· J2-League
Tokushima Vortis
ATT
5/10
DEF
7/10
vs
Yokohama
6/10
ATT
6/10
DEF
Naplánováno· Sat 31 Oct · 05:00
Nejlepší tip
Pod 2.5
55%
Přesný výsledek
Výborná
1-1
12.8%
6.50
Výborná
1-0
11.4%
11.00
0-1
9.5%
8.50
2-1
8.6%
12.00
0-0
8.4%
11.00
2-0
7.7%
17.00
1-2
7.2%
9.00
0-2
5.3%
11.00
142%
3.25TOP
X27%
3.30
231%
2.15
1X69%
X258%
1273%
Probable XI4-4-2
Mitsui
Aoki
Tanaka
Kaique
Matsuda
Iwao
Fukuta
Kanuma
Kodama
Watari
Suzuki
Impact Player Hot Head
Sestavy ještě nebyly oznámeny — níže kompletní analýza kádru
Tokushima Vortis23 hráči
48.0/ 99Průměrný
Nejlepší
1Lawrence IzuchukwuFWD65
2Ken Iwao MID63
3Hayato TanakaDEF61
Yokohama20 hráči
49.6/ 99Průměrný
Nejlepší
1AdaíltonFWD81
2Hayato SugitaDEF69
3Makito ItoDEF65
Tokushima Vortis37
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Lawrence Izuchukwu65733950
Ken Iwao 63577854
Hayato Tanaka61543783
Kaique59595656
Hayato Aoki58454778
Daiki Watari57483889
Kai Chide Kitamura5556950
Ko Yanagisawa55534761
Malcolm Tsuyoshi Moyo54555947
Yuya Takagi52475356
Naoki Kanuma52318357
Jumma Miyazaki48484550
Soya Takada45454550
Yokohama43
PlayerCelkové HodnoceníImpaktAgreseDisciplína
Adaílton81855967
Hayato Sugita69527571
Makito Ito65436978
Lukian63547368
Naoya Komazawa63568661
Rita Watanabe62469561
Takuya Shimamura59514770
K. Hosoi56494668
João Paulo56613950
Akinori Ichikawa5455950
Kanta Chiba54504467
Kakeru Saito50456456
Leo Takae4479381
Žebříčky hráčů
Střelci
Největší gólová hrozba
1
AdaíltonYokohama
85
2
Lawrence IzuchukwuTokushima Vortis
73
3
Yukihito KajiyaTokushima Vortis
62
4
João PauloYokohama
61
5
Naoya KomazawaYokohama
56
Zeď
Nejlepší obránci
1
Keita MatsudaTokushima Vortis
72
2
KaiqueTokushima Vortis
59
3
Kohei YamakoshiTokushima Vortis
58
4
Malcolm Tsuyoshi MoyoTokushima Vortis
55
5
Hayato TanakaTokushima Vortis
54
Kontrola
Nejlepší tvůrci hry
1
Leo TakaeYokohama
79
2
Shunto KodamaTokushima Vortis
59
3
Ken Iwao Tokushima Vortis
57
4
Takuya ShimamuraYokohama
51
5
Akiyuki YokoyamaYokohama
47
Zlobivci
Nejpravděpodobnější žlutá karta
1
Keita MatsudaTokushima Vortis
1
2
Shunto KodamaTokushima Vortis
1
3
Kota YamadaYokohama
1
4
Leo TakaeYokohama
1
5
Ryosuke IwasakiYokohama
1
Žebříček síly
Nejsilnější celkově
1
AdaíltonYokohama
81
2
Hayato SugitaYokohama
69
3
Lawrence IzuchukwuTokushima Vortis
65
4
Makito ItoYokohama
65
5
Ken Iwao Tokushima Vortis
63

Frequently Asked Questions

Our Poisson model calculates the probability of every possible scoreline for Tokushima Vortis 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 Tokushima Vortis 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 Tokushima Vortis 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 Tokushima Vortis 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 Tokushima Vortis 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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