Baltic Women`s Volleyball League 2020-2021

Baltic Women`s Volleyball League 2020-2021

Baltic Women`s Volleyball League 2020-2021 Best players WING SPIKER
PlayerPlayedServeServeBlockBlockAttackAttackReceptionReceptionRanking
  MS#=/TotSv ind.Sv ind.#=/TotBl ind.Bl ind.#=/TotSp ind.Sp ind.#=/TotRc ind.Rc ind.Index

1

Peit Nette
(TalTech/Tradehouse)

6

20

18

7

3

105

0.0251

0.0251

6

6

0

24

0.0072

0.0072

70

9

3

167

6.9461

6.9461

15

13

2

99

0.2929

0.2929

0.27462

2

Kiisk Ingrid
(TÜ/Bigbank)

3

10

8

9

3

45

0.0257

0.0257

2

6

0

16

0.0047

0.0047

36

4

6

79

3.2911

3.2911

14

5

2

59

0.322

0.322

0.25176

3

Bogdanovič Viltė
(TK “Kaunas”-VDU)

7

23

13

11

1

100

0.0138

0.0138

11

9

3

29

0.0108

0.0108

107

22

16

249

6.3735

6.3735

29

9

10

118

0.4068

0.4068

0.2354

4

Kramena Kristine
(Riga Volleyball School/LU)

8

28

14

9

5

122

0.0154

0.0154

6

4

2

20

0.0049

0.0049

87

15

12

248

6.7742

6.7742

28

8

4

147

0.517

0.517

0.22859

5

Makauskaitė Eglė
(TK “Kaunas”-VDU)

7

23

9

9

5

71

0.0138

0.0138

5

12

1

27

0.0049

0.0049

41

10

5

148

4.0405

4.0405

29

12

2

124

0.4355

0.4355

0.21033

6

Šimkuse Liene
(Jelgava)

7

29

8

14

2

102

0.0079

0.0079

4

13

0

25

0.0032

0.0032

96

16

14

225

8.5067

8.5067

26

5

4

112

0.5625

0.5625

0.20465

7

Kuivonen Eva Liisa
(Audentes SG/NK)

5

15

3

9

3

61

0.0085

0.0085

9

8

1

24

0.0127

0.0127

47

11

13

139

2.482

2.482

8

7

2

53

0.1132

0.1132

0.2019

8

Plāte Līga
(Jelgava)

5

17

12

7

1

71

0.0152

0.0152

0

5

1

9

0

0

50

10

13

137

3.3504

3.3504

8

14

0

82

0.1951

0.1951

0.2002

9

Smilškalne Elza
(RSU/MVS)

4

17

9

22

0

59

0.0122

0.0122

1

2

0

4

0.0014

0.0014

71

23

7

163

4.2761

4.2761

2

2

0

38

0.4737

0.4737

0.19606

10

Mõrd Maren
(Audentes SG/NK)

5

15

8

5

2

47

0.0141

0.0141

2

4

0

7

0.0028

0.0028

30

12

9

105

1.2857

1.2857

25

15

5

99

0.2323

0.2323

0.19437

11

Danilovaite Auguste
(Alytaus “Prekyba – Parama”)

1

3

1

2

1

5

0.014

0.014

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.18209

12

Ergle Amanda Nikola
(Riga Volleyball School/LU)

8

25

6

8

3

77

0.0073

0.0073

2

11

0

19

0.0016

0.0016

52

16

12

150

4

4

21

5

2

127

0.3307

0.3307

0.18025

13

Jurdža Anna
(RSU/MVS)

4

17

6

8

0

65

0.0081

0.0081

1

3

0

8

0.0014

0.0014

39

12

9

108

2.8333

2.8333

5

8

1

72

0.5417

0.5417

0.17832

14

Černiševa Diana
(VK "miLATss")

3

11

2

3

0

45

0.0042

0.0042

4

0

0

6

0.0085

0.0085

24

8

1

71

2.3239

2.3239

2

12

1

61

0.2787

0.2787

0.17829

15

Gull Gerli
(TÜ/Bigbank)

2

6

2

3

0

16

0.0081

0.0081

1

2

0

6

0.004

0.004

10

1

2

32

1.3125

1.3125

4

2

1

22

0.2273

0.2273

0.1772

16

Huik Annabel
(TÜ/Bigbank)

6

17

2

6

2

44

0.005

0.005

2

4

1

10

0.0025

0.0025

38

5

5

101

4.7129

4.7129

11

9

5

74

0.0676

0.0676

0.1768

17

Jursone Inese
(Jelgava)

4

16

3

9

0

40

0.0038

0.0038

3

3

0

10

0.0038

0.0038

46

10

7

105

4.419

4.419

11

7

0

71

0.4366

0.4366

0.17572

18

Cehanoviča Ilona
(Jelgava)

7

28

7

18

2

86

0.0071

0.0071

9

13

0

49

0.0071

0.0071

29

6

2

62

9.4839

9.4839

2

1

1

10

0.1

0.1

0.17515

19

Pertens Silvia
(TalTech/Tradehouse)

5

17

2

8

0

58

0.0028

0.0028

4

5

0

12

0.0056

0.0056

45

13

7

123

3.4553

3.4553

11

8

2

59

0.2881

0.2881

0.17199

20

Cerņavska Marija
(VK "miLATss")

3

11

1

1

0

30

0.0021

0.0021

4

2

0

12

0.0085

0.0085

23

8

4

66

1.8333

1.8333

0

8

1

59

0.4746

0.4746

0.17029

21

Grudzinskaitė Gerda
(Alytaus “Prekyba – Parama”)

4

12

2

6

0

38

0.0036

0.0036

1

0

0

4

0.0018

0.0018

37

5

8

93

3.0968

3.0968

6

3

1

38

0.4737

0.4737

0.16588

22

Hansman Helerin
(TÜ/Bigbank)

1

3

1

1

0

4

0.008

0.008

0

1

0

1

0

0

0

0

0

7

0

0

2

3

1

19

0.1579

0.1579

0.16386

23

Kiaušaitė Kamilė
(TK “Kaunas”-VDU)

6

10

5

5

0

28

0.0056

0.0056

3

4

0

10

0.0034

0.0034

6

4

1

26

0.3846

0.3846

6

2

2

41

0.3415

0.3415

0.1638

24

Embrekte Anna
(RSU/MVS)

4

15

2

1

0

34

0.0027

0.0027

2

0

0

2

0.0027

0.0027

27

6

7

75

2.8

2.8

3

2

0

11

0.1818

0.1818

0.16299

25

Macenko Inga
(VK "miLATss")

3

11

2

1

0

29

0.0042

0.0042

2

3

0

10

0.0042

0.0042

10

1

2

31

2.4839

2.4839

2

3

1

41

0.3659

0.3659

0.16143

26

Lebedeva Elina
(Audentes SG/NK)

3

8

0

6

1

22

0.0025

0.0025

1

2

0

6

0.0025

0.0025

23

8

1

55

2.0364

2.0364

14

2

4

60

0.3833

0.3833

0.15998

27

Hollas Salme Adeele
(TÜ/Bigbank)

6

12

2

2

0

29

0.0025

0.0025

2

4

0

7

0.0025

0.0025

12

5

4

44

0.8182

0.8182

11

5

4

68

0.2647

0.2647

0.15568

28

Daunytė Agnė
(Alytaus “Prekyba – Parama”)

1

3

0

1

0

10

0

0

1

0

0

2

0.007

0.007

5

3

2

20

0

0

0

2

0

6

0.1667

0.1667

0.15359

29

Grudzinskaitė Rugilė
(Alytaus “Prekyba – Parama”)

3

9

0

0

1

23

0.0024

0.0024

0

3

0

4

0

0

18

10

4

65

0.5538

0.5538

6

3

0

32

0.375

0.375

0.15008

30

Siimson Sonja
(Audentes SG/NK)

3

7

1

2

0

9

0.0023

0.0023

0

0

0

0

0

0

0

1

2

5

-4.2

-4.2

1

3

1

8

-0.25

-0.25

0.14718

31

Rahuoja Ragne
(Audentes SG/NK)

4

8

0

2

0

19

0

0

0

3

0

4

0

0

12

4

1

31

1.8065

1.8065

1

1

0

3

0

0

0.14686

32

Säästla Maria
(TalTech/Tradehouse)

3

5

0

0

0

13

0

0

1

0

0

4

0.0024

0.0024

1

1

0

3

0

0

1

1

0

5

0.4

0.4

0.1452

33

Kukučionytė Viktorija
(TK “Kaunas”-VDU)

5

6

1

0

0

6

0.0014

0.0014

0

0

0

0

0

0

2

0

0

3

4

4

0

1

1

4

-0.5

-0.5

0.1447

34

Konkina Anastasiya
(TK “Kaunas”-VDU)

4

7

0

2

0

12

0

0

0

3

0

6

0

0

4

0

2

22

0.6364

0.6364

0

0

0

0

0

0

0.14307

35

Bradko Viktorija
(Riga Volleyball School/LU)

1

1

0

0

0

0

0

0

0

0

0

0

0

0

1

0

1

3

0

0

1

1

0

3

0.3333

0.3333

0.14174

36

Gerke Karina
(VK "miLATss")

2

3

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.14105

37

Lember Nora
(Audentes SG/NK)

2

5

0

0

0

5

0

0

0

0

0

0

0

0

3

1

0

8

1.25

1.25

1

1

1

6

0.1667

0.1667

0.14105

38

Laigu Liis
(TÜ/Bigbank)

1

1

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

1

1

1

0

0

0

0

0

0

0.14105

39

Nemme Madara
(Jelgava)

3

8

2

3

0

7

0.0034

0.0034

0

0

0

0

0

0

3

1

0

6

2.6667

2.6667

3

2

0

18

0.2222

0.2222

0.14105

40

Leite Jana
(Jelgava)

4

5

0

0

0

9

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0.14105

41

Rauluševičiūtė Vilūnė
(TK “Kaunas”-VDU)

1

1

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.14105

42

Želvyte Aurelia
(Alytaus “Prekyba – Parama”)

1

1

0

1

0

1

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0.14105

Ranking Calculation

Wing-spiker

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)

  • Reception Index (Rc ind.): positive receptions minus negative receptions divided the total receptions (ranking is available only if the player has made at least three receptions 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 Receptions per set:  3

  • Minimum number of Spikes per set:  3

Serve

  • # serve ace

  • / half point

  • = serve error

Reception

  • # perfect

  • / half point

  • = error

Attack

  • # point

  • / blocked

  • = error

Block

  • # point

  • / Net touch

  • = hand out

Filters applied

  • Minimum number of Matches played:  1