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)

1

3

1

3

0

5

0.0073

0.0073

6

2

1

20

0.0511

0.0511

6

1

0

10

1.5

1.5

0.60984

2

Shchekalyuk Viktor
(Gargzdai Amber-Arlanga)

3

10

6

8

1

35

0.0138

0.0138

5

5

1

41

0.0138

0.0138

19

4

0

36

4.1667

4.1667

0.46876

3

Aru Marx
(SELVER Tallinn)

2

8

5

7

0

19

0.014

0.014

2

3

1

34

0.0084

0.0084

16

4

0

26

3.6923

3.6923

0.44253

4

Adamovics Zigurds
(SK Jēkabpils Lūši)

1

4

3

6

0

12

0.0164

0.0164

0

2

1

14

0.0055

0.0055

8

1

0

14

2

2

0.43926

5

Starkutis Linas
(Gargzdai Amber-Arlanga)

3

10

4

7

2

27

0.0092

0.0092

2

4

1

33

0.0069

0.0069

17

0

0

25

6.8

6.8

0.41228

6

Lebreux Kevin
(PÄRNU VK)

3

10

5

10

1

23

0.0112

0.0112

1

7

1

31

0.0045

0.0045

7

3

0

23

1.7391

1.7391

0.40374

7

Varblane Mihkel
(SELVER Tallinn)

2

7

4

4

0

15

0.0112

0.0112

1

4

0

23

0.0028

0.0028

14

3

1

28

2.5

2.5

0.39707

8

Saaremaa Alex
(BIGBANK Tartu)

2

5

4

2

0

14

0.0108

0.0108

1

2

0

15

0.0027

0.0027

5

0

0

8

3.125

3.125

0.39537

9

Naaber Mart
(BIGBANK Tartu)

2

7

4

10

0

23

0.0108

0.0108

0

2

0

18

0

0

5

1

0

9

3.1111

3.1111

0.38253

10

Perillus Sten
(TalTech)

2

7

3

8

0

20

0.009

0.009

0

2

0

19

0

0

5

1

1

12

1.75

1.75

0.37027

11

Kļaviņš Verners
(VK BIOLARS/Jelgava MSG)

1

3

1

1

0

7

0.0085

0.0085

0

2

0

6

0

0

2

1

0

3

1

1

0.36611

12

Catlakšs Leo
(VK BIOLARS/Jelgava MSG)

1

3

1

1

0

5

0.0085

0.0085

0

0

0

6

0

0

2

0

0

6

1

1

0.36611

13

Stiegelis Eduards
(VK BIOLARS/Jelgava MSG)

1

3

1

2

0

3

0.0085

0.0085

0

3

0

5

0

0

5

3

2

16

0

0

0.36453

14

Tanila Mihkel
(TalTech)

1

4

1

5

0

11

0.0052

0.0052

0

3

0

12

0

0

4

0

0

6

2.6667

2.6667

0.35081

15

Nazarovs Antons
(Daugavpils Universitāte/Ezerzeme)

1

4

1

13

0

17

0.0055

0.0055

0

3

0

12

0

0

1

1

1

5

-0.8

-0.8

0.34683

16

Soo Kevin
(BIGBANK Tartu)

2

7

0

3

0

10

0

0

1

4

1

21

0.0054

0.0054

5

1

1

14

1.5

1.5

0.34531

17

Slavēns Gatis
(RTU/Robežsardze/Jūrmala)

2

6

1

9

0

13

0.0036

0.0036

0

2

0

17

0

0

4

1

0

10

1.8

1.8

0.34078

18

Peterson Kristo-Joosep
(PÄRNU VK)

2

7

0

4

0

10

0

0

1

2

0

26

0.0032

0.0032

2

1

1

11

0

0

0.33327

19

Klopovs Olegs
(RTU/Robežsardze/Jūrmala)

3

7

0

3

0

6

0

0

0

1

1

20

0.0022

0.0022

5

0

0

5

7

7

0.32854

20

Dzenis Jekabs
(RTU/Robežsardze/Jūrmala)

3

9

0

13

1

24

0

0

0

9

0

25

0

0

10

3

1

28

1.9286

1.9286

0.32198

21

Hääl Helger
(SELVER Tallinn)

1

2

0

2

0

2

0

0

0

2

0

2

0

0

1

0

0

2

1

1

0.3206

22

Žolnerovičs Devids
(Daugavpils Universitāte/Ezerzeme)

1

4

0

6

0

10

0

0

0

2

0

12

0

0

8

4

1

16

0.75

0.75

0.32022

23

Päid Siim
(BIGBANK Tartu)

1

1

0

0

0

1

0

0

0

1

0

5

0

0

0

0

0

0

0

0

0.31911

24

Tīsis Kristians
(VK BIOLARS/Jelgava MSG)

1

2

0

1

1

3

0

0

0

0

0

1

0

0

0

0

0

2

0

0

0.31911

25

Žaltauskas Edvinas
(Gargzdai Amber-Arlanga)

1

1

0

0

0

0

0

0

0

1

0

2

0

0

0

0

0

0

0

0

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:  1