1. Bundesliga Frauen

1. Bundesliga Frauen - Hauptrunde

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

1

van Daelen Deborah
(Allianz MTV Stuttgart)

19

68

19

17

29

276

0.0163

0.0163

20

11

33

118

0.0068

0.0068

174

27

20

477

18.1048

18.1048

0.56889

2

Drpa Marta
(SC Potsdam)

20

72

28

37

20

226

0.0156

0.0156

19

8

18

69

0.0062

0.0062

301

60

45

762

18.5197

18.5197

0.56338

3

Drewniok Kimberly
(VC Wiesbaden)

20

75

33

50

15

259

0.0144

0.0144

15

21

16

93

0.0045

0.0045

233

48

48

552

18.6141

18.6141

0.55054

4

Lippmann Louisa
(SSC Palmberg Schwerin)

20

72

17

28

16

240

0.0102

0.0102

28

16

27

114

0.0087

0.0087

302

50

38

659

23.3809

23.3809

0.54064

5

Korhonen Piia
(Dresdner SC)

19

67

23

31

9

213

0.0111

0.0111

34

6

23

99

0.0118

0.0118

277

57

38

665

18.3368

18.3368

0.52784

6

Segovia elles Dayana Patricia
(Rote Raben Vilsbiburg)

20

72

19

21

10

235

0.0094

0.0094

32

29

14

114

0.0103

0.0103

208

55

49

578

12.955

12.955

0.48191

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

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

9

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

10

Šunjić Jelena
(Ladies in black Aachen)

18

55

19

10

6

175

0.0081

0.0081

5

2

25

55

0.0016

0.0016

120

35

24

343

9.7813

9.7813

0.44376

11

Neuhaus Frauke
(Ladies in black Aachen)

14

37

13

22

4

89

0.007

0.007

7

5

12

33

0.0029

0.0029

68

19

17

203

5.8325

5.8325

0.41545

12

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

13

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

14

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

15

Perovic Nikoleta
(Allianz MTV Stuttgart)

11

20

3

2

2

21

0.003

0.003

7

4

5

18

0.0042

0.0042

24

7

4

51

5.098

5.098

0.3534

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)

16

32

4

8

1

33

0.0019

0.0019

4

0

6

18

0.0015

0.0015

24

9

8

81

2.7654

2.7654

0.34052

18

Weske Emilia
(SC Potsdam)

7

8

0

2

1

7

0.001

0.001

1

0

0

3

0.001

0.001

5

1

1

16

1.5

1.5

0.32418

19

Maase Rica
(Dresdner SC)

6

10

1

3

1

8

0.0027

0.0027

0

0

2

5

0

0

9

1

2

20

3

3

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