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

Aigner-Swesey Delainey
(VC Wiesbaden)

26

100

37

51

24

369

0.013

0.013

38

10

16

92

0.0081

0.0081

252

67

52

706

18.8385

18.8385

0.54341

2

Lippmann Louisa
(Schweriner SC)

27

96

18

45

17

270

0.0082

0.0082

41

9

45

160

0.0096

0.0096

349

74

47

848

25.8113

25.8113

0.53527

3

Drpa Marta
(SC Potsdam)

25

100

25

45

21

296

0.0103

0.0103

25

7

17

78

0.0056

0.0056

313

85

46

887

20.5186

20.5186

0.52306

4

Mc Mahon Elizabeth Ann
(Dresdner SC)

27

100

16

52

11

275

0.0059

0.0059

52

6

27

151

0.0114

0.0114

339

91

56

875

21.9429

21.9429

0.49606

5

Iwohn Nele
(Köpenicker SC Berlin)

23

77

20

47

18

229

0.01

0.01

10

9

25

67

0.0026

0.0026

184

66

29

547

12.5283

12.5283

0.47617

6

Wilson Erica
(USC Münster)

18

66

17

27

10

214

0.0087

0.0087

8

15

7

49

0.0026

0.0026

214

58

49

576

12.2604

12.2604

0.4627

7

Whitney Aiyana
(Allianz MTV Stuttgart)

26

78

8

27

16

191

0.0055

0.0055

20

6

25

93

0.0046

0.0046

187

48

34

483

16.9565

16.9565

0.45843

8

van Daelen Deborah
(Allianz MTV Stuttgart)

28

80

15

14

19

206

0.0074

0.0074

13

5

21

62

0.0028

0.0028

132

31

29

416

13.8462

13.8462

0.45816

9

Baciu Iona Maria
(Ladies in Black Aachen)

23

79

3

14

6

188

0.0024

0.0024

9

9

16

70

0.0024

0.0024

198

42

41

521

17.4376

17.4376

0.4291

10

Mesa Luaces Liana
(Rote Raben Vilsbiburg)

16

51

8

21

7

145

0.0056

0.0056

8

16

3

55

0.003

0.003

141

40

27

341

11.0674

11.0674

0.42768

11

Patockova Tereza
(VfB 91 Suhl)

22

57

15

24

1

147

0.0045

0.0045

15

4

4

39

0.0042

0.0042

114

32

30

329

9.0091

9.0091

0.40974

12

Neuhaus Frauke
(Ladies in Black Aachen)

20

54

12

30

2

100

0.0041

0.0041

10

9

11

34

0.0029

0.0029

71

27

24

214

5.0467

5.0467

0.38471

13

Valongo de Castro Juliana "Lia"
(VfB 91 Suhl)

21

59

8

12

7

147

0.0044

0.0044

9

8

11

38

0.0027

0.0027

79

25

33

289

4.2872

4.2872

0.38394

14

Rolfzen Amber
(Dresdner SC)

12

34

3

10

5

91

0.0039

0.0039

11

3

19

61

0.0054

0.0054

48

14

14

155

4.3871

4.3871

0.38291

15

Holstein Marie
(Köpenicker SC Berlin)

14

34

4

14

4

67

0.0034

0.0034

5

2

11

28

0.0021

0.0021

48

14

9

171

4.9708

4.9708

0.37707

16

Große Scharmann Lena
(VC Olympia Berlin)

20

49

4

24

4

88

0.0025

0.0025

5

2

11

34

0.0016

0.0016

71

24

23

222

5.2973

5.2973

0.37058

17

Drewniok Kimberly
(SC Potsdam)

17

28

2

4

1

12

0.001

0.001

1

0

3

11

0.0003

0.0003

31

10

5

95

4.7158

4.7158

0.34464

18

Piest Madleen
(Schwarz-Weiß Erfurt)

17

33

2

5

5

61

0.0029

0.0029

0

2

5

12

0

0

30

20

14

123

-1.0732

-1.0732

0.34261

19

Mach Annalena
(VC Wiesbaden)

24

53

7

16

2

109

0.0021

0.0021

3

1

1

7

0.0007

0.0007

23

6

7

78

6.7949

6.7949

0.34123

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