ABH 0-2 AL- · 64% ALD 0-1 NEO · 49% KON 1-2 GAL · 54% LEH 0-2 PAR · 64% NIC 1-2 REN · 50% BOD 1-2 VIK · 46% LYN 1-1 RAN · 41% ALS 1-2 AL- · 56% SVE 1-2 FSV · 46% ZUL 0-2 CLU · 62% VIL 2-1 ESP · 56% SHE 2-1 WRE · 48% LIL 2-1 LEN · 50% CAS 0-2 SPO · 66% ABH 0-2 AL- · 64% ALD 0-1 NEO · 49% KON 1-2 GAL · 54% LEH 0-2 PAR · 64% NIC 1-2 REN · 50% BOD 1-2 VIK · 46% LYN 1-1 RAN · 41% ALS 1-2 AL- · 56% SVE 1-2 FSV · 46% ZUL 0-2 CLU · 62% VIL 2-1 ESP · 56% SHE 2-1 WRE · 48% LIL 2-1 LEN · 50% CAS 0-2 SPO · 66%
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UEFA Europa League · League Stage - 4 Nov 5, 2026 · 17:45 · UTC Not Started

Lillestrom
vs
Plzen.

Quick answer

Who will win Lillestrom vs Plzen?

3 of 10 AI models pick Lillestrom to win (30%). A draw is in play: 6 of 10 models picked it. The mean predicted score is 1–1 at 44% average confidence (low).

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

Head-to-head history → Lillestrom form Plzen form

10 AI models read this match. The plurality on 1X2 leans toward the draw (6 of 10 models). The mean predicted score (rounded from all models) is 1–1 at 44% mean confidence (Low band) — see the strip and table below.

Who do you think will win?

Vote before kick-off and compare with AI

UEFA Europa League table Where Lillestrom and Plzen stand right now
— Consensus

Where the panel lands.

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

Lillestrom win 30%

3 / 10 models

Draw 60%

6 / 10 models

Plzen win 10%

1 / 10 models

— Model breakdown

10 picks. Receipts attached.

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

#
Model
Pick
Score
Conf.
Reasoning
01
GPT-5.6 Luna
Lillestrom win
1–0
42%
Both teams have struggled in the league stage, but Lillestrom's home advantage and Plzen's four losses in five suggest a narrow home win. Lillestrom's low scoring rate favors 1-0 over a higher-total result.
02
Claude Sonnet 5
Lillestrom win
2–1
48%
Both sides struggling defensively and in poor form, but Plzen's worse away form (1W 4L, conceding freely) gives Lillestrom a slight home edge.
03
Gemini 3.8 Flash
Draw
1–1
48%
Both teams have struggled for consistency in Europe with leaky defenses. Plzen carries slightly more European pedigree, but Lillestrom's home advantage balances this into a tightly contested stalemate.
04
Grok 4.6
Draw
1–1
46%
Both leaky and low-scoring in UEL (Lillestrom 4-10, Plzen 7-11, many losses). Home edge offsets Plzen’s slightly better attack; thin finishing points to a 1-1.
05
DeepSeek V4 Pro
Draw
1–1
38%
Both sides struggle defensively and have modest scoring records; Lillestrom's late goals and Plzen's poor away form point to a tight, low-scoring stalemate.
06
Qwen 3.8 Max
Draw
1–1
22%
Both sides poor in Europe; Lillestrom score late but rarely, Plzen concede freely yet still find goals. Two struggling attacks suggest a low-scoring stalemate.
07
Mistral Medium 3.5
Draw
1–1
58%
Both teams struggle defensively; Lillestrom’s late goals and Plzen’s inconsistency suggest a tight, low-scoring draw.
08
Kimi K3
Plzen win
1–2
48%
Lillestrom score late but concede heavily at home; Plzen's superior European experience and slightly better attack should edge a tight game despite their poor form.
09
GLM 5.3
Lillestrom win
2–1
52%
Lillestrom's steadier EL form (1W 2D) and home edge against a leaky Plzen side (0 clean sheets, 4 losses) favor a narrow home win; Plzen's scoring suggests they find a goal.
10
MiMo V2.6 Pro
Draw
1–1
38%
Both sides sit bottom of the league stage with leaky defenses (Lillestrom 4-10, Plzen 7-11) and similar struggles; a share of the spoils suits two teams low on confidence, though Plzen's greater goal threat keeps this uncertain.
— Scoreline frequency

How often each scoreline showed up.

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

  • 1–1
    6 models
  • 2–1
    2 models
  • 1–0
    1 model
  • 1–2
    1 model

Season data

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

Lillestrom

4-3-3
W-D-L
1-2-2
Goals
4-10
Clean sheets
1
Failed to score
2
Best win run
1
Penalties
0/1
When they score
15'30'45'60'75'90'
When they concede
15'30'45'60'75'90'

Plzen

4-4-1-1
W-D-L
1-0-4
Goals
7-11
Clean sheets
0
Failed to score
2
Best win run
1
Penalties
2/3
When they score
15'30'45'60'75'90'
When they concede
15'30'45'60'75'90'

Key players · Lillestrom

  • T. Lehne Olsen 2 G · 0 A · 4 apps

Key players · Plzen

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

Top scorers · UEFA Europa League

  1. 1 L. Diony Omonia Nicosia 4
  2. 2 A. Cissè AC Milan 3
  3. 3 Yeremy Pino Crystal Palace 3
  4. 4 Sergio Gómez Real Sociedad 3
  5. 5 K. Zachariassen Ferencvarosi TC 3
New at Lillestrom F. Ottosson (IFK Goteborg), D. Bassi (Bodo/Glimt), H. Melland (Aalesund)
New at Plzen T. Pališčák (Žilina), D. Vavro (VfL Wolfsburg), A. Gabriel (FC Midtjylland)
— Aggregated insights

What’s moving the panel.

01
Models lean the draw

60% of models take a stalemate — cross-check score frequency and 1X2 strip before sizing.

02
Low confidence

Mean 44% across the panel with real dispersion — compare unanimous calls vs split tickets in the model table.

03
xG tilt 1.20 vs 1.00

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.

Lillestrom
#23 · 1 PTS · GD -1
Last 5
W W L W D
8 GF · 10 GA
Recent fixtures
  • Sogndal 2-1 W
  • Brann 3-2 W
  • Real Sociedad 0-6 L
  • Sandefjord 0-2 W
  • Sparta Praha 1-1 D
Team news
F. Elkaer — Knee Injury R. Gabrielsen — Shoulder Injury E. Lucky — Ankle Injury Y. Paintsil — Injury E. B. Garnas — Injury K. Krygard — Injury M. Waehler — Shoulder Injury E. Kitolano — Broken Leg T. Olsen — Leg Injury Y. Ibrahimaj — Yellow Cards U. Jenssen — Injury H. Woxen — Red Card
Plzen
#31 · 0 PTS · GD -4
Last 5
W L L W L
12 GF · 9 GA
Recent fixtures
  • Vysočina Jihlava 1-6 W
  • Pardubice 1-2 L
  • Levski Sofia 1-2 L
  • FK Jablonec 2-3 W
  • Ferencvarosi TC 2-1 L
Team news
V. Jemelka — Knee Injury J. Paluska — Surgery M. Tvrdon — Off the roster M. Vydra — Knee Injury P. Adu — Coach's decision A. Sojka — Health problems

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 Lillestrom, treat that as the default script unless late team news breaks the assumptions.
  • Watch the draw lane at 60% — tight UEFA Europa 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.

Lillestrom
~30%
implied lean
Draw
~60%
implied lean
Plzen
~10%
implied lean

Over / under prediction (totals)

Model-derived xG sums to 2.20 goals in expectation. A notional totals line near 2.5 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 Lillestrom 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 2.20 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: Lillestrom 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 Lillestrom vs Plzen in UEFA Europa 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 Lillestrom win, with vote shares roughly 30% / 60% / 10% home, draw, and away (rounded).

If you are asking who will win Lillestrom vs Plzen, 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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Ask multiple AIs about this match.

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

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