Naiste Eesti meistrivõistlused

Naiste Eesti Meistrivõistlused

Naiste Eesti meistrivõistlused Best players MIDDLE BLOCKER
PlayerPlayedServeBlockAttackRanking
  MS#=/TotSv ind.#=/TotBl ind.#=/TotSp ind.Index

1

Põld Kätriin
(TalTech)

13

42

23

13

6

136

0.0165

30

11

2

63

0.0171

79

4

4

146

20.4247

0.62255

2

Pill Liisbet
(TÜ/Bigbank)

18

59

21

26

17

185

0.0141

31

29

2

94

0.0115

100

13

13

181

24.1215

0.57337

3

Sova Johanna
(TalTech)

2

7

1

1

1

18

0.0067

7

6

0

16

0.0235

10

2

0

19

2.9474

0.54622

4

Laub Paula
(Viimsi/Tallinna Ülikool)

9

31

16

11

2

101

0.0123

19

16

0

56

0.013

18

2

2

48

9.0417

0.53897

5

Tugedam Karmen
(Audentese SG /Noortekoondis)

19

65

36

49

8

196

0.0134

29

31

2

116

0.0088

42

19

7

122

8.5246

0.52029

6

Provotorov Liisa
(BARRUS/Võru VK)

14

49

20

34

10

154

0.0145

14

11

0

38

0.0068

76

23

11

208

9.8942

0.51858

7

Polman Maarja Liisa
(Pärnu Spordikool)

17

62

23

21

3

168

0.0099

29

17

0

69

0.0111

71

11

13

198

14.7172

0.51568

8

Sild Kristel
(TÜ/Bigbank)

16

39

19

21

11

135

0.0127

23

15

0

64

0.0097

17

6

2

55

6.3818

0.51516

9

Arak Mari
(TÜ/Bigbank)

15

46

18

13

9

167

0.0119

16

15

2

55

0.007

51

6

5

101

18.2178

0.51311

10

Nõmm Emma Marie
(Pärnu Spordikool)

9

33

9

21

5

98

0.0101

12

5

0

23

0.0086

19

5

7

67

3.4478

0.47902

11

Nälk Hanna Liise
(Viljandi Metall)

14

50

16

32

5

134

0.0095

16

29

0

67

0.0072

63

18

13

166

9.6386

0.47641

12

Hiie Berit
(TBD Pharmatech/Kastre)

16

48

13

23

6

149

0.0074

24

18

0

67

0.0093

52

13

10

124

11.2258

0.47507

13

Peit Eliisa
(TalTech)

16

36

14

10

5

124

0.0087

13

17

0

56

0.006

44

10

3

93

12

0.46604

14

Maiste Laura Liisa
(TÜ/Bigbank)

18

57

15

22

12

179

0.0098

10

13

0

30

0.0036

95

34

16

327

7.844

0.45271

15

Järvekülg Maare
(Pärnu Spordikool)

12

41

9

28

2

107

0.0064

16

14

0

37

0.0093

20

15

2

68

1.8088

0.44731

16

Kruus Lisethe
(TBD Pharmatech/Kastre)

17

48

9

22

6

126

0.0054

19

10

0

39

0.0069

31

9

6

92

8.3478

0.43636

17

Tänav Sirli
(Viimsi/Tallinna Ülikool)

14

49

7

20

1

89

0.0037

16

26

0

70

0.0074

34

9

3

106

10.1698

0.42804

18

Silm Keiu
(Viimsi/Tallinna Ülikool)

2

7

1

4

2

21

0.0102

0

1

0

4

0

14

0

3

26

2.9615

0.42322

19

Soodla Heleri
(Audentese SG /Noortekoondis)

13

33

5

11

3

87

0.0036

17

11

0

42

0.0077

13

7

4

42

1.5714

0.41312

20

Koppel Marii
(BARRUS/Võru VK)

14

44

5

15

3

116

0.0039

12

17

0

40

0.0059

53

28

5

179

4.9162

0.4098

21

Prunt Elis
(Pärnu Spordikool)

14

37

7

19

1

73

0.0036

5

3

0

10

0.0022

34

20

9

149

1.2416

0.37761

22

Päll Elisaveta
(TBD Pharmatech/Kastre)

1

1

0

0

0

0

0

1

0

0

2

0.0058

0

0

0

0

0

0.36713

23

Vengerfeldt Katariina
(TÜ/Bigbank)

4

6

0

4

0

11

0

3

3

0

6

0.0044

1

1

0

4

0

0.35878

24

Allik Maarja
(Viljandi Metall)

18

29

5

11

1

55

0.0021

1

7

0

15

0.0004

3

2

3

24

-2.4167

0.35174

25

Varšavskaja Julija
(Pärnu Spordikool)

5

6

0

2

0

6

0

0

0

0

0

0

0

0

0

0

0

0.33246

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