Competition Site by Data Project

SUPERLIGA (M) 2022./2023.

SLM2223

Prvi krug natjecanja

Best players
- Best players
- Libero
- Wing-spiker
- Opposite
- Middle-Blocker
- Setter
Team recap
Team details
Team players
All Players

Middle-Blocker

SUPERLIGA (M) 2022./2023. Best players MIDDLE BLOCKER

Player

Played

Block

Serve

Attack

Ranking

Tot

Bl ind.

Tot

Sv ind.

Tot

Sp ind.

Index

Daca Benjamin
(OK RIBOLA KAŠTELA)

0,0273

128

0,0081

13,1351

0,53609

Mandura Ivan
(OK KITRO VARAŽDIN )

0,024

132

0,008

110

11,1818

0,51292

Stevanović Radiša
(HAOK MLADOST)

0,0157

13,1509

0,50453

Zelenika Hrvoje
(OKM CENTROMETAL)

0,0162

230

0,0056

137

231

34,9481

0,49465

Viloria Marquez Paul Alejandro
(MOK MURSA - OSIJEK)

0,0132

267

0,0089

157

29,3503

0,48336

Pavlović Fran
(OK KITRO VARAŽDIN )

0,0275

0,8571

0,47611

Da Silva Santos Pedro Luiz
(MOK MURSA - OSIJEK)

0,0125

162

0,0086

101

167

26,6587

0,47362

Barać Bruno
(OK RIBOLA KAŠTELA)

0,013

133

0,0116

15,2308

0,47157

Brčić Dominik
(OK MEDICINAR TRNJE)

0,0167

132

0,0074

209

11,488

0,46713

Šućur Kristijan
(OKM CENTROMETAL)

0,0104

268

0,0078

183

28,9836

0,46118

Jakopec Sven
(HAOK MLADOST)

0,012

122

0,0113

12,5088

0,45988

Sponza Gianluca
(OK ROVINJ)

0,0121

100

0,0108

6,8

0,44819

Gajić Tihomir
(HAOK MLADOST)

0,0139

0,0093

3,7143

0,4461

Vaško Antonio
(HAOK MLADOST)

0,0196

0,0028

0,5

0,44253

Kuruc Ivan
(OK MEDICINAR TRNJE)

0,014

0,0058

10,3175

0,44075

Karačić Mislav
(MOK MARSONIA)

0,0155

141

0,0048

6,3375

0,4377

Jurlin Dino
(OK ZADAR)

0,0158

0,0042

1,9701

0,42952

Maruna Patrik
(OK ZADAR)

0,0105

103

0,007

126

11,1111

0,42737

Ćosić Ivan
(OK SPLIT)

0,0125

144

0,0047

169

10,6864

0,42684

Kovačević Andrej
(OK ROVINJ)

0,0126

136

0,0042

10,7143

0,4257

Lasić Luka
(MOK RIJEKA)

0,0103

0,0051

6,8936

0,41007

Grgić Matko
(MOK MARSONIA)

0,0104

156

0,0027

101

13,6436

0,41006

Skočić Dejan
(OK SPLIT)

0,0158

1,8182

0,40908

Gudelj Ivan
(OK SPLIT)

0,0062

0,0047

14,2105

0,39669

Duraković Arnel
(OK SISAK)

0,0074

0,39602

Žeželić Franko
(MOK RIJEKA)

0,0086

106

0,0059

-2,2326

0,38951

Mezak Rene
(OK KITRO VARAŽDIN )

0,0056

0,0025

10,5778

0,37714

Škarpa Karlo
(OK ZADAR)

0,0036

0,0042

6,1765

0,36708

Šoš Dino
(MOK MURSA - OSIJEK)

0,0054

0,0027

1,8

0,36281

Fatorić Andrea
(OK ROVINJ)

0,0077

0,36179

Vukosavljević Domagoj
(MOK MURSA - OSIJEK)

0,0081

-1

0,3607

Senčar Fran
(OK SISAK)

0,0074

0,8182

0,35964

Matošević Domagoj
(OK MEDICINAR TRNJE)

0,0039

0,0026

2,5385

0,3551

Šango Roko
(OK ZADAR)

0,0078

0,35392

Majer Petar
(MOK MURSA - OSIJEK)

0,002

0,0025

4,8

0,34842

Janko Daniel
(OK ROVINJ)

0,0046

2,3636

0,34349

Krmpotić Mario
(MOK RIJEKA)

0,0024

0,34254

Tomić Luka
(OK MEDICINAR TRNJE)

0,0014

0,0029

0,34088

Jengić Filip
(OKM CENTROMETAL)

0,0017

0,33903

Šoše Luka
(OK ZADAR)

0,0009

0,0017

-1,625

0,33104

Banović Noa
(MOK RIJEKA)

0,0019

-0,8571

0,32774

Đulović Adnan
(MOK RIJEKA)

0,0016

0,3262

Orešković Bruno
(MOK MARSONIA)

0,31911

Martinović Ivan
(OK RIBOLA KAŠTELA)

-1,5

0,31911

Hinić Manuel
(OK ROVINJ)

0,31911

Šunjo Gabrijel
(MOK MURSA - OSIJEK)

-2

0,31911

Cota Davor
(OK SISAK)

0,31911

Kokot Josip
(MOK MARSONIA)

-2

0,31616

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: