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)

22

85

49

94

16

350

0.0178

0.0178

39

41

0

131

0.0107

0.0107

364

51

55

692

31.6908

31.6908

0.56545

2

Pita Daneil Alejandro Ramirez
(PÄRNU Võrkpalliklubi)

20

74

38

82

15

280

0.016

0.016

32

61

1

129

0.0097

0.0097

287

45

46

575

25.2243

25.2243

0.51736

3

Jefanov Andres
(Selver x TalTech)

15

56

37

46

13

232

0.0204

0.0204

18

25

3

65

0.0074

0.0074

209

49

36

427

16.2623

16.2623

0.48545

4

Williams Jack David Ronald
(BIGBANK Tartu)

20

68

27

50

11

245

0.0117

0.0117

25

47

5

109

0.0077

0.0077

226

51

34

474

20.2278

20.2278

0.45507

5

Kalnins Toms
(Robežsardze / RSU)

21

81

17

79

11

264

0.0077

0.0077

26

51

3

134

0.0071

0.0071

312

51

68

624

25.0529

25.0529

0.45203

6

Uuskari Markus
(PÄRNU Võrkpalliklubi)

18

58

21

25

8

185

0.0095

0.0095

7

18

0

45

0.0023

0.0023

129

26

12

288

18.3264

18.3264

0.40229

7

Vilcāns Sandis
( Ezerzeme/DU)

15

55

16

37

8

169

0.0095

0.0095

22

27

0

76

0.0087

0.0087

157

43

44

368

10.462

10.462

0.40145

8

Timusk Kaspar
(Audentes/Solarstone)

10

29

12

20

4

95

0.012

0.012

10

13

1

35

0.0075

0.0075

79

21

19

226

5.0044

5.0044

0.38221

9

Gabdulļins Matīss
(Jēkabpils Lūši)

16

56

9

36

3

171

0.0048

0.0048

8

33

0

72

0.0032

0.0032

182

29

41

500

12.544

12.544

0.35442

10

Makarov Ilija
( Ezerzeme/DU)

8

24

9

15

3

72

0.0094

0.0094

9

24

0

56

0.007

0.007

27

5

4

45

9.6

9.6

0.34288

11

Mironovs Daņila
( Ezerzeme/DU)

15

41

5

16

3

70

0.003

0.003

4

12

1

25

0.0015

0.0015

69

17

16

138

10.6957

10.6957

0.32761

12

Vaškelis Edvinas
(Elga-Grafaite S-Sportas Šiauliai)

3

11

4

3

0

27

0.0074

0.0074

0

4

0

6

0

0

40

4

2

78

4.7949

4.7949

0.31679

13

Täht Tarvo
(Barrus Võru VK)

16

46

6

31

5

92

0.0042

0.0042

17

35

2

89

0.0065

0.0065

35

8

5

73

13.863

13.863

0.3127

14

Soome Mark Norman
(Audentes/Solarstone)

1

2

0

1

0

5

0

0

1

0

0

1

0.0085

0.0085

3

4

0

9

-0.2222

-0.2222

0.3006

15

Lehiste Kristjan
(Audentes/Solarstone)

4

7

0

5

0

10

0

0

2

2

0

5

0.004

0.004

7

4

0

16

1.3125

1.3125

0.28002

16

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

17

Stopkin Marten
(PÄRNU Võrkpalliklubi)

10

16

2

4

1

17

0.002

0.002

0

3

0

4

0

0

13

5

0

33

3.8788

3.8788

0.27053

18

Jakobson Karl
(Barrus Võru VK)

11

13

1

3

0

14

0.0006

0.0006

0

1

0

3

0

0

10

2

3

26

2.5

2.5

0.26392

19

Prauls Nils Daniels
(Robežsardze / RSU)

20

46

1

3

0

31

0.0003

0.0003

2

10

1

22

0.0006

0.0006

33

10

14

93

4.4516

4.4516

0.26375

20

Ieviņš Emīls
(Jēkabpils Lūši)

1

2

0

0

0

1

0

0

0

0

0

0

0

0

2

0

0

2

2

2

0.2612

21

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

22

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