Baltic Women`s Volleyball League 2018-2019

Baltic Women`s Volleyball League 2018-2019

Baltic Women`s Volleyball League 2018-2019 Best players MIDDLE BLOCKER
PlayerPlayedServeServeBlockBlockAttackAttackRanking
  MS#=/TotSv ind.Sv ind.#=/TotBl ind.Bl ind.#=/TotSp ind.Sp ind.Index

1

Stauskytė Gintarė
(Alytaus “Prekyba – Parama”)

5

21

12

8

3

71

0.0164

0.0164

16

1

4

26

0.0175

0.0175

22

2

4

46

7.3043

7.3043

0.60028

2

Kasperavičiūtė Gabija
(TK Aušrinė-KKSC)

16

53

20

14

1

155

0.009

0.009

50

20

3

136

0.0213

0.0213

83

29

12

226

9.8496

9.8496

0.56504

3

Motiejūnaitė Kotryna
(TK “Kaunas”-VDU)

17

67

23

15

8

240

0.0107

0.0107

48

30

6

152

0.0165

0.0165

96

20

18

230

16.8957

16.8957

0.56228

4

Pikk Renate
(TÜ/Bigbank)

16

54

22

23

6

167

0.0111

0.0111

29

16

7

78

0.0115

0.0115

80

9

3

154

23.8442

23.8442

0.54679

5

Dudkina Viktorija
(TK “Kaunas”-VDU)

17

67

17

6

8

221

0.0086

0.0086

39

36

7

148

0.0134

0.0134

104

18

9

264

19.5417

19.5417

0.5288

6

Tarasenko Darja
(VK "miLATss")

16

61

12

14

1

198

0.0047

0.0047

46

20

0

140

0.0168

0.0168

101

25

17

259

13.8958

13.8958

0.50557

7

Loorman Mari
(TalTech/Tradehouse)

16

69

12

10

4

140

0.0053

0.0053

40

55

7

209

0.0134

0.0134

114

23

14

265

20.0491

20.0491

0.50038

8

Ozola Lasma
(Riga Volleyball School)

17

61

24

48

4

219

0.0103

0.0103

27

23

1

83

0.0099

0.0099

44

14

5

178

8.5674

8.5674

0.50007

9

Dolkart Katerina
(Alytaus “Prekyba – Parama”)

19

78

25

43

7

225

0.0091

0.0091

35

28

5

133

0.01

0.01

82

33

16

231

11.1429

11.1429

0.49472

10

Dzierkale Kristīne
(Jelgava/LLU)

12

47

14

12

4

155

0.0084

0.0084

25

11

0

88

0.0117

0.0117

71

10

18

233

8.6738

8.6738

0.49462

11

Arak Mari
(TÜ/Bigbank)

15

46

14

16

4

139

0.0075

0.0075

23

18

1

72

0.0096

0.0096

63

11

12

133

13.8346

13.8346

0.48348

12

Rozīte Simona
(Jelgava/LLU)

17

67

15

12

3

193

0.0061

0.0061

33

12

0

119

0.0113

0.0113

65

13

17

175

13.4

13.4

0.48078

13

Hollas Heleene
(TalTech/Tradehouse)

7

28

4

7

7

98

0.0084

0.0084

13

13

1

58

0.0099

0.0099

43

8

10

101

6.9307

6.9307

0.47961

14

Tammerik Sylvia
(TalTech/Tradehouse)

13

49

5

12

1

103

0.0026

0.0026

31

24

0

116

0.0132

0.0132

69

14

11

161

13.3913

13.3913

0.46167

15

Reiter Laura
(TÜ/Bigbank)

15

45

8

9

4

154

0.0051

0.0051

18

11

4

43

0.0076

0.0076

61

12

11

160

10.6875

10.6875

0.44247

16

Blaškevič Karina
(Alytaus “Prekyba – Parama”)

18

70

15

10

4

207

0.0057

0.0057

24

23

1

88

0.0072

0.0072

26

12

11

103

2.0388

2.0388

0.42882

17

Alehina Iveta
(VK "miLATss")

16

60

12

29

1

155

0.0047

0.0047

15

29

1

108

0.0055

0.0055

144

40

29

414

10.8696

10.8696

0.4257

18

Valionytė Vaiva
(TK Aušrinė-KKSC)

16

52

12

10

2

149

0.006

0.006

9

9

2

50

0.0038

0.0038

45

12

10

142

8.4225

8.4225

0.4213

19

Gordejeva Jekaterina
(Riga Volleyball School)

8

16

6

10

1

40

0.0056

0.0056

3

0

0

21

0.0024

0.0024

3

1

1

15

1.0667

1.0667

0.39326

20

Bytautaitė Jomilė
(TK “Kaunas”-VDU)

11

16

3

1

1

22

0.0022

0.0022

3

1

0

10

0.0017

0.0017

1

1

2

10

-3.2

-3.2

0.36051

21

Stamm Karmen
(TalTech/Tradehouse)

17

49

7

6

3

80

0.0032

0.0032

0

1

0

4

0

0

1

0

0

4

12.25

12.25

0.35835

22

Vengerfeldt Katariina
(TÜ/Bigbank)

5

5

1

1

0

5

0.0016

0.0016

0

1

0

2

0

0

1

0

0

2

2.5

2.5

0.3451

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:  5