.9397

0.47846

19

Ilves Hanna-Liisa
(Barrus/Võru VK)

15

51

15

6

1

160

0.0069

0.0069

18

25

0

88

0.0077

0.0077

130

14

14

351

14.8205

14.8205

0.46664

20

Rita Anna
(Riga Volleyball School/LU)

19

56

10

26

4

101

0.0043

0.0043

34

30

0

123

0.0104

0.0104

53

13

5

127

15.4331

15.4331

0.46268

21

Shramko Marina
(Jonavos ,,Aušrinė'' )

17

52

12

13

2

137

0.0051

0.0051

22

14

0

67

0.0079

0.0079

53

11

5

121

15.9008

15.9008

0.45416

22

Sova Johanna
(TalTech/Tradehouse)

17

45

5

12

0

80

0.002

0.002

29

33

1

103

0.0114

0.0114

51

5

9

111

15

15

0.44766

23

Tugedam Karmen
(Audentes SG/NK)

18

69

8

30

1

151

0.003

0.003

17

23

0

58

0.0057

0.0057

27

8

5

76

12.7105

12.7105

0.41602

24

Vengerfeldt Katariina
(TÜ/Bigbank)

3

8

2

3

1

19

0.0062

0.0062

2

1

0

6

0.0041

0.0041

9

0

2

18

3.1111

3.1111

0.4152

25

Bredika Liva
(VK Jelgava)

9

23

2

11

2

46

0.0029

0.0029

11

9

0

43

0.0079

0.0079

32

5

11

93

3.957

3.957

0.41226

26

Kravceviča Evelīna
(SUFA klubs/DU)

15

34

8

7

1

76

0.0036

0.0036

10

25

0

75

0.004

0.004

18

6

4

40

6.8

6.8

0.39828

27

Ivanova Anželika
(Riga Volleyball School/LU)

20

54

6

36

2

102

0.0024

0.0024

14

28

0

94

0.0041

0.0041

28

11

4

79

8.8861

8.8861

0.39276

28

Soodla Heleri
(Audentes SG/NK)

16

49

5

26

1

89

0.0023

0.0023

5

9

0

19

0.0019

0.0019

20

3

3

55

12.4727

12.4727

0.38511

29

Provotorov Liisa
(Barrus/Võru VK)

7

14

4

6

0

34

0.0034

0.0034

5

2

1

11

0.0043

0.0043

8

8

6

48

-1.75

-1.75

0.38362

30

Tralmaka Emīlija
(RSU/MSG)

2

7

2

3

0

26

0.0057

0.0057

0

4

0

16

0

0

8

6

3

27

-0.2593

-0.2593

0.37875

31

Regute Amanda
(VK Jelgava)

1

4

1

0

0

14

0.0049

0.0049

0

1

0

3

0

0

5

3

1

15

0.2667

0.2667

0.37309

32

Mikoliūnaitė Greta
(Jonavos ,,Aušrinė'' )

10

20

5

0

0

66

0.0029

0.0029

3

6

0

16

0.0018

0.0018

10

2

5

44

1.3636

1.3636

0.36934

33

Sild Kristel
(TÜ/Bigbank)

1

1

0

0

0

0

0

0

0

0

0

1

0

0

2

0

0

3

0.6667

0.6667

0.3336

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