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)

19

73

43

78

13

302

0.0179

0.0179

32

32

0

103

0.0102

0.0102

307

46

47

580

26.9345

26.9345

0.5402

2

Pita Daneil Alejandro Ramirez
(PÄRNU Võrkpalliklubi)

17

60

32

67

9

231

0.0152

0.0152

28

46

1

106

0.0104

0.0104

225

37

36

452

20.177

20.177

0.4914

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)

18

61

25

46

11

225

0.0123

0.0123

23

40

5

98

0.0078

0.0078

211

44

33

440

18.5773

18.5773

0.45139

5

Kalnins Toms
(Robežsardze / RSU)

19

72

13

67

8

235

0.0065

0.0065

24

45

3

118

0.0074

0.0074

275

44

62

553

22.0036

22.0036

0.43159

6

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

7

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

8

Uuskari Markus
(PÄRNU Võrkpalliklubi)

15

46

20

19

3

153

0.0094

0.0094

5

11

0

28

0.002

0.002

100

20

11

225

14.1067

14.1067

0.38041

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

Täht Tarvo
(Barrus Võru VK)

13

39

6

29

4

87

0.0048

0.0048

16

28

2

76

0.0076

0.0076

33

7

4

66

13

13

0.32154

13

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

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)

10

12

1

3

0

14

0.0007

0.0007

0

1

0

3

0

0

10

2

2

24

3

3

0.26426

19

Prauls Nils Daniels
(Robežsardze / RSU)

18

40

1

3

0

31

0.0003

0.0003

2

7

1

19

0.0006

0.0006

31

10

14

89

3.1461

3.1461

0.26408

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