Albirex Niigata and Kataller Toyama bring a combined 2.71 xG and 10.18 expected corners, a strong platform for an open contest. Toyama’s attack is 47% above average, while Albirex’s is 11% stronger, making goals at both ends likely; their combined chance creation should also push the match beyond 2.5 goals and 8.5 corners.
1Match GoalsOver 2.5
2Both Teams to ScoreYes
3Corners Over/UnderOver 8.5
Probable XI4-4-2
Yoshimitsu
Hayakawa
Geria
Funaki
Mori
Ishiyama
Shirai
Okumura
Yoshida
Boudah
Konagaya
Impact Player Hot Head
Formazioni non ancora annunciate — analisi completa della rosa sotto
Albirex Niigata26 giocatori
50.5/ 99Medio
Migliori
1Ryosuke MaedaMID70
2Tetsuya KatoDEF67
3Eiji ShiraiMID66
Kataller Toyama30 giocatori
52.5/ 99Medio
Migliori
1Yuki NakashimaFWD91
2Ayumu KamedaMID90
3Kiyoshiro TsuboiFWD84
Albirex Niigata39
PlayerValutazioneImpattoAggressivitàDisciplina
Ryosuke Maeda70736854
Tetsuya Kato67466979
Eiji Shirai66474791
Yamato Wakatsuki60474891
Takuya Shimamura59514770
Yusuke Onishi58359369
Kanji Kuwayama57687525
Fumiya Hayakawa52533861
Michael James Fitzgerald51424072
Matheus Moraes51534550
Yudai Fujiwara50524550
Keisuke Kasai50496443
Tomoya Osawa49483361
Kataller Toyama41
PlayerValutazioneImpattoAggressivitàDisciplina
Yuki Nakashima91997856
Ayumu Kameda90996967
Kiyoshiro Tsuboi84954661
Shimpei Nishiya76617083
Daichi Matsuoka69996527
Keita Ueda68864550
Shunsuke Tanimoto62762760
Yuto Kunitake62771867
H. Matsumoto57457156
Sho Fuseya56545257
Tsubasa Yoshihira 56603950
Yuki Saneto55624550
Shun Mizoguchi53583161
Classifiche giocatori
Cecchini
Maggior minaccia di gol
1
Yuki NakashimaKataller Toyama
99
2
Kiyoshiro TsuboiKataller Toyama
95
3
Kanji KuwayamaAlbirex Niigata
68
4
Manato FurukawaKataller Toyama
62
5
Tsubasa Yoshihira Kataller Toyama
60
Muro
Migliori difensori
1
Jason GeriaAlbirex Niigata
78
2
Yuki SanetoKataller Toyama
62
3
Shimpei NishiyaKataller Toyama
61
4
K. FunakiAlbirex Niigata
54
5
Fumiya HayakawaAlbirex Niigata
53
Controllo
Migliori registi
1
Ayumu KamedaKataller Toyama
99
2
Daichi MatsuokaKataller Toyama
99
3
Keita UedaKataller Toyama
86
4
Yuto KunitakeKataller Toyama
77
5
Shunsuke TanimotoKataller Toyama
76
Cattivi ragazzi
Più propensi ai cartellini
1
Iury CastilhoAlbirex Niigata
1
Aggressività 55
2
Min-woo KangKataller Toyama
1
Aggressività 71
3
Nobuyuki ShiinaKataller Toyama
1
Aggressività 53
4
Manato FurukawaKataller Toyama
1
Aggressività 23
5
Riki MatsudaKataller Toyama
1
Aggressività 62
Classifica potenza
Più forti in assoluto
1
Yuki NakashimaKataller Toyama
91
2
Ayumu KamedaKataller Toyama
90
3
Kiyoshiro TsuboiKataller Toyama
84
4
Shimpei NishiyaKataller Toyama
76
5
Ryosuke MaedaAlbirex Niigata
70
Domande frequenti
Our Poisson model calculates the probability of every possible scoreline for Albirex Niigata vs Kataller Toyama based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.
The Bet Builder combines multiple selections from the Albirex Niigata vs Kataller Toyama match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.
xG (expected goals) measures the quality of scoring chances. If Albirex Niigata has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).
Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Albirex Niigata vs Kataller Toyama without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.
A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Albirex Niigata a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.
AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.