TOP SPORT Lietuvos Moterų Tinklinio Čempionatas 2022/2023

Competition

TOP SPORT Lietuvos Moterų Tinklinio Čempionatas 2022/2023 Best players MIDDLE BLOCKER
PlayerPlayedServeServeBlockBlockAttackAttackRanking
  MS#=/TotSv ind.Sv ind.#=/TotBl ind.Bl ind.#=/TotSp ind.Sp ind.Index

1

MUKAITĖ Emilija
(Jonavos SC)

12

41

9

14

1

117

0.0058

0.0058

38

13

0

66

0.022

0.022

31

8

5

82

9

9

0.54014

2

FIRINOVIČ Lilija
(Svaja - Viktorija - LSU)

10

39

25

27

2

152

0.0164

0.0164

11

10

0

28

0.0067

0.0067

146

29

15

377

10.5517

10.5517

0.53701

3

SHRAMKO Marina
(JONAVOS "Aušrinė")

11

31

18

7

0

102

0.0128

0.0128

14

10

0

34

0.01

0.01

40

8

5

79

10.5949

10.5949

0.52609

4

MIKOLIŪNAITĖ Greta
(JONAVOS "Aušrinė")

11

33

14

4

2

138

0.0113

0.0113

17

11

0

47

0.012

0.012

45

12

1

106

9.9623

9.9623

0.52509

5

KASPARAVIČIŪTĖ Gabija
(TK “Kaunas”-VDU)

8

26

9

19

2

87

0.0096

0.0096

14

11

0

40

0.0122

0.0122

50

6

2

84

13

13

0.51716

6

GABULAITĖ Roberta
(Svaja - Viktorija - LSU)

12

45

11

13

3

138

0.0074

0.0074

28

18

0

70

0.0147

0.0147

55

11

5

120

14.625

14.625

0.51708

7

KVEDARAITĖ Danielė
(JONAVOS "Aušrinė")

12

34

11

16

0

119

0.0072

0.0072

18

6

0

28

0.0118

0.0118

109

13

13

287

9.8328

9.8328

0.48674

8

KAČINAITĖ Neringa
(VILNIAUS Universitetas)

12

42

10

12

0

119

0.0053

0.0053

26

19

0

56

0.0137

0.0137

39

22

14

144

0.875

0.875

0.46543

9

KARVELYTĖ Aurėja
(JONAVOS "Aušrinė")

6

15

5

3

1

50

0.0088

0.0088

5

1

0

8

0.0074

0.0074

17

3

2

41

4.3902

4.3902

0.46162

10

MOTIEJŪNAITĖ Kotryna
(TK “Kaunas”-VDU)

4

13

0

2

0

32

0

0

10

7

0

26

0.0166

0.0166

20

6

2

47

3.3191

3.3191

0.44217

11

VALIONYTĖ Vaiva
(TK “Kaunas”-VDU)

8

20

6

7

3

64

0.0079

0.0079

4

1

0

13

0.0035

0.0035

28

5

0

51

9.0196

9.0196

0.43667

12

ŽUKAITĖ Austėja
(Svaja - Viktorija - LSU)

13

43

7

10

1

129

0.0041

0.0041

15

14

0

52

0.0078

0.0078

24

1

3

86

10

10

0.43377

13

ŠIGAUSKAITĖ Stela
(Svaja - Viktorija - LSU)

7

21

3

8

2

48

0.0049

0.0049

5

12

0

25

0.0049

0.0049

20

2

4

51

5.7647

5.7647

0.41342

14

UMANTAITĖ Benita
(VILNIAUS Universitetas)

12

43

6

14

0

127

0.0033

0.0033

8

13

0

28

0.0044

0.0044

101

28

12

314

8.3535

8.3535

0.4021

15

IVANAUSKAITĖ Gelmė
(Jonavos SC)

11

30

7

7

0

65

0.0045

0.0045

9

17

0

37

0.0058

0.0058

3

4

2

31

-2.9032

-2.9032

0.4001

16

TOLIUŠYTĖ Gabija
(VILNIAUS Universitetas)

14

38

8

10

1

89

0.004

0.004

6

14

0

30

0.0027

0.0027

24

16

7

90

0.4222

0.4222

0.3827

17

GILYTĖ Ieva
(Jonavos SC)

9

18

5

12

0

48

0.0036

0.0036

5

4

0

11

0.0036

0.0036

7

8

3

37

-1.9459

-1.9459

0.38064

18

OBELIENIUTE Deimante
(VILNIAUS Universitetas)

9

19

4

5

0

50

0.0027

0.0027

0

7

0

10

0

0

12

4

1

41

3.2439

3.2439

0.36005

19

ERGARDT Eva
(Jonavos SC)

4

5

0

4

0

6

0

0

1

0

0

1

0.0019

0.0019

0

0

0

0

0

0

0.34343

20

BLINSTRUBYTĖ Vestina
(VILNIAUS Universitetas)

1

2

0

1

0

4

0

0

0

1

0

3

0

0

1

1

1

5

-0.4

-0.4

0.33177

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