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% COM 2-0 VEN · 63% CER 1-2 ANT · 50% GET 0-1 SEV · 50% 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% COM 2-0 VEN · 63% CER 1-2 ANT · 50% GET 0-1 SEV · 50%
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J1 League · Regular Season - 13 Fukuda Denshi Arena, Chiba Not Started

JEF United Chiba
vs
Mito Hollyhock.

Quick answer

Who will win JEF United Chiba vs Mito Hollyhock?

3 of 10 AI models pick JEF United Chiba to win (30%). A draw is in play: 7 of 10 models picked it. The mean predicted score is 1–1 at 41% average confidence (low).

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

Head-to-head history → JEF United Chiba form Mito Hollyhock form

10 AI models read this match. The plurality on 1X2 leans toward the draw (7 of 10 models). The mean predicted score (rounded from all models) is 1–1 at 41% 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 JEF United Chiba and Mito Hollyhock stand right now
— Consensus

Where the panel lands.

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

JEF United Chiba win 30%

3 / 10 models

Draw 70%

7 / 10 models

Mito Hollyhock win 0%

0 / 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
Draw
1–1
34%
Both teams have weak records and limited scoring output; JEF's home advantage is offset by their 1W-4D-7L campaign. Recent head-to-head meetings were both 1-1, supporting another tight draw despite Mito's five-match losing run.
02
Claude Sonnet 5.5
JEF United Chiba win
1–0
44%
Both sides struggling for goals, but Mito on a five-game losing streak and home side has recent H2H edge and venue advantage.
03
Gemini 3.8 Flash
JEF United Chiba win
1–0
48%
Mito Hollyhock are on a five-match losing run and have zero clean sheets, giving struggling Chiba home advantage to edge a tight, low-scoring encounter.
04
Grok 4.7
Draw
1–1
34%
Both sides leaky and low-scoring in J1 (12 goals each), JEF winless lately and Mito on five straight losses; recent H2H 1-1s fit a shared stalemate at Fukuda Denshi.
05
DeepSeek V4 Pro
Draw
1–1
42%
Both sides are in poor form with low scoring and recent H2H draws; JEF's home edge is offset by Mito's need for points.
06
Qwen 3.8 Max
Draw
1–1
32%
Both teams in poor form; last two H2H meetings ended 1-1. Mito's five-game losing streak and zero clean sheets offset JEF's home edge.
07
Meta Muse Spark 1.3
JEF United Chiba win
1–0
52%
Mito on five straight losses with zero clean sheets; JEF unbeaten in five H2Hs including 1-1 draws. Home edge at Fukuda Denshi supports narrow low-scoring win.
08
Kimi K3
Draw
1–1
48%
Both sides struggle to score (12 goals each) and defend poorly; recent H2H shows consecutive 1-1 draws, and Mito's five straight losses suggest limited away threat despite Chiba's home advantage.
09
GLM 5.3
Draw
1–1
30%
Both sides struggling (1W and 2W), recent H2H meetings ended 1-1 twice; Mito score but never keep clean sheets, JEF draw-heavy, so a tight 1-1 fits.
10
MiMo V2.6 Pro
Draw
1–1
42%
Both sides struggle for wins (JEF 1W, Mito 2W) and drew 1-1 twice in 2026; Mito's five straight losses and JEF's home edge still point to a low, shared-score draw.
— Scoreline frequency

How often each scoreline showed up.

7 of 10 models settled on 1–1. That convergence is a strong scoreline signal—many fixtures fan out wider across the panel.

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

Predicted lineups: JEF United Chiba vs Mito Hollyhock

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

JEF United Chiba

4-4-2 · XI v Cerezo Osaka, 25 Oct
Y. Kobayashi
99 Erison
99 Erison
9 H. Goya
9 Goya
37 M. Himeno
37 Himeno
41 T. Yasui
41 Yasui
6 Dudu
6 Dudu
8 T. Tsukui
8 Tsukui
67 M. Hidaka
67 Hidaka
2 B. Nduka
2 Nduka
24 K. Toriumi
24 Toriumi
15 T. Mae
15 Mae
19 J. Suarez
19 Suarez

Mito Hollyhock

4-4-2 · XI v Sanfrecce Hiroshima, 31 Oct
N. Mori
33 Patryck Ferreira
33 Ferreira
22 K. Taniguchi
22 Taniguchi
44 M. Nishihara
44 Nishihara
3 K. Osaki
3 Osaki
19 T. Semba
19 Semba
30 J. Okumura
30 Okumura
16 Y. Tamura
16 Tamura
17 K. Itakura
17 Itakura
15 Y. Kimoto
15 Kimoto
25 T. Mase
25 Mase
27 J. Słowik
27 Słowik

Season data

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

JEF United Chiba

4-4-2
W-D-L
1-4-7
Goals
12-24
Clean sheets
2
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'

Mito Hollyhock

4-4-2
W-D-L
2-3-7
Goals
12-22
Clean sheets
0
Failed to score
5
Best win run
1
Penalties
2/2
When they score
15'30'45'60'75'90'
When they concede
15'30'45'60'75'90'

Key players · JEF United Chiba

  • No player in the competition's top scorers or assisters.

Key players · Mito Hollyhock

  • A. Watanabe 5 G · 0 A · 8 apps

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 Fukuda Denshi Arena, Chiba
New at JEF United Chiba B. Nduka (Vissel Kobe), H. Kida (Cerezo Osaka), T. Iida (Mito Hollyhock)
New at Mito Hollyhock J. Okumura (Albirex Niigata), Leonardo Hugo (Sao Paulo), A. Kawazura (Nagoya Grampus)
— Aggregated insights

What’s moving the panel.

01
Models lean the draw

70% of models take a stalemate — cross-check score frequency and 1X2 strip before sizing.

02
Low confidence

Mean 41% across the panel with real dispersion — compare unanimous calls vs split tickets in the model table.

03
xG tilt 1.00 vs 0.70

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.

JEF United Chiba
#20 · 7 PTS · GD -12
Last 5
D L D D L
7 GF · 10 GA
Recent fixtures
  • Cerezo Osaka 2-2 D
  • Urawa 3-2 L
  • Yokohama F. Marinos 0-0 D
  • V-varen Nagasaki 1-1 D
  • Jubilo Iwata 4-2 L
Team news

No major injury updates in the current API snapshot.

Mito Hollyhock
#17 · 9 PTS · GD -10
Last 5
L L L L L
1 GF · 15 GA
Recent fixtures
  • Sanfrecce Hiroshima 0-3 L
  • Cerezo Osaka 1-3 L
  • FC Tokyo 6-0 L
  • Fagiano Okayama 2-0 L
  • Urawa 0-1 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 JEF United Chiba, treat that as the default script unless late team news breaks the assumptions.
  • Watch the draw lane at 70% — 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.

JEF United Chiba
~30%
implied lean
Draw
~70%
implied lean
Mito Hollyhock
~0%
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 JEF United Chiba 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: JEF United Chiba 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 JEF United Chiba vs Mito Hollyhock 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 - 1; 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 JEF United Chiba win, with vote shares roughly 30% / 70% / 0% home, draw, and away (rounded).

If you are asking who will win JEF United Chiba vs Mito Hollyhock, 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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