· J2-League
Oita Trinita
ATT
6/10
DEF
5/10
vs
Vanraure Hachinohe
5/10
ATT
4/10
DEF
Scheduled· Tue 3 Nov · 05:00
Best Pick
Home Win
58%
Correct Score
Top
1-0
12.6%
6.00
Top
1-1
11.1%
6.50
2-0
11%
9.00
2-1
9.7%
9.50
0-0
7.2%
7.50
0-1
6.4%
8.00
3-0
6.4%
19.00
3-1
5.7%
21.00
158%
2.15TOP
X23%
3.25
219%
3.30
1X81%
X242%
1277%
Probable XI4-4-2
Kono
Miyagawa
Oda
Tone
Nodake
Kozakai
Matsuoka
Kiyotake
Nakagawa
Furukawa
Aizawa
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Oita Trinita27 players
47.8/ 99Average
Top Rated
1Patrick VerhonMID82
2Ayuki MiyagawaDEF71
3S. InoueDEF69
Vanraure Hachinohe20 players
51.8/ 99Average
Top Rated
1Teruhiro SasakiDEF78
2William OwieFWD76
3Kazuma NagataMID72
Oita Trinita43
PlayerOverall RatingImpactAggressionDiscipline
Patrick Verhon82718891
Ayuki Miyagawa71497491
S. Inoue69508278
Y. Misao67546271
Issei Tone62527854
Kotaro Arima59464991
Shinya Utsumoto58623267
Hiroshi Kiyotake5599421
Taiga Kimoto55485361
Mun Kyung Gun5456950
Koya Hayashida54236884
Kim Hyun-Woo53465367
Manato Kimoto51249967
Vanraure Hachinohe33
PlayerOverall RatingImpactAggressionDiscipline
Teruhiro Sasaki78677779
William Owie76777460
Kazuma Nagata72539991
Wataru Hiramatsu67714565
Yudai Sawada67593791
Yuma Igari65625367
Shinnosuke Suzuki62496961
Aoi Sato62573383
Shogo Onishi49491250
Haruki Shirai49514550
Hiroto Yukie49573450
Shoma Otoizumi47223691
Ryusei Takao47625912
Player Rankings
Snipers
Top goal threats
1
William OwieVanraure Hachinohe
77
2
Ryusei TakaoVanraure Hachinohe
62
3
Kim Hyun-WooOita Trinita
46
4
Kotaro ArimaOita Trinita
46
5
Matheus LeiriaOita Trinita
45
Wall
Top defenders
1
Matheus PereiraOita Trinita
71
2
Wataru HiramatsuVanraure Hachinohe
71
3
Teruhiro SasakiVanraure Hachinohe
67
4
Yudai SawadaVanraure Hachinohe
59
5
Hiroto YukieVanraure Hachinohe
57
Control
Top playmakers
1
Hiroshi KiyotakeOita Trinita
99
2
Patrick VerhonOita Trinita
71
3
Shinya UtsumotoOita Trinita
62
4
Yuma IgariVanraure Hachinohe
62
5
Aoi SatoVanraure Hachinohe
57
Bad Boys
Most likely to get booked
1
Matheus PereiraOita Trinita
1
2
Hiroshi KiyotakeOita Trinita
1
3
Yasufumi NishimuraOita Trinita
1
4
David AizawaOita Trinita
1
5
Daigo FurukawaOita Trinita
6
Power Rankings
Strongest overall
1
Patrick VerhonOita Trinita
82
2
Teruhiro SasakiVanraure Hachinohe
78
3
William OwieVanraure Hachinohe
76
4
Kazuma NagataVanraure Hachinohe
72
5
Ayuki MiyagawaOita Trinita
71

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Oita Trinita vs Vanraure Hachinohe 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 Oita Trinita vs Vanraure Hachinohe 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 Oita Trinita 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 Oita Trinita vs Vanraure Hachinohe 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 Oita Trinita 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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