1. Bundesliga Frauen

Competition

1. Bundesliga Frauen Best players OPPOSITE
PlayerPlayedServeServeBlockBlockAttackAttackRanking
  MS#=/TotSv ind.Sv ind.#=/TotBl ind.Bl ind.#=/TotSp ind.Sp ind.Index

1

Lippmann Louisa
(SSC Palmberg Schwerin)

27

97

32

38

23

345

0.0127

0.0127

35

17

40

155

0.0081

0.0081

420

79

54

935

29.7743

29.7743

0.59473

2

Drpa Marta
(SC Potsdam)

23

84

33

43

22

264

0.0154

0.0154

24

9

22

85

0.0067

0.0067

362

78

53

927

20.932

20.932

0.57382

3

van Daelen Deborah
(Allianz MTV Stuttgart)

26

92

25

19

32

369

0.0141

0.0141

29

14

38

147

0.0072

0.0072

221

40

28

623

22.5939

22.5939

0.57098

4

Drewniok Kimberly
(VC Wiesbaden)

22

81

34

55

16

279

0.0138

0.0138

15

23

16

95

0.0041

0.0041

254

55

53

606

19.5149

19.5149

0.54918

5

Korhonen Piia
(Dresdner SC)

24

86

28

36

13

280

0.0112

0.0112

39

7

30

126

0.0106

0.0106

344

75

51

876

21.4018

21.4018

0.54193

6

Segovia elles Dayana Patricia
(Rote Raben Vilsbiburg)

22

78

22

24

14

262

0.0107

0.0107

32

31

15

120

0.0095

0.0095

225

65

53

639

13.061

13.061

0.49444

7

Skinner Aisha
(VCO Berlin)

18

59

15

17

14

169

0.0109

0.0109

12

3

25

60

0.0045

0.0045

177

64

58

486

6.677

6.677

0.4579

8

Šunjić Jelena
(Ladies in black Aachen)

22

67

25

16

7

219

0.0087

0.0087

10

3

33

74

0.0027

0.0027

145

46

28

429

11.0886

11.0886

0.45697

9

Mercado Chavez Erika Andreina
(Schwarz-Weiß Erfurt)

19

60

15

28

10

176

0.0092

0.0092

20

12

19

83

0.0074

0.0074

221

75

56

605

8.9256

8.9256

0.45633

10

Patockova Tereza
(VfB Suhl LOTTO Thüringen)

19

60

17

36

10

176

0.0094

0.0094

20

14

13

71

0.0069

0.0069

156

55

38

442

8.552

8.552

0.45573

11

Neuhaus Frauke
(Ladies in black Aachen)

15

39

13

22

4

96

0.0065

0.0065

9

5

14

38

0.0035

0.0035

71

21

19

219

5.5205

5.5205

0.41003

12

Perovic Nikoleta
(Allianz MTV Stuttgart)

15

31

6

4

2

51

0.0035

0.0035

13

5

10

36

0.0056

0.0056

53

11

10

111

8.9369

8.9369

0.40143

13

Piest Madleen
(Schwarz-Weiß Erfurt)

16

45

8

16

6

90

0.0064

0.0064

5

4

10

29

0.0023

0.0023

55

32

23

192

0

0

0.38071

14

Cesar Annie
(Allianz MTV Stuttgart)

1

3

1

0

0

3

0.0058

0.0058

0

0

0

0

0

0

0

0

0

0

0

0

0.37281

15

Holzer Katharina
(VfB Suhl LOTTO Thüringen)

15

39

6

6

4

68

0.0043

0.0043

3

6

6

23

0.0013

0.0013

33

8

3

112

7.6607

7.6607

0.36112

16

Mach Annalena
(VC Wiesbaden)

5

15

3

5

0

26

0.0033

0.0033

0

4

0

7

0

0

13

1

1

25

6.6

6.6

0.35077

17

Bettendorf Martenne Julia
(SSC Palmberg Schwerin)

20

36

5

9

2

37

0.0021

0.0021

4

0

6

18

0.0012

0.0012

27

10

8

88

3.6818

3.6818

0.34212

18

Weske Emilia
(SC Potsdam)

8

9

0

2

1

8

0.0009

0.0009

1

0

0

3

0.0009

0.0009

5

1

1

18

1.5

1.5

0.32405

19

Maase Rica
(Dresdner SC)

8

12

1

3

1

9

0.0019

0.0019

0

0

2

5

0

0

9

1

2

22

3.2727

3.2727

0.32305

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