Elme Messer Men`s Baltic League 25/26

Elme Messer Men`s Baltic League 25/26

Elme Messer Men`s Baltic League 25/26 Best players OPPOSITE
PlayerPlayedServeServeBlockBlockAttackAttackRanking
  MS#=/TotSv ind.Sv ind.#=/TotBl ind.Bl ind.#=/TotSp ind.Sp ind.Index

1

Santos Renato Oliveira
(Barrus Võru VK)

13

49

28

53

9

200

0.0177

0.0177

20

18

0

62

0.0096

0.0096

200

28

25

370

19.4676

19.4676

0.49866

2

Jefanov Andres
(Selver x TalTech)

10

37

24

33

5

148

0.018

0.018

13

15

3

43

0.0081

0.0081

148

34

20

288

12.0764

12.0764

0.4546

3

Pita Daneil Alejandro Ramirez
(PÄRNU Võrkpalliklubi)

11

39

23

43

3

155

0.015

0.015

16

32

1

71

0.0092

0.0092

151

20

23

306

13.7647

13.7647

0.45223

4

Williams Jack David Ronald
(BIGBANK Tartu)

10

34

14

25

4

125

0.0108

0.0108

17

21

3

58

0.0102

0.0102

116

26

23

254

8.9685

8.9685

0.40974

5

Kalnins Toms
(Robežsardze / RSU)

12

44

6

45

7

144

0.0064

0.0064

13

26

2

67

0.0064

0.0064

172

28

40

344

13.3023

13.3023

0.38409

6

Timusk Kaspar
(Audentes/Solarstone)

9

26

11

18

4

85

0.0124

0.0124

9

12

1

33

0.0074

0.0074

71

19

18

205

4.3122

4.3122

0.38122

7

Vilcāns Sandis
( Ezerzeme/DU)

9

32

5

21

6

93

0.0072

0.0072

17

19

0

56

0.0112

0.0112

85

30

26

225

4.1244

4.1244

0.3721

8

Uuskari Markus
(PÄRNU Võrkpalliklubi)

9

27

12

11

1

89

0.0088

0.0088

3

5

0

14

0.002

0.002

57

13

5

124

8.4919

8.4919

0.35129

9

Täht Tarvo
(Barrus Võru VK)

9

30

4

25

4

70

0.0056

0.0056

13

16

2

56

0.0092

0.0092

28

7

1

53

11.3208

11.3208

0.33421

10

Gabdulļins Matīss
(Jēkabpils Lūši)

10

36

4

23

2

105

0.0039

0.0039

5

22

0

53

0.0033

0.0033

125

22

25

348

8.069

8.069

0.32996

11

Makarov Ilija
( Ezerzeme/DU)

4

8

2

1

2

19

0.0066

0.0066

4

12

0

21

0.0066

0.0066

6

1

3

11

1.4545

1.4545

0.32564

12

Mironovs Daņila
( Ezerzeme/DU)

9

24

4

7

3

46

0.0042

0.0042

2

9

1

15

0.0012

0.0012

45

11

10

89

6.4719

6.4719

0.31388

13

Vaškelis Edvinas
(Elga-Grafaite S-Sportas Šiauliai)

2

7

2

1

0

14

0.0056

0.0056

0

4

0

6

0

0

20

3

2

49

2.1429

2.1429

0.29648

14

Mišeikis Arvydas
(Elga-Grafaite S-Sportas Šiauliai)

2

5

0

2

0

7

0

0

1

3

0

5

0.0028

0.0028

7

1

2

25

0.8

0.8

0.27731

15

Stopkin Marten
(PÄRNU Võrkpalliklubi)

6

8

1

2

0

8

0.0011

0.0011

0

0

0

0

0

0

5

3

0

14

1.1429

1.1429

0.26612

16

Prauls Nils Daniels
(Robežsardze / RSU)

12

28

0

3

0

23

0

0

2

6

1

17

0.001

0.001

27

8

12

75

2.6133

2.6133

0.26561

17

Jakobson Karl
(Barrus Võru VK)

7

9

1

3

0

12

0.0009

0.0009

0

1

0

3

0

0

10

2

2

24

2.25

2.25

0.26555

18

Lehiste Kristjan
(Audentes/Solarstone)

1

3

0

2

0

2

0

0

0

0

0

0

0

0

1

1

0

4

0

0

0.2612

19

Krakops Edijs
(Jēkabpils Lūši)

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.2612

20

Rumševičius Arnas
(Elga-Grafaite S-Sportas Šiauliai)

1

2

0

0

0

0

0

0

0

0

0

1

0

0

0

1

1

8

-0.5

-0.5

0.25929

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