Credit24 Champins League 21/22

Credit24 Champions League

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

1

Kordas Valentin
(BIGBANK Tartu)

19

62

19

57

12

246

0.0113

0.0113

14

36

0

83

0.0051

0.0051

226

30

36

467

21.242

21.242

0.44271

2

Gabduļins Matīss
(RTU/Robežsardze/Jūrmala)

18

67

11

45

11

233

0.0074

0.0074

29

43

0

110

0.0097

0.0097

309

38

65

692

19.9451

19.9451

0.44061

3

Šmidl Matej
(TalTech)

16

54

24

48

9

216

0.0125

0.0125

15

34

1

76

0.0057

0.0057

246

62

43

511

14.9002

14.9002

0.42269

4

Uuskari Markus
(PÄRNU VK)

19

71

25

44

9

302

0.011

0.011

12

38

1

80

0.0039

0.0039

258

62

53

611

16.617

16.617

0.41213

5

Lõhmus Timo
(SELVER Tallinn)

15

48

18

30

4

160

0.0091

0.0091

17

35

0

73

0.007

0.007

164

44

38

365

10.7836

10.7836

0.39056

6

Katinas Daumantas
(Gargzdai Amber-Arlanga)

1

3

1

2

0

7

0.0076

0.0076

2

6

1

9

0.0153

0.0153

15

1

0

25

1.68

1.68

0.38573

7

Vilcāns Sandis
(VK BIOLARS/Jelgava MSG)

13

45

8

31

5

129

0.0066

0.0066

14

26

0

60

0.0071

0.0071

141

33

44

341

8.4457

8.4457

0.3667

8

Getman Nazar
(Daugavpils Universitāte/Ezerzeme)

18

61

11

45

5

193

0.0054

0.0054

11

20

0

39

0.0037

0.0037

155

46

39

420

10.1667

10.1667

0.34932

9

Jemeljanovs Pavels
(SK Jēkabpils Lūši)

15

45

8

32

1

124

0.0037

0.0037

13

38

0

78

0.0053

0.0053

134

39

36

311

8.537

8.537

0.341

10

Kais Kaur Erik
(TalTech)

10

30

5

15

3

69

0.0048

0.0048

3

2

0

15

0.0018

0.0018

53

14

12

123

6.5854

6.5854

0.32022

11

Nuut Mihkel
(SELVER Tallinn)

14

41

8

30

3

91

0.0048

0.0048

2

10

1

32

0.0009

0.0009

74

28

18

189

6.0741

6.0741

0.31352

12

Pukitis Rihards
(Daugavpils Universitāte/Ezerzeme)

14

37

4

7

3

104

0.003

0.003

6

19

0

39

0.0026

0.0026

46

14

14

134

4.9701

4.9701

0.30836

13

Hansman Hergo
(BIGBANK Tartu)

9

16

4

9

1

27

0.004

0.004

2

3

0

9

0.0016

0.0016

15

9

5

45

0.3556

0.3556

0.28735

14

Stog Jan
(SELVER Tallinn)

4

4

1

1

0

4

0.0015

0.0015

2

1

0

3

0.0031

0.0031

5

2

1

14

0.5714

0.5714

0.28502

15

Krakops Edijs
(SK Jēkabpils Lūši)

4

8

0

4

1

21

0.0016

0.0016

0

6

0

11

0

0

6

3

5

30

-0.5333

-0.5333

0.26674

16

Liepa Toms Emīls
(VK BIOLARS/Jelgava MSG)

6

8

0

1

0

7

0

0

1

1

1

5

0.0011

0.0011

4

3

5

23

-1.3913

-1.3913

0.26645

17

Rubavicius Vilius
(Gargzdai Amber-Arlanga)

11

16

1

6

0

20

0.0005

0.0005

1

3

0

8

0.0005

0.0005

6

0

2

16

4

4

0.2662

18

Landzāns Kristers
(SK Jēkabpils Lūši)

5

9

0

2

0

3

0

0

0

2

0

3

0

0

9

2

2

19

2.3684

2.3684

0.2612

19

Teikmanis Janis
(RTU/Robežsardze/Jūrmala)

1

3

0

0

0

1

0

0

0

0

0

0

0

0

2

0

0

3

2

2

0.2612

20

Samuilovs Aleksandrs
(SK Jēkabpils Lūši)

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

1

-1

-1

0.2612

Ranking Calculation

Opposite

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:  3

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