Elme Messer Women`s Baltic League 25/26

Elme Messer Women`s Baltic League 25/26

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

1

Tugedam Karmen
(Audentese SG)

2

7

10

4

2

38

0.0392

0.0392

3

2

0

7

0.0098

0.0098

24

9

6

76

0.8289

0.8289

0.72503

2

Rakauskaite Barbora
(VMSM Sostinės tauras - VTC)

7

25

5

14

7

71

0.011

0.011

25

11

1

47

0.0229

0.0229

44

5

6

78

10.5769

10.5769

0.59466

3

Soodla Heleri
(Audentese SG)

11

40

16

23

8

130

0.0137

0.0137

25

25

4

85

0.0143

0.0143

30

8

4

81

8.8889

8.8889

0.55974

4

Urbāne Monta
(RSU)

6

18

2

2

5

46

0.0081

0.0081

18

12

3

52

0.0209

0.0209

37

5

1

67

8.3284

8.3284

0.55169

5

Bole Emanuela
(LAT U18 / RVS)

9

30

25

21

1

71

0.0206

0.0206

6

6

0

23

0.0047

0.0047

73

32

12

206

4.2233

4.2233

0.54882

6

Kiviloo Liis
(Rae Spordikool/VIASTON)

7

23

4

8

7

61

0.0101

0.0101

18

14

3

64

0.0165

0.0165

45

5

8

96

7.6667

7.6667

0.53992

7

Allorg Kristel
(Rae Spordikool/VIASTON)

5

16

7

8

4

69

0.014

0.014

8

9

0

24

0.0102

0.0102

41

4

5

97

5.2784

5.2784

0.52856

8

Liepa Samanta
(Riga Volleyball School/LU)

8

35

17

16

5

105

0.0151

0.0151

13

11

1

45

0.0089

0.0089

18

3

1

26

18.8462

18.8462

0.51936

9

Roziņa Marta
(LAT U18 / RVS)

9

30

18

16

7

97

0.0198

0.0198

3

2

0

8

0.0024

0.0024

2

4

0

12

-5

-5

0.51809

10

Galubaitė Roberta
(Kaunas-VDU)

11

31

13

9

5

109

0.0113

0.0113

16

8

1

43

0.01

0.01

44

4

4

91

12.2637

12.2637

0.5165

11

Stamm Kertu
(Audentese SG)

11

38

22

15

0

120

0.0126

0.0126

16

19

0

65

0.0092

0.0092

33

11

5

89

7.2584

7.2584

0.51267

12

Sola Līva
(Riga Volleyball School/LU)

8

35

8

11

2

105

0.0069

0.0069

22

19

3

81

0.0151

0.0151

16

4

2

36

9.7222

9.7222

0.50531

13

Postnova Anastasija
(RSU)

2

8

3

4

2

32

0.0155

0.0155

2

8

1

19

0.0062

0.0062

0

2

2

11

-2.9091

-2.9091

0.49928

14

Kandimaa Lill
(TÜ/Bigbank)

9

31

12

8

5

117

0.0119

0.0119

12

13

0

43

0.0084

0.0084

17

7

2

48

5.1667

5.1667

0.4975

15

Brūvere Keita Kintija
(LAT U18 / RVS)

9

30

13

15

2

70

0.0119

0.0119

10

12

1

32

0.0079

0.0079

7

1

0

17

10.5882

10.5882

0.48396

16

Juodkaite Lukrecija
(VMSM Sostinės tauras - VTC)

4

14

6

5

2

49

0.0118

0.0118

5

12

0

20

0.0074

0.0074

5

0

0

11

6.3636

6.3636

0.47959

17

Dilytė Ugnė
(LTU U18)

10

30

9

18

8

90

0.0112

0.0112

9

7

1

24

0.0059

0.0059

28

9

6

71

5.493

5.493

0.47552

18

Skalbe Elza
(LAT U18 / RVS)

9

30

9

22

2

51

0.0087

0.0087

14

20

1

48

0.0111

0.0111

16

9

8

46

-0.6522

-0.6522

0.47532

19

Gedminaitė Saulė
(LTU U18)

10

29

8

6

3

101

0.0072

0.0072

14

11

2

41

0.0092

0.0092

24

3

3

49

10.6531

10.6531

0.4719

20

Tralmaka Emīlija
(RSU)

9

26

5

8

8

87

0.0095

0.0095

8

15

2

48

0.0058

0.0058

33

5

3

67

9.7015

9.7015

0.46771

21

Česnavičiūtė Elija
(LTU U18)

6

16

5

3

4

42

0.0107

0.0107

5

5

0

15

0.0059

0.0059

12

4

6

42

0.7619

0.7619

0.46195

22

Kačinaitė Neringa
(Kaunas-VDU)

11

24

7

7

4

78

0.0069

0.0069

14

9

1

46

0.0088

0.0088

32

3

11

69

6.2609

6.2609

0.45751

23

Maļina Anabella
(Riga Volleyball School/LU)

7

21

11

10

1

59

0.0095

0.0095

8

9

0

31

0.0064

0.0064

38

17

16

134

0.7836

0.7836

0.45465

24

Pill Liisbet
(TÜ/Bigbank)

6

16

6

5

2

34

0.0082

0.0082

4

3

3

16

0.0041

0.0041

12

2

1

31

4.6452

4.6452

0.43533

25

Valionytė Vaiva
(Kaunas-VDU)

10

23

8

9

2

85

0.0071

0.0071

6

8

0

34

0.0042

0.0042

36

3

5

73

8.8219

8.8219

0.43406

26

Põldma Liisa
(TÜ/Bigbank)

7

17

4

3

4

25

0.0075

0.0075

2

0

0

4

0.0019

0.0019

22

7

5

53

3.2075

3.2075

0.41176

27

Jefanov Maria Elizabeth
(Audentese SG)

4

9

2

2

1

29

0.0055

0.0055

3

3

0

11

0.0055

0.0055

4

1

4

17

-0.5294

-0.5294

0.41086

28

Mitiakina Simona
(RSU)

6

12

1

2

0

41

0.0012

0.0012

6

8

1

29

0.0069

0.0069

10

3

1

19

3.7895

3.7895

0.39097

29

Čekauskaite Ana
(VMSM Sostinės tauras - VTC)

3

4

0

3

2

12

0.0041

0.0041

1

3

0

8

0.0021

0.0021

1

0

2

7

-0.5714

-0.5714

0.37761

30

Bukovska Adriana
(LAT U18 / RVS)

8

17

5

13

0

33

0.0044

0.0044

2

4

0

10

0.0018

0.0018

15

13

10

67

-2.0299

-2.0299

0.37564

31

Vaitulionytė Ela
(LTU U18)

4

5

0

4

0

7

0

0

2

1

0

3

0.0038

0.0038

0

1

1

4

-2.5

-2.5

0.35535

32

Arak Mari
(TÜ/Bigbank)

3

5

0

3

1

6

0.002

0.002

0

0

0

0

0

0

3

0

0

8

1.875

1.875

0.35203

33

Armale Alise
(LAT U18 / RVS)

8

14

0

4

0

4

0

0

0

1

0

2

0

0

10

6

2

45

0.6222

0.6222

0.33352

34

Dapkutė Atėnė
(LTU U18)

2

2

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.33246

35

Ozolina Betija Beate
(Riga Volleyball School/LU)

1

1

0

0

0

2

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.33246

36

Valaitytė Ugnė
(LTU U18)

3

3

0

2

0

3

0

0

0

0

0

0

0

0

0

2

1

5

-1.8

-1.8

0.32938

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