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Football Goalscorer Predictions – Player Scoring Probabilities & Analysis
Explore football goalscorer predictions from Betiball with player-level scoring probabilities built around expected goals (xG), recent shots, minutes played, attacking role, penalty responsibility, current form and the defensive profile of the opposition.
Open any fixture for deeper match context, or compare player statistics, Under/Over 2.5 predictions, BTTS predictions, 1X2 predictions and today's goalscorer predictions.
Predictions 1x2
Under/Over 2.5
1X2 Live
Half Time
Half Time / Full Time
Both To Score
Double chance
Handicap
Scorers
Corners
Cards
Home team
Away team
Prob. %
Pred
Correct
score
score
Avg.
goals
goals
Coef.
Score
Weather
English Premier League
50%
Bryan Mbeumo
50%
Matheus Cunha
40%
Kiernan Dewsbury-Hall
1 - 1
2.00
3.60
NS
—
55%
João Pedro Junqueira de Jesus
33%
Cole Jermaine Palmer
28%
Christos Tzolis
2 - 1
3.00
4.20
NS
—
Spanish La Liga
55%
Pierre-Emerick Aubameyang
28%
Ayoze Perez
28%
Gerard Moreno Balaguero
2 - 2
4.00
5.25
FT
2 - 3
24℃～25℃
55%
Raphael Dias Belloli
33%
Fermin Lopez
28%
Hugo Duro
1 - 2
3.00
9.10
NS
—
55%
Roger Brugue
28%
Joaquin Munoz Benavides
28%
Daniel Lorenzo Guerrero
2 - 1
3.00
3.48
NS
—
55%
Mariano Diaz Mejia
55%
Ante Budimir
28%
Lucas Boye
2 - 1
3.00
3.60
NS
—
55%
Roberto Fernandez Jaen
28%
Tyrhys Dolan
28%
Javier Hernandez
1 - 1
2.00
3.17
NS
—
German Bundesliga
55%
Younes Ebnoutalib
28%
Jonathan Michael Burkardt
28%
Mert Komur
2 - 2
4.00
3.95
NS
—
Italian Serie A
55%
Donyell Malen
42%
Gianluca Scamacca
28%
Rodrigo Mora
2 - 1
3.00
3.40
FT
2 - 1
23℃～24℃
28%
Jose David Romero
28%
El Bilal Toure
28%
Andrea Colpani
1 - 2
3.00
3.11
NS
—
39%
Armand Lauriente
28%
Nicolo Cambiaghi
28%
Jens Odgaard
2 - 1
3.00
3.23
NS
—
28%
Jeremie Boga
28%
Nicolas Gonzalez
28%
Francisco Conceicao
2 - 1
3.00
3.33
NS
—
France Ligue 1
55%
Kamory Doumbia
28%
Mbwana Samatta
28%
Rassoul Ndiaye
2 - 1
3.00
3.40
FT
1 - 2
14℃～15℃
55%
Louis Mafouta
28%
Sofiane Diop
28%
Nathan NGoumou Minpole
2 - 2
4.00
4.35
FT
1 - 1
14℃～15℃
55%
Gessime Yassine
28%
Lucas Maronnier
28%
Hugo Picard
3 - 1
4.00
3.51
NS
—
55%
Esteban Lepaul
28%
Anthony Bermont
28%
Mohamed Amine Sbai
2 - 2
4.00
3.85
NS
—
55%
Amine Gouiri
28%
Igor Paixao
28%
Adilson Angel Abreu de Almeida Gomes
2 - 1
3.00
3.68
NS
—
Vietnamese professional league
55%
David Henen
37%
Milan Makaric
18%
Mamadou Pape Mbodj
1 - 1
2.00
3.00
NS
—
55%
Olahamichael
55%
Reon Moore
55%
Van Luong Ngo
1 - 2
3.00
3.10
NS
—
55%
Lam Ti Phong
2 - 1
3.00
9.00
NS
—
Indonesia Super League
55%
David Aparecido da Silva
34%
Ciro Henrique Alves Ferreira E Silva
31%
Tyronne Gustavo Del Pino Ramos
2 - 2
4.00
4.00
NS
—
55%
Carlos Franca
1 - 1
2.00
3.28
NS
—
55%
Andrew Jung
49%
Uilliam Barros Pereira
37%
Alex de Aguiar Gomes
2 - 1
3.00
7.30
NS
—
J-League Division 1
55%
Takuma Nishimura
55%
Koki Ogawa
55%
Yuki Soma
1 - 2
3.00
3.21
NS
—
25°C
55%
Leonardo de Sousa Pereira
21%
Aleksandar Cavric
14%
Harumi Minamino
2 - 1
3.00
3.57
NS
—
23°C
55%
Kazuki Tanaka
37%
Deniz Hummet
37%
Issam Jebali
1 - 2
3.00
3.62
NS
—
23°C
55%
Akito Suzuki
28%
Mutsuki Kato
28%
Sebastien Haller
1 - 1
2.00
4.10
NS
—
26°C
55%
Kaina Tanimura
22%
Yoshio Koizumi
22%
Yusuke Segawa
2 - 2
4.00
5.00
NS
—
55%
Solomon Sakuragawa
2 - 1
3.00
4.45
NS
—
55%
Marcio Augusto da Silva Barbosa,Marcinho
55%
Lazar Romanic
55%
Yasuto Wakisaka
1 - 1
2.00
3.63
NS
—
55%
Yuya Osako
55%
Thiago Santos Santana
2 - 1
3.00
4.81
NS
—
55%
Rafael Papagaio
55%
Motoki Nagakura
37%
Ryuma Nakano
2 - 1
3.00
4.75
NS
—
Korea League
55%
Ko Jae Hyeon
55%
Min-seo Moon
37%
Lee Gun Hee
1 - 1
2.00
5.35
NS
—
55%
Matheus Oliveira De Souza
39%
Abdallah Khalaihal
39%
Kim Dae Won
1 - 2
3.00
3.32
NS
—
USA Major League Soccer
55%
Prince Prince Owusu
47%
Milan Iloski
28%
Bruno Damiani
2 - 1
3.00
301.00
84
2 - 0
28℃～29℃
40%
Anders Dreyer
28%
Daryl Dike
28%
Antoine Griezmann
2 - 1
3.00
15.50
84
1 - 0
28℃～29℃
28%
Josef Martinez
28%
Brais Mendez
28%
Darren Yapi
2 - 1
3.00
101.00
86
2 - 0
28℃～29℃
55%
Guilherme Augusto Vieira dos Santos
50%
Pep Biel Mas Jaume
32%
Idan Toklomati
2 - 1
3.00
8.50
82
0 - 0
28℃～29℃
28%
Daniel Salloi
28%
Djordje Mihailovic
28%
Theo Corbeanu
2 - 2
4.00
37.00
85
3 - 4
22℃～23℃
55%
Julian Hall
43%
Jorge Ruvalcaba
28%
Paul Rothrock
2 - 2
4.00
3.30
45
0 - 0
18℃～19℃
55%
Petar Musa
28%
Logan Farrington
28%
Stephen Afrifa
3 - 1
4.00
3.65
45
2 - 2
33℃～34℃
28%
Jorge Alastuey
28%
Brandon Vazquez
28%
Facundo Torres
2 - 1
3.00
4.85
45
0 - 1
28℃～29℃
55%
Lionel Andres Messi
34%
Luis Suarez
28%
Aleksey Miranchuk
3 - 2
5.00
6.20
20
0 - 0
28℃～29℃
55%
Evander da Silva Ferreira
47%
Ahoueke Steeve Kevin Denkey
28%
Ayoub Jabbari
2 - 1
3.00
4.33
NS
—
28℃～29℃
55%
Denis Bouanga
45%
Sergi Solans
28%
Diego Luna
2 - 1
3.00
3.65
NS
—
29℃～30℃
55%
Carles Gil de Pareja Vicent
28%
Hirving Rodrigo Lozano Bahena
28%
Kyogo Furuhashi
1 - 2
3.00
3.80
NS
—
22℃～23℃
55%
Brian White
28%
Bruno Caicedo
28%
Thomas Muller
2 - 1
3.00
6.25
NS
—
16℃～17℃
55%
Kelvin Yeboah
28%
Kristoffer Velde
28%
David Pereira Da Costa
2 - 2
4.00
3.71
NS
—
14℃～15℃
Thai Premier League
55%
Denilson Pereira Junior
40%
Njiva Rakotoharimalala
18%
Caique Venancio Lemes
1 - 2
3.00
3.50
NS
—
55%
Bruno Henrique Baio da Cunha
42%
Mohamed Eisa
34%
Willen Mota Inacio
1 - 1
2.00
3.10
NS
—
55%
Guilherme Bissoli Campos
26%
Robert Zulj
12%
John Baggio
1 - 1
2.00
7.20
NS
—
Chinese Super League
55%
Rafael Rogerio da Silva, Rafael Ratao
26%
Landry Nany Dimata
22%
Luis Miguel Nlavo Asue
1 - 2
3.00
3.15
NS
—
55%
Felipe Silva
39%
Gustavo Henrique da Silva Sousa
29%
Wellington Alves da Silva
1 - 1
2.00
3.24
NS
—
55%
Oscar Taty Maritu
29%
Jefferson Pereira
26%
Guy Carel Mbenza Kamboleke
2 - 1
3.00
3.86
NS
—
55%
Alberto Quiles
40%
Wang Yudong
40%
Alexandru Mitrita
2 - 2
4.00
3.50
NS
—
Primera Division de Mexico
55%
Rafa Llorente
55%
Roberto Carlos Alvarado Hernandez
28%
David Rodriguez
1 - 1
2.00
4.00
FT
0 - 3
-2℃～-1℃
55%
Julian Carranza
37%
Juan Brunetta
28%
Rodrigo Aguirre
2 - 1
3.00
3.40
22
0 - 1
-2℃～-1℃
55%
Eugenio Pizzuto
28%
Manuel Vicente Capasso
28%
Luis Calzadilla
2 - 1
3.00
3.33
NS
—
21℃～22℃
55%
Miguel Angel Marquez Machado
55%
Nicolas Ibanez
28%
Luka Romero
2 - 1
3.00
6.40
NS
—
AFC Championship U20
55%
Adrian Segecic
41%
Gabriel Popovic
28%
Bernardo
2 - 2
4.00
3.75
NS
—
30°C
55%
Kanta Chiba
55%
Naoki Kumata
28%
Isa Sakamoto
1 - 1
2.00
19.00
NS
—
31°C
55%
Gurkirat Singh
28%
Maheson Tongbram Singh
1 - 1
2.00
4.00
NS
—
55%
Mohammad Amin Hazbavi
28%
Khuat Van Khang
2 - 2
4.00
7.00
NS
—
55%
Hayder Abdulkareem
28%
Mohammed Jameel Shanaa
1 - 2
3.00
3.25
NS
—
55%
Yong-hak Kim
28%
Young-Jun Lee
28%
Kang Seong-Jin
2 - 1
3.00
15.00
NS
—
55%
Amadoni Kamolov
1 - 2
3.00
6.00
NS
—
55%
Abbosbek Fayzullayev
55%
Zafarmurod Abdirahmatov
1 - 1
2.00
6.00
NS
—
55%
Abdullah Hadi Radif
18%
Musab Fahz Aljuwayr
18%
Ahmed Al-Rawi
2 - 1
3.00
3.75
NS
—
England Championship
55%
Eom Ji-Sung
55%
Adam Idah
28%
Moussa Yeo
1 - 1
2.00
3.20
FT
0 - 0
11℃～12℃
55%
Phil Neumann
55%
Marshall Munetsi
55%
Rodrigo Martins Gomes
2 - 2
4.00
3.28
NS
—
Portugal Primera Liga
55%
Luis Javier Suarez Charris
37%
Flavio Goncalves
28%
Rodrigo Zalazar
2 - 1
3.00
17.00
FT
2 - 0
20℃～21℃
55%
Goncalo Paciencia
28%
Vinicius Lopes Da Silva
28%
Tiago Ribeiro
1 - 1
2.00
3.83
NS
—
55%
Hector Hernandez Marrero
55%
Andre Clovis
28%
Gil Pinto Martins
1 - 1
2.00
3.69
NS
—
Holland Eredivisie
52%
Ricardo Pepi
45%
Guus Til
28%
Oscar Gloukh
2 - 2
4.00
3.80
FT
1 - 3
13℃～14℃
28%
Jaden Slory
28%
Uriel van Aalst
28%
Eser Gurbuz
1 - 3
4.00
3.55
FT
0 - 3
11℃～12℃
50%
Thom van Bergen
28%
Pelle Clement
28%
David van der Werff
2 - 2
4.00
3.85
NS
—
55%
Patrick Brouwer
28%
Jelani Seedorf
28%
Iwan Henstra
2 - 1
3.00
4.90
NS
—
45%
Trenskow Jacob
28%
Manuel rivera
28%
Ringo Meerveld
2 - 2
4.00
4.25
NS
—
28%
Yannick Eduardo
28%
Evan Rottier
28%
Mohammed Amine Ihattaren
2 - 1
3.00
3.83
NS
—
Primera Division de Colombiano-Apertura
55%
Dayro Mauricio Moreno Galindo
41%
Jefry Arley Zapata Ramirez
28%
Santiago Mera
1 - 1
2.00
3.80
FT
2 - 0
29℃～30℃
28%
Gustavo Torres
28%
Breiner Rodriguez
28%
Patrick Preciado
1 - 1
2.00
3.30
FT
2 - 2
12℃～13℃
28%
Nahuel Bustos
28%
Franco Fagundez
28%
Julio Sinisterra
1 - 1
2.00
6.50
FT
2 - 2
12℃～13℃
55%
Luis Fernando Muriel Fruto
28%
Guillermo Paiva
28%
Wilfrido De La Rosa Mendoza
2 - 1
3.00
6.90
NS
—
29℃～30℃
55%
Luciano Pons
48%
Emerson Batalla
2 - 1
3.00
—
NS
—
55%
Jorge Rivaldo
35%
Fabricio Sanguinetti
30%
Facundo Bone
1 - 1
2.00
3.75
NS
—
55%
Leonardo Castro
55%
Rodrigo Contreras
1 - 2
3.00
5.10
NS
—
Swedish Allsvenskan
55%
Paulos Abraham
37%
Noah Christoffersson
37%
Victor Lind
1 - 2
3.00
11.00
NS
—
55%
Leo Ostman
55%
Mads Kristian Hansen
37%
Ari Sigurpalsson
1 - 1
2.00
3.65
NS
—
Scottish Premier League
28%
Ismeal Kabia
28%
Fraser Taylor
28%
Chris Mochrie
2 - 2
4.00
5.50
FT
1 - 2
9℃～10℃
55%
Cheick Tidiane Diabate
55%
Josh Fowler
55%
Martin Boyle
2 - 1
3.00
3.21
NS
—
55%
Oisin McEntee
28%
Joshua McPake
28%
Claudio Braga
2 - 1
3.00
5.50
NS
—
Swiss Super League
55%
Amir Abrashi
55%
Lindrit Kamberi
55%
Umeh Emmanuel
2 - 2
4.00
3.65
FT
2 - 2
19℃～20℃
55%
Enoch Owusu
55%
Lukas Daschner
55%
Lukas Gortler
2 - 1
3.00
3.85
NS
—
55%
Winsley Boteli
33%
Brighton Labeau
22%
Donat Rrudhani
3 - 2
5.00
4.30
NS
—
55%
Zan Celar
41%
Asane Sow
41%
Dereck Moncada
1 - 3
4.00
3.75
NS
—
Turkish Super Liga
55%
Alexandros Kyziridis
28%
Diomande Mohammed
28%
Ylber Ramadani
1 - 1
2.00
4.35
NS
—
55%
Adrian Benedyczak
55%
Dia Saba
28%
Elson Mendes Da Silva
1 - 1
2.00
3.25
NS
—
55%
Mohamed Salah
28%
Aral Simsir
28%
Ernest Muci
1 - 1
2.00
5.40
NS
—
55%
Matej Maglica
55%
Berkan smail Kutlu
55%
Josafat Mendes
1 - 1
2.00
3.15
NS
—
Belgian Pro League
55%
Mustapha Isah
37%
Ryan Merlen
37%
Samed Bazdar
2 - 1
3.00
4.90
FT
4 - 0
14℃～15℃
55%
Michael Frey
41%
Dennis Dargahi
28%
Adnane Abid
1 - 1
2.00
3.60
FT
1 - 0
14℃～15℃
55%
Tiago Araujo
55%
Ortega Josue Vergara Yamir
28%
Dante Vanzeir
1 - 2
3.00
3.61
NS
—
55%
Tobias Hedl
55%
Istvan Atrok
28%
Ken Masui
1 - 2
3.00
3.63
NS
—
55%
Rafiu Durosinmi
28%
Danylo Sikan
28%
Mihajlo Cvetkovic
2 - 1
3.00
3.40
NS
—
28%
Jearl Margaritha
28%
Lennart Mertens
28%
Kurt Abrahams
2 - 1
3.00
3.70
NS
—
Costa Rica Primera Division
28%
Fernando Lesme
28%
Jose Alvarado
12%
Abner Hudson
2 - 1
3.00
5.50
FT
2 - 3
22℃～23℃
55%
Raheem Giusseppe Cole Martinez
1 - 2
3.00
3.20
NS
—
Primera Division de Honduras
55%
John Kleber
35%
Rodrigo Gomez
35%
Rodrigo de Olivera Donado
1 - 1
2.00
4.00
FT
0 - 0
33°C
55%
Nicolas Messiniti
30%
Roman Rubilio Castillo Alvarez
28%
Alexy Vega
1 - 1
2.00
4.75
NS
—
28°C
55%
Eddie Hernandez
50%
Kilmar Aldair Peña Oseguera
25%
Gustavo Alves Ezequiel Sousa
2 - 1
3.00
7.50
NS
—
Liga Nacional de Guatemala
55%
Nicolas Martinez Vargas
14%
Jonathan Josue Pozuelos
12%
Lynner Oneal Garcia Mejia
2 - 1
3.00
3.50
FT
1 - 1
24°C
1%
Roberto Meneses
1%
Jose Ochoa
1%
Marlon Chun
2 - 1
3.00
4.75
16
0 - 0
26°C
55%
Diego Casas
32%
Steven Cardenas
28%
Antonio López
2 - 1
3.00
3.10
NS
—
55%
Victor Avalos
50%
Uri Emanuel Amaral Benitez
44%
Janderson Kione Pereira Rodrigues
2 - 1
3.00
4.50
NS
—
55%
Jose Martinez
28%
Yasniel Matos
23%
John Mendez
2 - 0
2.00
10.00
NS
—
Bosnia and Herzegovina Premier League
55%
Toni Jovic
1%
Ognjen Klenpic
1%
Mohamed Ghorzi
1 - 1
2.00
3.00
FT
1 - 2
25°C
55%
Aldin Mesic
28%
Collin Seedorf
28%
Joao Erick
2 - 1
3.00
2.90
NS
—
55%
Luka Juricic
55%
David Vukovic
28%
Francis Kyeremeh
1 - 1
2.00
3.00
NS
—
55%
Domagoj Jelavic
28%
Matija Kolaric
28%
Mato Stanic
2 - 1
3.00
10.00
NS
—
Croatia 1.Division
55%
Toni Fruk
28%
Samuele Vignato
28%
Domagoj Bukvic
2 - 1
3.00
4.20
FT
4 - 1
18℃～19℃
55%
Michele Sego
40%
Rokas Pukstas
30%
Ante Rebic
2 - 3
5.00
6.80
NS
—
55%
Aleks Stojakovic
48%
Josip Mitrovic
48%
Jagusic Adriano
2 - 1
3.00
3.40
NS
—
Russia Premier League
55%
Gilson Benchimol Tavares
55%
Ruslan Kul
55%
Andrey Kasadzhikov
1 - 1
2.00
4.05
NS
—
55%
Artem Maksimenko
37%
Iker Pozo La Rosa
37%
Gamid Agalarov
2 - 1
3.00
4.40
NS
—
55%
Konstantin Tyukavin
55%
Manfred Alonso Ugalde Arce
55%
Anton Miranchuk
2 - 1
3.00
3.25
NS
—
55%
Chinonso Offor
55%
Brayan Gil Hurtado
28%
Sergei Varatynov
2 - 1
3.00
3.25
NS
—
Moldova Divizia Nationala
55%
Denis Kozlovskiy
18%
Vladimir Fratea
15%
Guy Dahan
1 - 1
2.00
11.00
NS
—
55%
Andrei Cobet
43%
Ze Flores
37%
Sibiry Keita
1 - 1
2.00
—
NS
—
Lithuania - 1.Division
55%
Yegor Glushach
47%
Xabi Auzmendi
39%
Hideyasu Tanaka
1 - 1
2.00
3.03
NS
—
55%
Ronald Sobowale
48%
Amine Benchaib
41%
Fabien Ourega
2 - 1
3.00
5.90
NS
—
55%
Nikola Petkovic
46%
Liviu Antal
37%
Yuri Kendysh
3 - 2
5.00
8.20
NS
—
hạng nhất Séc
55%
Matyas Vojta
55%
Michal Sevcik
2 - 2
4.00
3.30
NS
—
55%
Jan Chramosta
55%
Lamin Jawo
43%
Lukas Masek
1 - 1
2.00
3.15
NS
—
55%
Michal Bilek
48%
Michal Cupak
1 - 2
3.00
3.15
NS
—
55%
Lukas Haraslin
50%
Albion Rrahmani
44%
Jan Kuchta
2 - 1
3.00
3.35
NS
—
Cyprus 1 Division
55%
Hrvoje Milicevic
49%
Djordje Ivanovic
49%
Riad Bajic
2 - 2
4.00
4.90
NS
—
55%
Stefan Drazic
43%
Luther Singh
43%
Nicolas Koutsakos
1 - 2
3.00
3.54
NS
—
Latvian Higher League
55%
Artjoms Puzirevskis
37%
Rolandas Baravykas
18%
Davids Druzinins
2 - 1
3.00
5.80
NS
—
Estonia Champions League
55%
Bubacarr Tambedou
39%
Wendell
28%
Henri Anier
1 - 2
3.00
4.55
NS
—
55%
Karel Eerme
47%
Tristan Koskor
47%
Pavel Marin
2 - 1
3.00
3.50
NS
—
Iceland Premier Division
55%
Kennie Chopart
41%
Jakob Bystrom
28%
Kristofer Konradsson
2 - 2
4.00
4.90
NS
—
55%
Ulfur Agust Bjornsson
37%
Gils Gislason
37%
Adolf Dadi Birgisson
2 - 2
4.00
3.65
NS
—
55%
Omar Sowe
55%
Bjarki Bjorn Gunnarsson
2 - 1
3.00
3.90
NS
—
55%
Hallgrimur Mar Steingrimsson
55%
Sveinn Margeir Hauksson
37%
Arnar Gunnleifsson
2 - 2
4.00
3.80
NS
—
55%
Aron Sigurdarson
33%
Eidur Gauti Saebjornsson
28%
Luke Morgan Conrad Rae
2 - 2
4.00
4.15
NS
—
Saudi Professional League
55%
Angelo Gabriel Borges Damaceno
55%
Samuel Almeida Costa
55%
Joao Felix Sequeira
2 - 2
4.00
5.75
FT
2 - 1
32℃～33℃
Israel Premier League
28%
Cedric Franck Don
28%
Bruno Roberto Pereira Da Silva
28%
Kenny Saief
2 - 1
3.00
4.20
FT
2 - 1
23℃～24℃
Slovak Super Liga
55%
Andraz Sporar
50%
Ammar Ramadan
32%
Tigran Barseghyan
1 - 2
3.00
3.60
FT
2 - 0
20℃～21℃
55%
Roman Cerepkai
37%
Matyas Kovacs
31%
Milan Rehus
2 - 1
3.00
5.00
NS
—
55%
Abdulrahman Taiwo
46%
Kudlicka Timotej
46%
Jan Hladik
1 - 2
3.00
4.33
NS
—
Brazil Serie A
55%
Luciano Batista da Silva Junior
28%
Wallace Yan
28%
Lucas Henrique Barbosa
1 - 1
2.00
3.30
FT
2 - 3
18℃～19℃
55%
Luciano da Rocha Neves
31%
Tomas Cuello
28%
Gustavo Santana
1 - 1
2.00
3.40
FT
2 - 0
16℃～17℃
31%
Agustin Canobbio Graviz
28%
Yeferson Julio Soteldo Martinez
28%
Paulo Henrique Chagas de Lima,Ganso
2 - 1
3.00
3.90
62
0 - 0
22℃～23℃
55%
Breno Henrique Vasconcelos Lopes
48%
Pedro Rocha Neves
34%
Joaquin Lavega Colzada
1 - 1
2.00
3.18
NS
—
55%
Pedro Guilherme Abreu dos Santos
29%
Samuel Dias Lino
26%
Jandir Breno Souza Silva
1 - 2
3.00
6.20
NS
—
55%
Gabriel Barbosa
41%
Johan Carbonero
41%
Neymar da Silva Santos Junior
1 - 1
2.00
3.90
NS
—
55%
Kevin Stiven Viveros Rodallega
19%
Matheus Pereira
19%
Kaio Jorge Pinto Ramos
1 - 1
2.00
3.77
NS
—
55%
Jose Manuel Lopez
48%
Danilo Dos Santos De Oliveira
48%
Mauricio Magalhaes Prado
1 - 2
3.00
3.45
NS
—
Primera Division de Chile
55%
Fernando Zampedri
29%
Justo Giani
28%
Alan Medina
2 - 2
4.00
3.50
FT
0 - 1
11℃～12℃
55%
Eduardo Jesus Vargas Rojas
55%
Nicolas Johansen
28%
Gonzalo Reyna
1 - 1
2.00
5.50
63
0 - 0
11℃～12℃
55%
Jeisson Vargas
44%
Felipe Chamorro
44%
Ignacio Jeraldino
1 - 2
3.00
3.35
NS
—
55%
Jorge Sebastia Saez
34%
Francisco Gonzalez
28%
Thiago Vecino Berriel
1 - 1
2.00
4.05
NS
—
55%
Marcelo Javier Correa
41%
Lionel Altamirano
34%
Maximiliano Samuel Romero
1 - 2
3.00
3.90
NS
—
55%
Nelson Da Silva
24%
Cecilio Alfonso Waterman Ruiz
3 - 1
4.00
4.52
NS
—
Slovenia 1.Liga
39%
Antonio Marin
28%
Dino Kojic
28%
Luis Amaranto Perea
1 - 1
2.00
5.00
FT
3 - 0
17℃～18℃
55%
Benjamin Tetteh
40%
Dario Vizinger
30%
El Arbi Hillel Soudani
2 - 1
3.00
5.90
NS
—
55%
Franko Kovacevic
35%
Andraz Ruedl
30%
Nikita Iosifov
1 - 1
2.00
3.45
NS
—
Romania Liga I
55%
Sergio Ribeiro
55%
Daniel Pirvulescu
55%
Andrej Fabry
1 - 1
2.00
3.05
NS
—
55%
Alexandru Stefan Pascanu
55%
Fabio Patrick dos Reis dos Santos Fernan
55%
Joao Pedro Santos Lameira
1 - 1
2.00
3.15
NS
—
Denmark Superligaen
55%
Kelvin Osemudiamen Ehibhatiomhan
37%
Haidara Mohamed Cherif
37%
Adrian Runason Justinussen
1 - 2
3.00
3.47
NS
—
55%
Mohamed Elyounoussi
37%
Birger Meling
18%
Mads Emil Madsen
1 - 1
2.00
3.72
NS
—
55%
Mike Themsen
55%
Olti Hyseni
55%
Mads Frokjaer
3 - 1
4.00
5.90
NS
—
Argentine Division 1
41%
Ignacio Russo
28%
Lucas Janson
28%
Mateo Seoane
1 - 1
2.00
2.75
FT
2 - 1
11℃～12℃
28%
Facundo Lencioni
28%
Esteban Fernandez
28%
Luciano Cingolani
2 - 1
3.00
3.65
FT
2 - 2
11℃～12℃
55%
Rodrigo Auzmendi
28%
Alexis Ricardo Cuello
28%
Facundo Farias
1 - 1
2.00
2.88
FT
1 - 0
12℃～13℃
28%
Simon Escobar
28%
Juan Policella
28%
Matias Pellegrini
1 - 1
2.00
12.00
52
1 - 0
12℃～13℃
55%
Angel Fabian Di Maria
39%
Enzo Nahuel Copetti
39%
Matias Coccaro
1 - 1
2.00
6.00
NS
—
55%
Gabriel Avalos Stumpfs
35%
Michael Nicolas Santos Rosadilla
25%
Matias Abaldo
1 - 1
2.00
3.25
NS
—
55%
Fabrizio Sartori
41%
Sebastian Driussi
41%
Alex Adrian Arce Barrios
1 - 1
2.00
4.78
NS
—
55%
Leandro Diaz
1 - 1
2.00
4.25
NS
—
UAE Pro-League
55%
Walid Azarou
34%
Cheickna Doumbia
34%
Abdoulaye Toure
2 - 1
3.00
3.55
NS
—
55%
Omar Kharbin
29%
Karim El Berkaoui
16%
Mohamed El Khaloui
1 - 2
3.00
4.35
NS
—
Belarusian Premier League
55%
Ksenаfontau Aliaksandr
55%
Ignat Pranovich
1 - 2
3.00
4.35
NS
—
55%
Nikita Golub
44%
Dan Spataru
1 - 1
2.00
5.20
NS
—
55%
Kirill Kirilenko
46%
Fedor Lebedev
37%
Martin Artyukh
2 - 1
3.00
3.20
NS
—
55%
Bassekou Diabate
55%
Artem Kontsevoj
55%
Evgeni Malashevich
1 - 1
2.00
3.30
NS
—
Bolivia Primera Division
55%
Felix Imanol Cardenas Lopez
28%
Victor Abrego
28%
Rafael Allan Mollercke, Rafinha
2 - 1
3.00
4.00
FT
1 - 1
17℃～18℃
55%
Rodrigo Luis Ramallo Cornejo
41%
Leonardo Viviani
41%
Alex Caceres
2 - 1
3.00
5.75
FT
3 - 0
17℃～18℃
55%
Enrique Luis Triverio
28%
Jose Berdecio
28%
Leonardo Vaca Gutierrez
2 - 1
3.00
54.00
88
1 - 2
17℃～18℃
55%
Martin Cauteruccio
21%
Leonel Justiniano Arauz
21%
Willie Hortencio Barbosa
2 - 2
4.00
4.65
NS
—
55%
Darwin Lom
37%
Adrian Estacio
37%
Ramiro Eguez Lima
2 - 1
3.00
9.50
NS
—
55%
Bayron Garces
41%
Damian Villalba
28%
William Alvarez
2 - 1
3.00
5.00
NS
—
Swiss Challenge League
55%
Joris Manquant
50%
Antonio Marchesano
40%
Elias Pasche
2 - 2
4.00
5.60
NS
—
Poland Ekstraklasa
55%
Karol Czubak
37%
Bartosz Kapustka
28%
Mateusz Szczepaniak
2 - 1
3.00
3.75
FT
2 - 3
16℃～17℃
55%
Ajdin Hasic
37%
Peter Federico
37%
Kacper Urbanski
1 - 1
2.00
3.35
NS
—
55%
Mahir Madatov
55%
Allahyar Sayyadmanesh
37%
Patryk Makuch
2 - 1
3.00
4.40
NS
—
55%
Nik Prelec
55%
Piotr Samiec-Talar
37%
Przemyslaw Banaszak
1 - 2
3.00
4.06
NS
—
Norwegian Tippeligaen
55%
Lucas Haren
46%
Daniel Seland Karlsbakk
37%
Carl Lange
2 - 2
4.00
3.65
NS
—
55%
Emil Breivik
47%
Eirik Hestad
31%
Teodor Haltvik
2 - 2
4.00
8.40
NS
—
55%
Heine Larsen
55%
Jens Hjertoe-Dahl
39%
Alexander Thongla-Iad Warneryd
1 - 2
3.00
4.45
NS
—
Georgia Primera Division
55%
Bjorn Johnsen
31%
Tornike Morchiladze
18%
Komnen Andric
2 - 2
4.00
3.30
NS
—
Hungary Borsodi Liga
28%
Andor Lapu
28%
Zsolt Haraszti
28%
Milan Peto
2 - 2
4.00
3.60
FT
2 - 4
18℃～19℃
55%
Daniel Lukacs
31%
Alen Skribek
28%
Joao Victor
2 - 1
3.00
3.19
NS
—
55%
Aljosa Matko
35%
Zsombor Gruber
20%
Franko Kovacevic
2 - 1
3.00
6.50
NS
—
55%
Nadhir Benbouali
32%
Milan Vitalis
28%
Zeljko Gavric
2 - 2
4.00
3.60
NS
—
Greece National A
55%
Luka Jovic
28%
Barnabas Varga
28%
Benjamin Garre
2 - 1
3.00
6.60
FT
5 - 0
22℃～23℃
55%
Cristopher Nunez
1 - 1
2.00
3.65
NS
—
55%
Gustav Granath
28%
Charis Mavrias
28%
Kosta Aleksic
2 - 1
3.00
3.95
NS
—
55%
Dimitrios Pelkas
18%
Cyriel Dessers
18%
Shola Shoretire
2 - 1
3.00
3.30
NS
—
North Central America Leagues Cup
55%
Joao Paulo Dias Fernandes
28%
Jesus Angulo
28%
Sergio Canales Madrazo
2 - 1
3.00
3.47
NS
—
Spanish Segunda Division
55%
Guillermo Macho
55%
Mariano Carmona
28%
Jordi Martin
2 - 1
3.00
3.25
FT
0 - 0
22℃～23℃
55%
Alex Fores
28%
Pablo Saenz
1 - 1
2.00
3.87
NS
—
Italian Serie B
55%
Giuseppe Cuomo
28%
Francesco Forte
28%
Bernat Guiu
1 - 2
3.00
3.30
NS
—
55%
Cristian Shpendi
28%
Matteo Guidi
28%
Dimitri Bisoli
2 - 1
3.00
3.16
NS
—
55%
Giacomo Olzer
55%
Giuseppe Caso
55%
Raffaele Russo
2 - 1
3.00
4.60
NS
—
55%
Marco Bellich
28%
Kevin Piscopo
28%
Nardo Di Antonio
2 - 1
3.00
3.00
NS
—
55%
Samuele Mulattieri
28%
Amin Sarr
28%
Abdou Harroui
2 - 1
3.00
4.17
NS
—
55%
Pietro Fusi
28%
Jari Vandeputte
28%
Adin Licina
2 - 1
3.00
3.61
NS
—
Portugal Segunda Liga
55%
Heinz Mörschel
15%
Jose Sampaio
15%
Mario Rivas
1 - 1
2.00
3.10
NS
—
55%
Falcao Carolino
41%
Rui Costa
28%
Andre Candeias
1 - 1
2.00
3.25
NS
—
55%
Juan Munoz
24%
Pablo Fernandez
24%
Jose Pedro Barros Goncalves
1 - 1
2.00
3.55
NS
—
German Bundesliga 2
55%
Jonas Sterner
28%
Ben Bobzien
28%
Niklas Hauptmann
2 - 1
3.00
3.40
FT
1 - 2
13℃～14℃
28%
Adrian Beck
28%
Aaron Opoku
28%
Max Marie
2 - 2
4.00
3.70
NS
—
55%
Josip Brekalo
55%
Sebastian Gronning
55%
Alexander Nollenberger
1 - 2
3.00
4.15
NS
—
55%
Felix Klaus
33%
Jan Schoppner
28%
Dennis Srbeny
2 - 2
4.00
3.85
NS
—
J-League Division 2
55%
Ota Yamamoto
47%
Toya Izumi
39%
Caprini
2 - 2
4.00
3.50
NS
—
24°C
55%
Naoya Komazawa
55%
Matheus Moraes
2 - 1
3.00
4.00
NS
—
22°C
55%
Ayumu Kameda
41%
Kiyoshiro Tsuboi
1 - 1
2.00
3.22
NS
—
France Ligue 2
55%
Thierno Ballo
55%
Irvin Cardona
28%
Zurab Davitashvili
2 - 1
3.00
4.75
FT
3 - 1
15℃～16℃
Holland Jupiler League
55%
Elias Sierra
55%
Ilias Breugelmans
1 - 2
3.00
3.65
NS
—
Peru Primera Division
37%
Nahuel Isaias Lujan
28%
Javier Andres Sanguinetti
28%
Piero Magallanes
2 - 1
3.00
6.00
FT
2 - 1
19°C
55%
Facundo Callejo
28%
Nicolas Silva
28%
Diego Soto
2 - 1
3.00
8.90
FT
2 - 0
15°C
55%
Alex Valera
44%
Matias Eric Sen
28%
Gianluca Lapadula
2 - 1
3.00
8.00
NS
—
21°C
55%
Franco Torres
47%
Jarlin Quintero
31%
Luis Iberico
2 - 1
3.00
7.50
NS
—
55%
Beto Da Silva
47%
Christopher Olivares
31%
Francisco Andres Arancibia Silva
2 - 1
3.00
4.40
NS
—
55%
Erick Leonel Castillo Arroyo
20%
Renzo Renato Garces Mori
16%
Jairo David Velez Cedeno
1 - 1
2.00
4.75
NS
—
55%
Johnny Víctor Vidales Lature
49%
Bernardo Cuesta
31%
Nicolas Quagliata
2 - 1
3.00
6.80
NS
—
Bulgaria Premier League
55%
Kristian Dimitrov
55%
Frederic Maciel
28%
Armstrong Okoflex
2 - 1
3.00
4.70
FT
1 - 0
14℃～15℃
55%
Julien Lamy
55%
Vasil Panayotov
2 - 1
3.00
3.01
NS
—
55%
Spas Delev
55%
Dimitar Kostadinov
55%
Ivaylo Chochev
1 - 1
2.00
5.35
NS
—
Poland Liga 1
55%
Vladislavs Gutkovskis
18%
Michal MIlewski
18%
Kacper Nowakowski
2 - 1
3.00
3.25
NS
—
55%
Marcin Urynowicz
55%
Lucjan Klisiewicz
55%
Kamil Wojtyra
2 - 1
3.00
3.45
NS
—
55%
Nikodem Zielonka
55%
Konrad Kasolik
55%
Andrzej Trubeha
2 - 1
3.00
3.25
NS
—
Primera Division de Paraguay
28%
Pablo Gaston Gerzel
28%
Marcelo Ferreira
28%
Tobias Gabriel Portillo Avalos
1 - 1
2.00
3.50
FT
1 - 2
18°C
55%
Adrian Alcaraz
46%
Nestor Abraham Camacho Ledesma
18%
Richard Ortiz
2 - 1
3.00
3.10
NS
—
55%
Lorenzo Melgarejo
24%
Estiven Perez
18%
Federico Carrizo
2 - 1
3.00
4.25
NS
—
55%
Ivan Maggi
47%
Elvio Vera
24%
Sebastián Quintana
1 - 2
3.00
3.15
NS
—
K League Challenge League
55%
Charles Lokolingoy
37%
Kim Jong Min
37%
Isnairo Reis Silva Morais
1 - 1
2.00
4.75
NS
—
55%
Luis Mina
37%
Yun Min Ho
37%
Djalma
1 - 1
2.00
3.30
NS
—
Primera Division de Ecuador
37%
Matias Perello
37%
Djorkaeff Neicer Reasco Gonzalez
37%
Franco Posse
2 - 1
3.00
6.00
FT
3 - 1
18°C
55%
Byron Efrain Palacios Velez
44%
Emiliano Clavijo
33%
Jose Fajardo Nelson
1 - 1
2.00
3.40
NS
—
55%
Bruno Miranda
46%
Dario Benedetto
37%
Jhonny Raul Quinonez Ruiz
2 - 1
3.00
3.60
NS
—
55%
Jorge Antonio Ordonez Galarce
55%
Vilinton Branda
44%
Matias Klimowicz
1 - 1
2.00
4.30
NS
—
Primera Division de Venezuela
44%
Isai Valladares
33%
Ruben Rojas
28%
Jose Perez
1 - 1
2.00
3.90
FT
1 - 1
28℃～29℃
55%
Ronald Rodriguez
44%
Nicolas Ladislao Fedor Flores, Miku
33%
Kavier Ortiz
1 - 1
2.00
3.00
NS
—
55%
Enderson Abreu
55%
Jesus Yendis
37%
Michael O'neal Covea Uzcategui
1 - 1
2.00
3.00
NS
—
55%
Cristian Romero
55%
Jose Luis Ochoa
2 - 1
3.00
9.50
NS
—
55%
Jose Ali Meza Draegertt
55%
Anthony Chelin Uribe Francia
55%
Flabian Londono
2 - 1
3.00
6.50
NS
—
Serbian Superliga
28%
Luka Pejovic
28%
Đorđe Đorđić
28%
Mitar Ergelas
1 - 1
2.00
3.30
FT
0 - 0
27°C
55%
Aleksandr Shestyuk
28%
John Mary
28%
Milos Spasic
2 - 1
3.00
3.20
FT
1 - 0
26°C
55%
Aleksandar Katai
29%
Vasilije Kostov
26%
Andrej Kostic
2 - 1
3.00
6.70
NS
—
55%
Milutin Vidosavljevic
47%
Lazar Randelovic
47%
Aleksa Vukanovic
2 - 1
3.00
9.70
NS
—
Turkey 2 Ligi A
55%
Mevlüt Simsek
28%
Ramzi Safuri
28%
Nikola Storm
1 - 1
2.00
3.30
NS
—
Uruguay Primera Division
55%
Douglas Matias Arezo Martinez
28%
Ivan Rossi
28%
Cristian Jaime
1 - 2
3.00
4.48
FT
0 - 4
7℃～8℃
55%
Christian Tabo
47%
Santiago Ramirez
31%
Renato Cesar Perez
2 - 1
3.00
3.60
FT
2 - 0
7℃～8℃
55%
Tomas Habib
1 - 1
2.00
3.58
NS
—
55%
Brian Montenegro
55%
Raul Andres Tarragona Lemos
1 - 1
2.00
3.20
NS
—
55%
Maximiliano Gomez
25%
Fernando Mimbacas
17%
Ramiro Peralta
1 - 1
2.00
4.15
NS
—
55%
Federico Andres Martinez Berroa
39%
Facundo Munoa
39%
Ruben Bentancourt
1 - 1
2.00
3.48
NS
—
Ukrainian Premier League
55%
Matvii Ponomarenko
43%
Mark Assinor
43%
Prosper Obah
1 - 1
2.00
3.17
NS
—
55%
Pylyp Budkivsky
43%
Nemanja Andjusic
24%
Vitaliy Boyko
1 - 2
3.00
3.25
NS
—
55%
Joaquin Cifuentes
2 - 1
3.00
3.11
NS
—
Brazil Serie B
55%
Juan Miritello
33%
Mailton dos Santos de Sa
28%
Kadu
1 - 1
2.00
2.95
FT
0 - 1
26℃～27℃
55%
Robson dos Santos Fernandes
28%
Nicolas Mores da Cruz
28%
Reidiney
2 - 1
3.00
6.25
FT
3 - 1
26℃～27℃
55%
Gustavo Coutinho
28%
Luiz Gustavo da Silva Machado Duarte
28%
Fabio de Lima Costa
1 - 1
2.00
3.55
FT
2 - 2
9℃～10℃
55%
Pedro Henrique Perotti
47%
Chrystian Barletta
31%
Melk
1 - 1
2.00
3.15
FT
2 - 1
26℃～27℃
55%
Bruno Santos
35%
Gabriel Boschilia
20%
Pablo Felipe Teixeira
1 - 1
2.00
3.05
NS
—
Argentine Division 2
28%
Diego Becker
28%
Agustin Gallo
28%
Kevin Colli
1 - 1
2.00
3.70
FT
2 - 0
11℃～12℃
55%
Mauricio Asenjo
28%
Gabriel Paredes
28%
Nicolas Molina
1 - 1
2.00
3.00
FT
0 - 0
11℃～12℃
55%
Maximiliano Melendez
55%
Alejandro Quintana
28%
Cristian Guanca
1 - 1
2.00
2.82
FT
0 - 1
14℃～15℃
55%
Ramiro Reynoso
28%
Lazaro Romero
28%
Ulises Delgado
1 - 1
2.00
2.82
FT
0 - 1
11℃～12℃
55%
Manuel Ignacio Lago
55%
Franco Fagundez Acosta
37%
Alan Leonel Bonansea
1 - 1
2.00
4.75
FT
1 - 2
32℃～33℃
55%
Martin Pino
31%
Vicente Poggi
1 - 1
2.00
2.81
NS
—
55%
Martiniano Moreno
46%
Brian Leonel Blando
1 - 0
1.00
4.10
NS
—
55%
Nazareno Funez
34%
Maximiliano Rogoski
1 - 1
2.00
3.35
NS
—
55%
Octavio Bianchi
46%
Cristian Menendez
46%
Mateo Daniel Benegas
2 - 1
3.00
2.97
NS
—
55%
Juan Pablo Gobetto
1 - 1
2.00
3.40
NS
—
55%
Luis Alberto Silba
39%
Guillermo Nicolas Sanchez
1 - 1
2.00
4.85
NS
—
55%
Facundo Ardiles
1 - 1
2.00
3.30
NS
—
55%
Lautaro Gordillo
43%
Juan Ignacio Sanchez Sotelo
1 - 1
2.00
2.81
NS
—
Belgian Second Division
55%
Aaron Bibout
39%
Victory Beniangba
2 - 2
4.00
3.40
FT
3 - 0
18°C
55%
Oumar Diouf
48%
Frederic Soelle Soelle
1 - 2
3.00
3.30
NS
—
55%
Mohamed Soumaré
47%
Vancy Romeo Mabanza
2 - 2
4.00
3.35
NS
—
World Cup Nữ U20
55%
Michelle Cooper
55%
Alyssa Thompson
2 - 1
3.00
6.10
NS
—
Finland Ykkonen
55%
Torfiq Ali-Abubakar
39%
Oiva Laaksonen
39%
Imani Lanquedoc
2 - 1
3.00
8.10
NS
—
South Africa Premier Soccer League
55%
Sede Junior Dion
50%
Iqraam Rayners
23%
Tashreeq Matthews
1 - 1
2.00
6.60
NS
—
55%
Flavio Antonio da Silva
44%
Siviwe Magidigidi
33%
Maliele Vincent Pule
1 - 1
2.00
5.10
NS
—
Nigerian Premier League
55%
Vincent Temitope
24%
Gideon Monday
24%
Johnmark Aule
2 - 1
3.00
7.00
NS
—
55%
Alex Oweilayefa
47%
Oke Solomon
31%
Anas Yusuf
2 - 1
3.00
5.00
NS
—
55%
Rabiu Ali
47%
Terdoo Shimagande
47%
Abdulaziz Dalhatu
2 - 0
2.00
—
NS
—
55%
Godwin Obaje
49%
Chidozie Iwundu
49%
Evwierhurhoma Igbunu
1 - 1
2.00
—
NS
—
55%
Joseph Arumala
2 - 1
3.00
—
NS
—
55%
Sodiq Ibrahim
49%
Chidera Michael
37%
Qamar Adegoke
2 - 0
2.00
—
NS
—
55%
Chukwuemeka Obioma
1 - 1
2.00
—
NS
—
Ireland FAI Cup
55%
Jamie McGonigle
28%
Declan Glass
28%
Brandon Kavanagh
2 - 2
4.00
3.90
NS
—
Austrian Cup
55%
Guido Burgstaller
55%
Christopher Cvetko
37%
Jonas Arweiler
2 - 2
4.00
17.00
FT
2 - 5
21°C
55%
Chukwubuike Adamu
37%
Amar Dedic
37%
Benjamin Sesko
1 - 2
3.00
12.00
NS
—
55%
Thai Baribo
37%
Dario Vizinger
1 - 2
3.00
7.00
NS
—
Norway Adeccoligaen
55%
Magnus Lankhof-Dahlby
44%
Oscar Aga
33%
Jacob Hanstad
3 - 2
5.00
5.70
FT
2 - 3
9℃～10℃
Kazakhstan Premier League
55%
Saif Popov
55%
Adilet Kenesbek
1 - 1
2.00
3.10
NS
—
55%
Roman Murtazayev
2 - 1
3.00
4.33
NS
—
55%
Zhaslan Zhumashev
2 - 1
3.00
4.75
NS
—
Liga Super Malaysia
55%
Chrigor Flores Moraes
14%
Faisal Halim
14%
Alvin Fortes
2 - 1
3.00
12.00
NS
—
Chinese Football Association Jia League
55%
Jerome Ngom Mbekeli
1 - 1
2.00
3.26
NS
—
55%
Vinni Triboulet
28%
Yang Chaosheng
28%
Rodrigo Henrique
1 - 1
2.00
5.90
NS
—
55%
Noel Mbo
48%
Loic Lapoussin
28%
Dongtao Chen
2 - 1
3.00
5.30
NS
—
Danish 1st Division
55%
Kasper Nygaard Andersen
37%
Isak Taannander
31%
Mathias Kubel
1 - 2
3.00
3.40
NS
—
Hong Kong Premier League
55%
Samuel Salustiano de Jesus Silva
18%
Weverton Guilherme Constancio Pereira
11%
Vinicius Soares
1 - 2
3.00
5.10
NS
—
55%
Everton Camargo
49%
Noah Baffoe
24%
Wong Wai
1 - 3
4.00
10.00
NS
—
55%
Adrian Revilla
49%
Leandro Martinez Ortiz
49%
Yumemi Kanda
2 - 2
4.00
5.20
NS
—
USL First Division
55%
Neco Brett
2 - 1
3.00
3.60
FT
2 - 2
29°C
55%
Paolo DelPiccolo
28%
Napo Matsoso
28%
Cameron Lancaster
2 - 1
3.00
6.50
87
1 - 1
18°C
Algerian Ligue Professionnelle 1
1%
Imed Saihi
1%
Mohamed Senhadji
1%
Mohamed Larbi
2 - 1
3.00
3.20
FT
1 - 1
24°C
49%
Youcef Amine Laouafi
1%
Omrani S.
1%
Belkacem Brahimi
1 - 1
2.00
3.30
FT
3 - 3
31°C
BRA SPC
55%
Leonardo Passos Alves
2 - 1
3.00
7.50
FT
7 - 0
22°C
Goalscorer Predictions — Estimating Which Players Are Most Likely to Score
A goalscorer prediction estimates the likelihood that an individual player will score in a football match. Unlike 1X2 or total-goals analysis, this is a player-level problem. Team strength still matters, but the forecast also depends on who is likely to play, where that player is used, how often they shoot and how many high-quality chances usually fall to them.
Betiball treats scoring probability as an estimate rather than a guarantee. A striker with the strongest probability on the card can still fail to score, while a lower-rated player can convert a single chance. The useful information lies in how the probabilities rank the available players and how well the supporting data explains that ranking.
Goalscorer probability is not the same as team goal probability
A team may be strongly expected to score without one player accounting for most of that probability. Goals can be distributed across several forwards, midfielders and set-piece threats. Player-specific analysis therefore needs more detail than simply knowing that the team has a high expected-goals total.
Role matters as much as reputation
A famous attacker is not automatically the strongest scorer candidate in every fixture. Starting position, expected minutes, penalty duty and current tactical role can materially change a player's opportunity to score.
Anytime Goalscorer, First Goalscorer and Player Scoring Probability
Goalscorer markets can describe different events, so the prediction must match the question being asked.
| Market | What It Measures | Key Difference |
|---|---|---|
| Anytime Goalscorer | Whether the player scores at any point in the relevant match period. | Timing of the goal does not matter. |
| First Goalscorer | Whether the player scores the first recognised goal of the match. | The goal must come before every other scorer. |
| Last Goalscorer | Whether the player scores the final recognised goal. | Depends on the complete sequence of scoring. |
| Two or More Goals | Whether the player scores at least twice. | Requires multiple scoring events from the same player. |
| Hat-Trick | Whether the player scores at least three goals. | A much rarer event than scoring once. |
Betiball's evergreen scorer page should make the displayed market clear wherever player probabilities are shown. A probability for scoring once should not be presented as if it also described first-goal or multi-goal probability.
How Betiball Approaches Goalscorer Predictions
Player-scoring models need to combine the expected scoring environment of the match with the player's own involvement in that environment. A useful forecast therefore starts at team level and then narrows the analysis to individual opportunity, role and finishing profile.
Expected minutes
A player's opportunity to score depends heavily on how long they are likely to be on the pitch. A regular 90-minute starter and an impact substitute should not receive the same treatment even if their scoring rate per shot is similar.
Expected goals
Player xG estimates the quality of the chances associated with a player. It provides more context than raw goals because a high shot total made up of low-quality attempts is different from repeated chances close to goal. Research on player-adjusted xG similarly shows that individual position and player characteristics can meaningfully change expected scoring outcomes.
Shots and shots on target
Shooting frequency helps describe how often a player places themselves in scoring situations. Shots on target provide further context, although neither measure should replace chance quality.
Penalty and set-piece responsibility
Penalty takers naturally receive additional scoring opportunity when their team earns a penalty. Direct free kicks, attacking set pieces and aerial roles can also alter the scoring profile of certain players.
Position and tactical role
Two players listed as forwards may perform very different jobs. One may operate centrally inside the penalty area while another stays wide and creates chances for team-mates. Position labels alone therefore provide less information than actual attacking role.
Opponent defensive quality
Player form needs to be interpreted against the defence they are facing. The same striker can carry a different scoring probability against an organised low-block side than against a team allowing frequent high-quality chances.
The Player Statistics That Matter Most
| Statistic | What It Shows | Why It Matters |
|---|---|---|
| Goals | Actual scoring output | Useful historical result, but affected by finishing variance |
| Player xG | Quality of scoring opportunities | Measures underlying goal expectation |
| xG per 90 | Chance quality adjusted for playing time | Helps compare players with different minutes |
| Shots per 90 | Shooting involvement | Shows how regularly the player attempts to score |
| Shots on target | Attempts testing the goalkeeper | Adds context to raw shooting volume |
| Minutes played | Recent playing-time profile | Essential for estimating future opportunity |
| Starts | How regularly the player begins matches | Supports expected-minutes estimation |
| Penalty responsibility | Whether the player is a likely penalty taker | Adds a specific route to goal |
| Touches in the penalty area | How often the player reaches dangerous positions | Useful attacking-role indicator where available |
| Opponent xGA | Quality of chances the opposition typically concedes | Adds defensive context to player opportunity |
Use Betiball Player Statistics to review available player data alongside the match-level prediction.
Why xG Is Useful for Goalscorer Analysis
Goals alone tell us what happened; expected goals help describe the chances behind those goals. A player with six goals from relatively few high-quality chances may have a different future scoring profile from a player with four goals who repeatedly reaches excellent shooting positions.
Academic work on expected-goals modelling treats scoring as a probability problem built from shot and event features, while player-adjusted approaches attempt to account for individual and positional differences. That makes xG especially relevant to scorer analysis, provided it is used alongside role, minutes and opposition rather than as a standalone answer.:contentReference[oaicite:2]{index=2}
Actual goals can run above or below xG
Short-term finishing can fluctuate. A player may score repeatedly from difficult chances or go several matches without converting good opportunities. Neither run automatically represents the player's long-term level.
xG does not know who will start
Historical xG becomes much less useful if the player is benched, returning from injury or expected to play limited minutes. Team selection remains one of the most important practical checks before interpreting a scorer forecast.
Why Starting Status and Expected Minutes Matter
Scoring opportunity is time-dependent. Even an elite finisher has less opportunity to score when expected to play 25 minutes instead of 90. Goalscorer probabilities should therefore respond to likely line-ups and expected playing time.
Regular starters
Players who regularly start and complete matches offer a more stable minutes baseline. Their historical per-90 statistics can usually be applied with less adjustment than those of irregular substitutes.
Impact substitutes
A substitute may have excellent per-90 scoring data while receiving far fewer actual minutes. Per-90 numbers should never be interpreted without considering the likely duration of the player's appearance.
Returning players
Players returning from injury or suspension may have historical numbers that remain strong while their immediate playing-time expectation is uncertain. That uncertainty should reduce confidence in the pre-match forecast.
Penalties, Set Pieces and Scoring Role
Penalty responsibility can materially change player scoring probability. A striker who takes penalties has an additional scoring route that a similarly positioned team-mate may not share.
This does not mean penalty takers should automatically rank first. Expected minutes, open-play chance creation and the likelihood of the team producing attacking opportunities still matter.
Set-piece threats
Central defenders and strong aerial players can carry meaningful scoring probability from corners and free kicks despite having limited open-play involvement. These players need a different statistical profile from forwards receiving regular chances from open play.
Team Goal Expectation Sets the Scoring Environment
Individual scorer probabilities are constrained by the expected number of goals available to the team. When a side is projected to create very little, every player's scoring probability is naturally limited. When team goal expectation is high, several players may carry meaningful probability at the same time.
Compare scorer analysis with Under/Over 2.5 Goals Predictions and Both Teams to Score Predictions for a broader view of the match's scoring environment.
How to Read a Goalscorer Probability
Goalscorer probability should be read as an estimate of an individual event, not as a certainty or a simple ranking of player quality. A player can be the most likely scorer in a fixture while still being more likely not to score than to score.
| Scoring Probability | Interpretation |
|---|---|
| 52% | A very strong individual scoring profile for the match |
| 38% | Strong scorer candidate with meaningful non-scoring probability |
| 27% | Relevant attacking threat, but scoring remains clearly uncertain |
| 16% | Lower individual scoring probability despite a possible route to goal |
These percentages are examples only. Production pages should display the actual Betiball probability for the player and fixture being analysed.
What Makes a Strong Goalscorer Candidate?
The strongest profiles tend to combine several favourable signals rather than one standout statistic.
High expected minutes
A player expected to start and remain on the pitch has more opportunity to accumulate shots and chances.
Central attacking role
Players operating close to goal generally receive more high-value shooting opportunities than players used in deeper positions.
Strong recent xG
Repeatedly generating high-quality opportunities can be more informative than a short run of goals from difficult chances.
Penalty responsibility
Being the likely penalty taker can increase scoring probability, especially for teams expected to spend significant time in the opposition penalty area.
Favourable opponent profile
Opponents allowing regular shots from dangerous positions can create a better environment for a central attacker than an elite defensive side.
Where Goalscorer Predictions Are Least Reliable
Player-level forecasts are particularly sensitive to late information. A single team-sheet decision can make historical player statistics far less relevant to the fixture.
Uncertain starting line-ups
Rotation, injuries and tactical changes can alter expected minutes shortly before kick-off.
Players with small samples
Young players, new signings and recent substitutes may have too little historical data for stable player-level estimates.
Changing roles
A player moved from centre-forward to a wide or deeper position may retain the same historical goals-per-90 figure while receiving very different chances in the new role.
Low-data competitions
Some leagues provide less complete player event data than major competitions. Where shot, xG or minutes information is limited, scorer probabilities naturally carry greater uncertainty.
How Goalscorer Prediction Accuracy Should Be Evaluated
Scorer performance should be measured separately from team markets such as 1X2 or BTTS. The event is player-specific, and its natural base rate is substantially different from a two- or three-outcome match market.
Preserve the original probability
Historical evaluation is most useful when Betiball retains the probability shown before kick-off together with the player's expected status at the time.
Record whether the player actually played
A player who never enters the pitch should not be evaluated in the same way as one who plays 90 minutes. Any public accuracy methodology should state clearly how non-starters and non-participants are handled.
Separate by probability band
Players estimated around 40% should score more frequently over time than players estimated around 15%. Calibration by probability band is therefore more informative than displaying only a list of correct and incorrect picks.
Use a meaningful sample
Goals are relatively rare individual events, so short samples can move sharply. Reporting the number of settled player predictions alongside the performance period adds essential context.
Compare Goalscorer Predictions with Other Football Markets
| Market | What It Predicts | Explore |
|---|---|---|
| 1X2 | Home win, Draw or Away win | 1X2 Predictions |
| Under / Over 2.5 | Expected total-goal range | O/U 2.5 Predictions |
| Both Teams to Score | Whether both sides score | BTTS Predictions |
| Half Time | Result at the interval | Half Time Predictions |
| Half Time / Full Time | Combined interval and final result | HT/FT Predictions |
| Double Chance | Two covered match-result outcomes | Double Chance Predictions |
| Asian Handicap | Relative team strength against an adjusted line | Asian Handicap Predictions |
| Corners | Expected corner profile | Corner Predictions |
| Cards | Expected disciplinary profile | Card Predictions |
Goalscorer Predictions by Matchday
Use this page as Betiball's evergreen player-scoring hub, or move to a date-specific page when you want the current fixture list. Betiball already exposes Scorers across Today, Tomorrow, Weekend and Yesterday navigation.
What Counts as a Goal in Football?
Goalscorer analysis ultimately depends on a recognised goal being awarded during the match. Under the official Laws of the Game, a goal is scored when the whole ball crosses the goal line between the posts and beneath the crossbar, provided the scoring team has committed no offence.:contentReference[oaicite:3]{index=3}
For the official wording, see IFAB Law 10 — Determining the Outcome of a Match .
Player attribution is a separate statistical matter from whether the goal itself is valid. Own goals, deflections and provider corrections can affect who receives scorer credit, so historical scorer data may occasionally be revised after an event is reviewed.
Goalscorer Predictions — Frequently Asked Questions
What is an anytime goalscorer prediction?
An anytime goalscorer prediction estimates whether a particular player will score during the relevant match period. The goal does not need to be the first or last goal unless the market specifically says so.
How does Betiball estimate goalscorer probability?
Useful inputs include expected minutes, player xG, recent shots, scoring form, tactical role, penalty responsibility, team goal expectation and opponent defensive strength. The exact information available varies by competition.
Is xG useful for predicting goalscorers?
Yes. xG provides information about the quality of a player's chances and can reveal more than goal totals alone. It should still be combined with likely minutes, role, team selection and opposition.
Why do expected minutes matter?
Players need time on the pitch to accumulate chances. A player expected to play 90 minutes usually has more scoring opportunity than a substitute expected to appear for the final 20 minutes, all else being equal.
Do penalty takers have higher scoring probability?
Penalty responsibility can increase a player's scoring opportunity because penalties offer a specific route to goal. It remains only one component of the overall forecast.
Can a defender be a strong goalscorer prediction?
Yes. Centre-backs and other aerially strong players can carry meaningful scoring probability from set pieces, particularly against opponents that concede chances from corners and free kicks.
Is the top goalscorer prediction guaranteed to score?
No. Even the highest-ranked player can fail to score. Individual goals are relatively rare events, so meaningful uncertainty remains around every player prediction.
What is the difference between goalscorer and Over 2.5 predictions?
Goalscorer predictions estimate whether a specific player scores. Over/Under 2.5 predicts the total number of goals in the match. See Under/Over 2.5 Predictions for the match-total market.
Where can I find today's goalscorer predictions?
Visit Today's Goalscorer Predictions for the current matchday player-scoring view.
Can Betiball guarantee a player will score?
No. Betiball presents model-generated football probabilities and supporting statistics. Player-scoring forecasts are estimates rather than guaranteed outcomes.
Transparent Player-Level Football Analysis
Betiball positions its prediction product as a data and analytics platform built around model-generated probabilities rather than guaranteed outcomes. Goalscorer analysis should follow the same principle: show the player, probability and supporting context clearly, and acknowledge when line-up or player data is uncertain.:contentReference[oaicite:4]{index=4}
Learn more through About Betiball, Player Statistics, FAQ, Terms of Use, Privacy Policy, Avoid Scam and Contact Betiball.
