Credit24 Champions League 18/19

Credit24 Champions League Reg. Season

Credit24 Champions League 18/19 Best players MIDDLE BLOCKER
PlayerPlayedBlockBlockServeServeAttackAttackRanking
  MS#=/TotBl ind.Bl ind.#=/TotSv ind.Sv ind.#=/TotSp ind.Sp ind.Index

1

Kivisild Meelis
(BIGBANK Tartu)

23

73

62

54

22

164

0.0167

0.0167

24

61

7

272

0.0084

0.0084

105

6

14

168

36.9345

36.9345

0.51509

2

Rannar Reimo
(RAKVERE VK)

23

81

50

15

7

95

0.0134

0.0134

17

13

14

314

0.0083

0.0083

110

12

9

188

38.3457

38.3457

0.49709

3

Svans Toms
(PÄRNU VK)

23

85

48

56

0

175

0.0127

0.0127

30

61

11

303

0.0109

0.0109

101

11

14

195

33.1282

33.1282

0.49702

4

Nazarovs Antons
(RTU/Robežsardze)

25

94

69

33

1

160

0.0164

0.0164

16

27

3

310

0.0045

0.0045

104

31

7

194

31.9794

31.9794

0.48534

5

Blumbergs Vladislavs
(SK Jēkabpils Lūši)

23

89

54

87

5

189

0.0135

0.0135

22

90

7

290

0.0072

0.0072

150

29

22

271

32.5129

32.5129

0.48257

6

Palmar Harri
(SAAREMAA VK)

23

79

46

53

3

145

0.0121

0.0121

22

41

10

289

0.0084

0.0084

115

18

9

210

33.1048

33.1048

0.48072

7

Adamovics Zigurds
(SK Jēkabpils Lūši)

17

67

39

70

5

169

0.0131

0.0131

14

60

4

212

0.0061

0.0061

125

10

15

210

31.9048

31.9048

0.47359

8

Popman Joonas
(RAKVERE VK)

23

82

41

23

8

107

0.011

0.011

17

30

2

275

0.0051

0.0051

112

17

16

205

31.6

31.6

0.45532

9

Kalde Ragnar
(TalTech)

22

83

41

36

2

122

0.0114

0.0114

16

41

2

266

0.005

0.005

100

19

13

205

27.5317

27.5317

0.45053

10

Varblane Mihkel
(SELVER Tallinn)

19

72

38

50

3

129

0.0113

0.0113

14

26

9

252

0.0068

0.0068

91

18

13

195

22.1538

22.1538

0.44998

11

Ennemuist Siim
(PÄRNU VK)

12

38

24

16

4

53

0.013

0.013

7

9

3

136

0.0054

0.0054

53

6

5

95

16.8

16.8

0.44366

12

Tanila Mihkel
(SAAREMAA VK)

14

45

22

54

1

120

0.0092

0.0092

12

36

9

155

0.0088

0.0088

55

5

11

94

18.6702

18.6702

0.44176

13

Slavēns Gatis
(VK BIOLARS/Jelgava)

17

61

35

13

0

84

0.013

0.013

10

26

1

189

0.0041

0.0041

80

18

14

156

18.7692

18.7692

0.44099

14

Naaber Mart
(BIGBANK Tartu)

13

41

20

4

13

49

0.0093

0.0093

7

20

5

119

0.0056

0.0056

85

8

3

140

21.6714

21.6714

0.43151

15

Vilmanis Raimonds
(Daugavpils Universitāte)

19

68

38

16

4

105

0.0122

0.0122

6

9

2

217

0.0026

0.0026

98

15

23

214

19.0654

19.0654

0.42926

16

Callaway Matthew Thomas
(SELVER Tallinn)

16

59

29

29

8

111

0.0108

0.0108

7

29

4

152

0.0041

0.0041

74

12

10

157

19.5414

19.5414

0.42878

17

Nassar Tamar
(PÄRNU VK)

19

69

34

27

0

102

0.0108

0.0108

10

26

1

262

0.0035

0.0035

36

9

3

83

19.9518

19.9518

0.42724

18

Soo Kevin
(BIGBANK Tartu)

20

59

32

46

3

122

0.0101

0.0101

3

23

1

137

0.0013

0.0013

47

10

5

103

18.3301

18.3301

0.4094

19

Cimoška Andis
(RTU/Robežsardze)

23

70

19

20

0

64

0.0049

0.0049

20

40

0

213

0.0051

0.0051

62

20

1

118

24.322

24.322

0.40777

20

Dzenis Jekabs
(OC Limbaži/MSĢ)

19

61

20

8

0

36

0.0071

0.0071

10

27

1

167

0.0039

0.0039

60

14

9

121

18.6529

18.6529

0.40537

21

Zavorotnijs Andrejs
(RTU/Robežsardze)

12

34

17

7

0

29

0.0079

0.0079

9

28

3

108

0.0056

0.0056

54

13

6

113

10.531

10.531

0.40446

22

Aru Marx
(SELVER Tallinn)

8

30

11

12

5

42

0.0077

0.0077

5

16

4

86

0.0063

0.0063

32

6

6

73

8.2192

8.2192

0.40311

23

Žolnerovičs Deivids
(Daugavpils Universitāte)

19

66

25

5

3

73

0.0081

0.0081

7

30

0

184

0.0023

0.0023

81

27

14

188

14.0426

14.0426

0.39538

24

Šimanskis Edgars
(OC Limbaži/MSĢ)

13

36

14

7

0

30

0.0073

0.0073

5

21

2

105

0.0037

0.0037

29

9

1

70

9.7714

9.7714

0.3908

25

Hääl Helger
(SELVER Tallinn)

14

43

16

29

4

78

0.0066

0.0066

8

18

4

94

0.0049

0.0049

25

11

3

70

6.7571

6.7571

0.38802

26

Plak Fabian
(SAAREMAA VK)

7

16

9

26

0

51

0.0069

0.0069

4

8

2

54

0.0046

0.0046

23

4

1

44

6.5455

6.5455

0.3877

27

Podkalns Mikus
(OC Limbaži/MSĢ)

19

56

13

7

0

38

0.0046

0.0046

8

18

2

140

0.0036

0.0036

33

9

4

65

17.2308

17.2308

0.38719

28

Veltson Markus
(TalTech)

18

60

14

23

1

55

0.0047

0.0047

11

20

3

163

0.0047

0.0047

22

0

3

52

21.9231

21.9231

0.36567

29

Prei Taavi
(SAAREMAA VK)

5

16

4

0

0

6

0.005

0.005

2

4

1

54

0.0037

0.0037

8

3

0

13

6.1538

6.1538

0.36252

30

Saaremaa Alex
(BIGBANK Tartu)

11

23

6

9

0

20

0.0036

0.0036

2

6

0

28

0.0012

0.0012

10

3

3

24

3.8333

3.8333

0.34961

31

Palang Kaur
(TalTech)

7

15

3

4

0

9

0.0023

0.0023

1

6

0

25

0.0008

0.0008

4

0

0

9

6.6667

6.6667

0.3346

32

Rahuoja Mattias
(PÄRNU VK)

5

12

1

2

0

6

0.0012

0.0012

0

4

0

38

0

0

7

0

1

17

4.2353

4.2353

0.33195

33

Sils Kristers
(SK Jēkabpils Lūši)

5

5

0

0

0

0

0

0

0

4

0

10

0

0

1

0

0

2

2.5

2.5

0.31911

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