· J2-League
Tegevajaro Miyazaki
ATT
5/10
DEF
5/10
vs
Oita Trinita
3/10
ATT
6/10
DEF
Scheduled· Sat 31 Oct · 05:00
Best Pick
Under 2.5
54%
Correct Score
Top
1-0
12.6%
6.50
Top
1-1
12%
6.00
2-0
9.9%
9.00
2-1
9.4%
9.00
0-0
8.1%
7.50
0-1
7.7%
9.50
1-2
5.7%
13.00
3-0
5.1%
17.00
152%
2.00TOP
X25%
3.10
223%
3.70
1X77%
X248%
1275%
Probable XI4-4-2
Tokuwaka
Watanabe
Manabe
Nagatomo
Kuroki
Murakoshi
Nakano
Okuma
Mochizuki
Take
Konishi
Impact Player Hot Head
Lineups not yet announced — full squad analysis below
Tegevajaro Miyazaki22 players
51.4/ 99Average
Top Rated
1Ken Tshizanga MatsumotoFWD84
2Koji OkumuraMID73
3Hikaru ManabeDEF68
Oita Trinita27 players
48.9/ 99Average
Top Rated
1Patrick VerhonMID81
2Ayuki MiyagawaDEF71
3S. InoueDEF69
Tegevajaro Miyazaki42
PlayerOverall RatingImpactAggressionDiscipline
Ken Tshizanga Matsumoto84816983
Koji Okumura73774673
Hikaru Manabe68596864
Kaiga Murakoshi66595473
Yuma Matsumoto64469962
Keigo Hashimoto56444391
Yudai Tanaka54514561
Shigeo Miyawaki53465856
Ren Inoue53467449
Yusei Toshida53414883
Kengo Kuroki5274956
Seitaro Tanaka52444567
Kenta Matsuyama50504550
Oita Trinita43
PlayerOverall RatingImpactAggressionDiscipline
Patrick Verhon81698691
Ayuki Miyagawa71517091
S. Inoue69518278
Issei Tone68457791
Shinya Utsumoto68662891
Y. Misao64486271
Kotaro Arima61485191
Hiroshi Kiyotake57992510
Koya Hayashida57256691
Taiga Kimoto55485361
Mun Kyung Gun5354950
Kim Hyun-Woo53465367
Manato Kimoto52259967
Player Rankings
Snipers
Top goal threats
1
Ken Tshizanga MatsumotoTegevajaro Miyazaki
81
2
Kotaro ArimaOita Trinita
48
3
Ryo SatoTegevajaro Miyazaki
46
4
Kim Hyun-WooOita Trinita
46
5
Matheus LeiriaOita Trinita
45
Wall
Top defenders
1
Kengo KurokiTegevajaro Miyazaki
74
2
Matheus PereiraOita Trinita
74
3
Hikaru ManabeTegevajaro Miyazaki
59
4
Takashi AbeTegevajaro Miyazaki
54
5
Ayuki MiyagawaOita Trinita
51
Control
Top playmakers
1
Hiroshi KiyotakeOita Trinita
99
2
Koji OkumuraTegevajaro Miyazaki
77
3
Patrick VerhonOita Trinita
69
4
Shinya UtsumotoOita Trinita
66
5
Kaiga MurakoshiTegevajaro Miyazaki
59
Bad Boys
Most likely to get booked
1
Yoko IesakaTegevajaro Miyazaki
1
2
Matheus PereiraOita Trinita
1
3
Yasufumi NishimuraOita Trinita
1
4
David AizawaOita Trinita
1
5
Mahiro AnoTegevajaro Miyazaki
3
Power Rankings
Strongest overall
1
Ken Tshizanga MatsumotoTegevajaro Miyazaki
84
2
Patrick VerhonOita Trinita
81
3
Koji OkumuraTegevajaro Miyazaki
73
4
Ayuki MiyagawaOita Trinita
71
5
S. InoueOita Trinita
69

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Tegevajaro Miyazaki vs Oita Trinita 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 Tegevajaro Miyazaki vs Oita Trinita 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 Tegevajaro Miyazaki 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 Tegevajaro Miyazaki vs Oita Trinita 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 Tegevajaro Miyazaki 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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