Credit24 Champins League 21/22

Credit24 Champions League

Credit24 Champins League 21/22 Best players MIDDLE BLOCKER
PlayerPlayedBlockBlockServeServeAttackAttackRanking
  MS#=/TotBl ind.Bl ind.#=/TotSv ind.Sv ind.#=/TotSp ind.Sp ind.Index

1

Svans Toms
(PÄRNU VK)

18

67

38

63

1

177

0.0131

0.0131

42

45

11

270

0.0183

0.0183

118

11

12

214

29.743

29.743

0.53028

2

Varblane Mihkel
(SELVER Tallinn)

18

65

45

42

2

124

0.0152

0.0152

7

44

6

232

0.0044

0.0044

108

20

17

190

24.2895

24.2895

0.46461

3

Starkutis Linas
(Gargzdai Amber-Arlanga)

19

66

45

63

5

172

0.0151

0.0151

3

22

5

184

0.0027

0.0027

88

9

8

175

26.7771

26.7771

0.45942

4

Mazenko Bohdan
(PÄRNU VK)

11

40

24

36

0

92

0.0141

0.0141

4

37

5

138

0.0053

0.0053

76

5

5

121

21.8182

21.8182

0.4582

5

Shchekalyuk Viktor
(Gargzdai Amber-Arlanga)

20

65

43

76

2

198

0.0135

0.0135

15

28

3

250

0.0057

0.0057

95

23

7

191

22.1204

22.1204

0.4574

6

Saaremaa Alex
(BIGBANK Tartu)

17

52

27

37

1

127

0.0108

0.0108

7

39

9

157

0.0064

0.0064

98

4

10

153

28.549

28.549

0.45566

7

Nazarovs Antons
(Daugavpils Universitāte/Ezerzeme)

11

43

34

32

0

89

0.018

0.018

4

15

2

124

0.0032

0.0032

38

7

11

82

10.4878

10.4878

0.45139

8

Aru Marx
(SELVER Tallinn)

16

59

28

41

1

108

0.0105

0.0105

13

36

5

224

0.0068

0.0068

101

15

9

176

25.8125

25.8125

0.4513

9

Perillus Sten
(TalTech)

12

44

36

35

2

125

0.0174

0.0174

4

9

2

101

0.0029

0.0029

25

7

7

70

6.9143

6.9143

0.44069

10

Slavēns Gatis
(RTU/Robežsardze/Jūrmala)

11

36

17

40

0

77

0.0096

0.0096

8

12

6

123

0.0079

0.0079

45

3

3

74

18.973

18.973

0.44027

11

Naaber Mart
(BIGBANK Tartu)

13

42

22

34

0

85

0.011

0.011

7

14

4

144

0.0055

0.0055

56

6

5

95

19.8947

19.8947

0.43801

12

Adamovics Zigurds
(SK Jēkabpils Lūši)

14

51

25

51

0

112

0.011

0.011

7

19

3

158

0.0044

0.0044

72

8

12

123

21.561

21.561

0.43513

13

Blumbergs Vladislavs
(SK Jēkabpils Lūši)

14

49

19

54

0

117

0.0084

0.0084

17

52

3

176

0.0088

0.0088

58

12

8

116

16.0517

16.0517

0.43259

14

Kuzmin Danila
(Daugavpils Universitāte/Ezerzeme)

14

46

29

35

0

78

0.0124

0.0124

8

51

3

156

0.0047

0.0047

45

8

9

104

12.3846

12.3846

0.42921

15

Catlakšs Leo
(VK BIOLARS/Jelgava MSG)

14

48

21

24

0

73

0.01

0.01

8

23

3

122

0.0052

0.0052

43

13

7

126

8.7619

8.7619

0.41227

16

Žolnerovičs Devids
(Daugavpils Universitāte/Ezerzeme)

16

49

24

25

0

74

0.0091

0.0091

2

29

2

143

0.0015

0.0015

74

11

8

131

20.5725

20.5725

0.40834

17

Soo Kevin
(BIGBANK Tartu)

16

47

13

27

1

78

0.0055

0.0055

7

19

3

137

0.0043

0.0043

46

3

4

79

23.2025

23.2025

0.40543

18

Lebreux Kevin
(PÄRNU VK)

5

17

7

22

1

44

0.0093

0.0093

3

12

1

50

0.0053

0.0053

13

4

1

39

3.4872

3.4872

0.39958

19

Dzenis Jekabs
(RTU/Robežsardze/Jūrmala)

18

59

20

55

1

131

0.0067

0.0067

3

45

5

168

0.0027

0.0027

47

11

6

108

16.3889

16.3889

0.39352

20

Klopovs Olegs
(RTU/Robežsardze/Jūrmala)

17

51

18

39

0

86

0.0063

0.0063

6

13

1

137

0.0025

0.0025

40

7

5

81

17.6296

17.6296

0.39223

21

Tanila Mihkel
(TalTech)

11

36

11

36

2

79

0.0063

0.0063

6

23

2

114

0.0046

0.0046

31

3

10

66

9.8182

9.8182

0.38945

22

Graham Beau James
(TalTech)

3

10

4

8

0

25

0.0071

0.0071

2

3

0

29

0.0036

0.0036

9

0

1

19

4.2105

4.2105

0.38048

23

Stiegelis Eduards
(VK BIOLARS/Jelgava MSG)

16

55

11

15

2

45

0.0046

0.0046

7

44

4

156

0.0046

0.0046

154

51

40

401

8.6409

8.6409

0.37807

24

Peterson Kristo-Joosep
(PÄRNU VK)

8

31

9

24

0

51

0.0066

0.0066

3

21

1

94

0.0029

0.0029

15

3

4

41

6.0488

6.0488

0.377

25

Pallo Jānis
(VK BIOLARS/Jelgava MSG)

4

10

6

1

1

12

0.0103

0.0103

0

7

0

22

0

0

4

1

1

9

2.2222

2.2222

0.37483

26

Samuilovs Aleksandrs
(SK Jēkabpils Lūši)

10

23

5

14

0

26

0.003

0.003

3

3

1

49

0.0024

0.0024

32

6

2

58

9.5172

9.5172

0.36075

27

Žaltauskas Edvinas
(Gargzdai Amber-Arlanga)

11

25

7

15

0

35

0.0038

0.0038

2

12

2

51

0.0022

0.0022

21

6

2

49

6.6327

6.6327

0.35904

28

Torn Remo
(TalTech)

9

20

7

14

0

32

0.0046

0.0046

1

7

1

58

0.0013

0.0013

21

6

2

44

5.9091

5.9091

0.35822

29

Kļaviņš Verners
(VK BIOLARS/Jelgava MSG)

12

34

5

6

0

28

0.0027

0.0027

3

11

2

64

0.0027

0.0027

15

6

3

35

5.8286

5.8286

0.35476

30

Hääl Helger
(SELVER Tallinn)

13

31

7

23

0

42

0.0033

0.0033

1

18

4

61

0.0023

0.0023

17

6

6

51

3.0392

3.0392

0.35147

31

Šmits Niks Kristaps
(VK BIOLARS/Jelgava MSG)

6

15

3

6

1

13

0.0033

0.0033

0

4

2

30

0.0022

0.0022

6

1

2

14

3.2143

3.2143

0.34624

32

Päid Siim
(BIGBANK Tartu)

8

14

3

7

0

17

0.0025

0.0025

0

7

3

42

0.0025

0.0025

7

0

1

10

8.4

8.4

0.34385

33

Tīsis Kristians
(VK BIOLARS/Jelgava MSG)

8

18

3

14

4

30

0.0025

0.0025

1

7

0

46

0.0008

0.0008

6

0

2

22

3.2727

3.2727

0.34074

34

Formanickis Aleksejs
(RTU/Robežsardze/Jūrmala)

11

17

3

1

0

8

0.0016

0.0016

1

1

0

10

0.0005

0.0005

2

0

1

4

4.25

4.25

0.32722

35

Voronko Eriks
(SK Jēkabpils Lūši)

1

3

0

5

0

7

0

0

0

2

0

9

0

0

2

0

0

5

1.2

1.2

0.32089

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