VEL 1-0 SAN · 54% LLA 0-1 JUN · 50% CFP 1-1 TIG · 45% EST 0-1 INS · 48% MON 1-1 CLU · 43% GUA 2-0 ATL · 58% COL 1-2 CHI · 49% CLU 2-1 TOL · 54% YOK 1-2 KAW · 47% JEF 1-1 AVI · 42% V-V 1-0 TOK · 44% MAC 2-0 GAM · 62% AME 3-0 EYü · 71% PEC 1-1 ADO · 40% VEL 1-0 SAN · 54% LLA 0-1 JUN · 50% CFP 1-1 TIG · 45% EST 0-1 INS · 48% MON 1-1 CLU · 43% GUA 2-0 ATL · 58% COL 1-2 CHI · 49% CLU 2-1 TOL · 54% YOK 1-2 KAW · 47% JEF 1-1 AVI · 42% V-V 1-0 TOK · 44% MAC 2-0 GAM · 62% AME 3-0 EYü · 71% PEC 1-1 ADO · 40%
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J1 League · Regular Season - 13 Best Denki Stadium, Fukuoka Not Started

Avispa Fukuoka
vs
V-varen Nagasaki.

Quick answer

Who will win Avispa Fukuoka vs V-varen Nagasaki?

4 of 10 AI models pick V-varen Nagasaki to win (40%). The mean predicted score is 1–0 at 43% average confidence (low).

Consensus of independent AI models on TuringStats, updated before kickoff. Not betting advice.

Head-to-head history → Avispa Fukuoka form V-varen Nagasaki form

10 AI models read this match. The plurality on 1X2 leans toward V-varen Nagasaki (4 of 10 models). The mean predicted score (rounded from all models) is 1–0 at 43% mean confidence (Low band) — see the strip and table below.

Who do you think will win?

Vote before kick-off and compare with AI

J1 League table Where Avispa Fukuoka and V-varen Nagasaki stand right now
— Consensus

Where the panel lands.

Vote share across models. Read this strip first, then the line-by-line table.

Avispa Fukuoka win 30%

3 / 10 models

Draw 30%

3 / 10 models

V-varen Nagasaki win 40%

4 / 10 models

— Model breakdown

10 picks. Receipts attached.

Line-by-line comparison of each system’s call.

#
Model
Pick
Score
Conf.
Reasoning
01
GPT-6 Luna
Avispa Fukuoka win
1–0
43%
Avispa’s home advantage and two recent wins over Nagasaki support a narrow victory, but their 2W-1D-9L season and low scoring output make 1-0 more plausible than a comfortable result.
02
Claude Sonnet 5.5
V-varen Nagasaki win
1–2
48%
Fukuoka's poor form (2W 9L, LWLLL) and leaky defense contrast with Nagasaki's steadier record and recent H2H edge at this venue.
03
Gemini 3.8 Flash
Draw
1–1
48%
Both sides have struggled for wins this season with low goal outputs. Fukuoka's poor home form balances out Nagasaki's frequent draws, pointing to a tight 1-1 stalemate.
04
Grok 4.7
Draw
1–1
38%
Both sides leak and score ~1 per game in a poor J1 run; recent H2H 1-0s and late Nagasaki goals point to a tight 1-1 rather than a home win.
05
DeepSeek V4 Pro
V-varen Nagasaki win
1–2
44%
Fukuoka sit bottom with five straight losses and poor scoring, while Nagasaki show better recent form and late-game threat. H2H is split, so modest confidence.
06
Qwen 3.8 Max
V-varen Nagasaki win
0–1
38%
Fukuoka's dire form (2W 9L, LWLLL) and leaky defence favour Nagasaki, who grind out tight results; H2H pattern of low-scoring narrow wins supports a single away goal.
07
Meta Muse Spark 1.3
Avispa Fukuoka win
1–0
46%
Both struggle for goals but Avispa won 1-0 at Best Denki in April and V-varen blanked 4 times; low-tempo home edge favours another narrow shutout.
08
Kimi K3
Avispa Fukuoka win
1–0
48%
Avispa won the last home H2H 1-0 and Nagasaki arrive winless in three away; both attacks are weak (13 and 15 goals in 12), so a tight low-scoring home edge at Best Denki.
09
GLM 5.3
V-varen Nagasaki win
0–1
38%
Fukuoka's league form is dire (2W 9L, LWLLL) while Nagasaki are steadier (DDLWL) and score late; recent H2H home wins are the only counter-signal, so a narrow low-scoring away win fits best.
10
MiMo V2.6 Pro
Draw
1–1
38%
Avispa's 2W9L season and LWLLL form offset home venue; Nagasaki's DDLWL is only slightly steadier. Both concede regularly (21 and 19 goals), pointing to a shared spoils draw.
— Scoreline frequency

How often each scoreline showed up.

3 of 10 models settled on 1–0. The rest of the list shows where dissent still lives before kickoff.

  • 1–0
    3 models
  • 1–1
    3 models
  • 1–2
    2 models
  • 0–1
    2 models
Team news

Predicted lineups: Avispa Fukuoka vs V-varen Nagasaki

Each side's last starting XI, with players ruled out of this match marked. The confirmed lineups replace this about an hour before kickoff.

Avispa Fukuoka

3-4-2-1 · XI v Tokyo Verdy, 24 Oct
S. Tsukahara
13 S. Morooka
13 Morooka
25 Y. Kitajima
25 Kitajima
6 M. Shigemi
6 Shigemi
29 Y. Maejima
29 Maejima
11 T. Miki
11 Miki
34 K. Shiihashi
34 Shiihashi
47 Y. Hashimoto
47 Hashimoto
38 D. Miya
38 Miya
37 M. Tashiro
37 Tashiro
16 T. Oka
16 Oka
24 Y. Obata
24 Obata

V-varen Nagasaki

3-4-2-1 · XI v Urawa, 25 Oct
T. Takagi
99 Jonathan
99 Jonathan
34 T. Matsumoto
34 Matsumoto
10 Matheus Jesus
10 Jesus
23 S. Yoneda
23 Yoneda
41 M. Hasegawa
41 Hasegawa
5 H. Yamaguchi
5 Yamaguchi
22 H. Onaga
22 Onaga
6 Y. Egawa
6 Egawa
29 I. Arai
29 Arai
31 K. Doi
31 Doi
1 M. Goto
1 Goto

Season data

J1 League numbers for both sides, when they score and the players who carry them.

Avispa Fukuoka

3-4-2-1
W-D-L
2-1-9
Goals
13-21
Clean sheets
1
Failed to score
4
Best win run
1
Penalties
0/0
When they score
15'30'45'60'75'90'
When they concede
15'30'45'60'75'90'

V-varen Nagasaki

3-4-2-1
W-D-L
3-3-6
Goals
15-19
Clean sheets
2
Failed to score
4
Best win run
1
Penalties
1/1
When they score
15'30'45'60'75'90'
When they concede
15'30'45'60'75'90'

Top scorers · J1 League

  1. 1 Léo Ceará Kashima 12
  2. 2 Marcinho Kawasaki Frontale 8
  3. 3 A. Suzuki Sanfrecce Hiroshima 8
  4. 4 M. Nagakura FC Tokyo 7
  5. 5 Y. Segawa Kashiwa Reysol 7
Venue Best Denki Stadium, Fukuoka
New at Avispa Fukuoka Oriol Romeu (Southampton), K. Mikuni (Nagoya Grampus), S. Morooka (Kashima)
New at V-varen Nagasaki A. Masuyama (Machida Zelvia), Jonathan (Stal Rzeszów), S. Diop (Fukuyama City)
— Aggregated insights

What’s moving the panel.

01
Consensus favors V-varen Nagasaki

40% of models lean away — the clearest cluster on this fixture before kickoff.

02
Low confidence

Mean 43% across the panel with real dispersion — compare unanimous calls vs split tickets in the model table.

03
xG tilt 0.80 vs 0.90

Derived from predicted scorelines (model means), not live match data — useful for pace vs vote-share sanity checks.

04
Match context

Expected-goals tilt and home-field rhythm (see xG on this page) usually explain whether the game stays open or compresses late.

Ask the Council about this match

Pro

10 AI models vote yes or no with this match's last-5 form, head-to-head, live status and the model picks above attached — not from memory. Then keep asking the same council follow-ups.

Ask with match data Ask without data (free)
— Match context

Form, history, team news.

Avispa Fukuoka
#19 · 7 PTS · GD -8
Last 5
L L L L W
5 GF · 11 GA
Recent fixtures
  • Tokyo Verdy 3-1 L
  • Gamba Osaka 1-2 L
  • Machida Zelvia 3-0 L
  • Sagan Tosu 2-1 L
  • Fagiano Okayama 2-1 W
Team news

No major injury updates in the current API snapshot.

V-varen Nagasaki
#14 · 12 PTS · GD -4
Last 5
L W L D L
5 GF · 6 GA
Recent fixtures
  • Urawa 1-0 L
  • Sanfrecce Hiroshima 4-0 W
  • Kashima 2-0 L
  • JEF United Chiba 1-1 D
  • Gainare Tottori 2-0 L
Team news

No major injury updates in the current API snapshot.

Confidence trend

Cumulative average confidence in table order.

First model Last model

Betting tips (AI-signal view)

Educational only — not financial advice. We summarize how the AI picks cluster so you can cross-check with your own staking plan.

  • Lean with the plurality: when 30% of models side with Avispa Fukuoka, treat that as the default script unless late team news breaks the assumptions.
  • Watch the draw lane at 30% — tight J1 League games often compress toward stalemates when both midfields win the second-ball.
  • If you chase “best bets today” narratives, require alignment between the headline pick and the score-frequency table; conflicting signals usually mean thinner edge.

Odds & analysis (implied probabilities)

Implied fair percentages from the model vote share (normalized to 100%) approximate how a balanced market might price the 1X2 if it mirrored this panel — useful for odds analysis homework even though we do not quote sportsbook ticks here.

Avispa Fukuoka
~30%
implied lean
Draw
~30%
implied lean
V-varen Nagasaki
~40%
implied lean

Over / under prediction (totals)

Model-derived xG sums to 1.70 goals in expectation. A notional totals line near 2 is consistent with that pace (rounded for readability). If your sportsbook posts a similar number, compare juice and live team news before deciding either side of the total.

Handicap prediction (spread-style read)

When Avispa Fukuoka is priced as the stronger side in the model vote, a −1 handicap narrative only clears if the most common scorelines include multi-goal wins. Cross-check the score-frequency list: if tight one-goal wins dominate, Asian handicaps near pick’em or −0.5 / −0.75 splits often fit the story better than a full −1.5 sell.

BTTS prediction (both teams to score)

With combined offensive weight near 1.70 xG, a heuristic “both teams score” prior lands around 55% yes before defensive adjustments. If several top models forecast clean-sheet pathways, downgrade BTTS enthusiasm even when the raw xG sum looks juicy.

Best bet framing (consensus-led)

Our headline best bet label follows the consensus recommendation: V-varen Nagasaki win. Pair that with the confidence band (Low) — high dispersion across models usually argues for smaller stake or pass, even when the headline pick looks tempting for a today prediction card on social.

Match overview

Looking for a today prediction on Avispa Fukuoka vs V-varen Nagasaki in J1 League? TuringStats aggregates multiple AI scorelines into one readable page so you can see who the models favor, the mean predicted score shown in the hero (1 - 0; the frequency chart below lists the most common exact scorelines), and implied splits before kickoff.

This prediction hub is written for readers comparing betting tips-style language with transparent model votes — not a single black-box call. The headline read is V-varen Nagasaki win, with vote shares roughly 30% / 30% / 40% home, draw, and away (rounded).

If you are asking who will win Avispa Fukuoka vs V-varen Nagasaki, start with the consensus strip and model table, then cross-check form and injuries in Match context further down — that order keeps the strongest signals first.

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