· J2-League
Shonan Bellmare
ATT
4/10
DEF
5/10
vs
Yokohama
6/10
ATT
6/10
DEF
Scheduled· Sat 14 Nov · 05:00
Best Pick
Under 2.5
57%
Correct Score
Top
1-1
13%
7.00
Top
0-1
11.5%
9.00
1-0
10.3%
12.00
0-0
9.1%
12.00
1-2
8.2%
9.00
2-1
7.4%
12.00
0-2
7.3%
11.00
2-0
5.8%
19.00
133%
3.20
X28%
3.40
239%
2.05TOP
1X61%
X267%
1272%
Probable XI4-4-2
Oshita
27
Fukushima
23
Matsumoto
15
Dohana
8
Ohno
16
Silva
30
Yamaguchi
36
Matsumura
25
Okuno
9
González
34
Yamada
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Shonan Bellmare25 players
53.6/ 99Average
Top Rated
1Fabián GonzálezFWD82
2Kosuke OnoseMID80
3Souki TamuraMID77
Yokohama20 players
50.3/ 99Average
Top Rated
1AdaíltonFWD81
2Hayato SugitaDEF64
3Makito ItoDEF64
Shonan Bellmare42
PlayerOverall RatingImpactAggressionDiscipline
9
Fabián González82769091
7
Kosuke Onose80677391
17
Souki Tamura77627391
30
Gota Yamaguchi76806561
25
Hiroaki Okuno70656467
15
Kazuki Dohana60545366
86
Naoki Kase60771167
31
Kota Sanada58609947
19
Taiyo Nishino58435291
3
Yutaro Hakamata57683555
6
Shohei Takeda56545356
2
Sere Matsumura55284791
37
Yuto Suzuki54196791
Yokohama43
PlayerOverall RatingImpactAggressionDiscipline
90
Adaílton81855967
19
Hayato Sugita64466275
16
Makito Ito64417078
49
Naoya Komazawa63549972
70
Rita Watanabe62469661
18
Takuya Shimamura59584069
9
Lukian59506868
30
Kanta Chiba58554072
10
João Paulo56613950
5
K. Hosoi55454475
21
Akinori Ichikawa5454950
45
Kakeru Saito50456456
Kaito Mori49531561
Player Rankings
Snipers
Top goal threats
1
AdaíltonYokohama
85
2
Fabián GonzálezShonan Bellmare
76
3
João PauloYokohama
61
4
Kanta ChibaYokohama
55
5
Naoya KomazawaYokohama
54
Wall
Top defenders
1
Yutaro HakamataShonan Bellmare
68
2
Kazuki DohanaShonan Bellmare
54
3
Hayato FukushimaShonan Bellmare
46
4
Hayato SugitaYokohama
46
5
K. HosoiYokohama
45
Control
Top playmakers
1
Gota YamaguchiShonan Bellmare
80
2
Leo TakaeYokohama
78
3
Naoki KaseShonan Bellmare
77
4
Kosuke OnoseShonan Bellmare
67
5
Hiroaki OkunoShonan Bellmare
65
Bad Boys
Most likely to get booked
1
Yuta FukasawaShonan Bellmare
1
2
Kota YamadaYokohama
1
3
Leo TakaeYokohama
1
4
Ryosuke IwasakiYokohama
1
5
Koshiro UdaYokohama
9
Power Rankings
Strongest overall
1
Fabián GonzálezShonan Bellmare
82
2
AdaíltonYokohama
81
3
Kosuke OnoseShonan Bellmare
80
4
Souki TamuraShonan Bellmare
77
5
Gota YamaguchiShonan Bellmare
76

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Shonan Bellmare vs Yokohama 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 Shonan Bellmare vs Yokohama 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 Shonan Bellmare 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 Shonan Bellmare vs Yokohama 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 Shonan Bellmare 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.

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