Statistics / Football / Lithuania. A Lyga / Banga vs Hegelmann Litauen

Banga vs Hegelmann Litauen Statistics & Analysis

Banga Banga
Jun 28, 2026 - 15:00
2 1.29
0 1.31
xG Accuracy: 57%
Hegelmann Litauen Hegelmann Litauen
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Tracked markets vs full-time result

Each row compares the pre-match model lean to the full-time result.

  • Market Prediction Result Outcome
  • Over / Under 2.5 Over 2.5 Under 2.5 (2 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes No ✖ Incorrect
  • 1X2 Banga Banga ✔ Correct
  • Correct Score Insights 1-1, 0-1, 1-0, 1-2, 2-1 2-0 ✖ Incorrect

Validation Report

Immutable Snapshot

Prediction Time: Jul 24, 2026 · 15:12 UTC Snapshot ID: dp-1610470

Closing Odds 1.97
AI Fair Odds —
CLV +0.0%
Final Result Banga win · Banga 2–0 Hegelmann Litauen
Prediction ✔ Correct
Decision Grade F

Model Performance

This prediction contributes to:

  • Primary Bets ROI (180d): -100.0%

Market intelligence

Supporting read on how the prioritized closing feed moved versus the model — use after the Primary pick above.

Market briefing

Market remained largely stable before kickoff. No meaningful late implied-price shift was detected between PRE30 and PRE1 on the prioritized bookmaker snapshot.

Despite limited late movement, the model still prices Draw (1X2), Hegelmann Litauen (1X2), Over 2.5 goals meaningfully above what those closing snapshots implied — that gap is a static “model vs. price” read, not a late steam or chase story.

The model still exceeds closing implied on Hegelmann Litauen (1X2) by about 7.4 percentage points — the clearest mispricing signal summarized on this page.

Model vs. closing implied

Market Model % Closing impl. % Gap (pp)
Banga (1X2) 38.3 47.1 -8.8
Draw (1X2) 29.4 28.0 +1.4
Hegelmann Litauen (1X2) 32.3 24.9 +7.4
Over 2.5 goals 48.2 46.9 +1.3
Under 2.5 goals 51.8 53.1 -1.3
What this means

In plain terms: the model lands near 32.3% on Hegelmann Litauen (1X2), while the closing snapshot implied about 24.9%. The difference — about 7.4 percentage points — is the largest model-vs.-market gap highlighted on this page.

Quick definitions: “closing implied” is the probability for that outcome implied by the final captured odds (after a simple de-vig). “Gap (pp)” is the model percentage minus that implied value, in percentage points (pp).

Closing-window line move

Single prioritized bookmaker per snapshot (not all books). Capture path: PRE30 → PRE1 · Book: Pinnacle

Column tags in parentheses: Closing uses the first available snapshot in PRE1→PRE5→PRE10→PRE30; Early uses the first available in PRE30→PRE10→PRE5 that is not the same capture as Closing.

Detailed capture odds are folded below — movement was negligible on de-vig implied prices.

View full line-by-line capture table
Market Early (PRE30) Closing (PRE1) Implied Δ (pp)
Banga (1X2) 1.97 1.97 0.0
Draw (1X2) 3.32 3.32 0.0
Hegelmann Litauen (1X2) 3.73 3.73 0.0
Over 2.5 goals 2.03 2.03 0.0
Under 2.5 goals 1.79 1.79 0.0

AI match briefing

AI Match Summary

Pre-match snapshot for this fixture.

  • League: A Lyga
  • Fixture: Banga vs Hegelmann Litauen
  • Kickoff: 2026-06-28 15:00:00
  • 1X2 (model): Home 38.3% · Draw 29.4% · Away 32.3%
  • xG (showing): Banga 1.29 — Hegelmann Litauen 1.31 (total xG ≈ 2.6)
  • Value headline: None (actionable) — best tracked EV is about +0.8%, still below the +2.0% minimum for a headline / default stake (no default bet).
  • Structural leans (not bets): Value lean (pricing): O/U 2.5 Over 2.5; BTTS Yes
  • BTTS (model): Yes N/A · No N/A
  • Correct score (top bin): N/A

Saying “no value” on a snapshot is a feature, not a bug: it protects readers from forcing a play when the edge is not there.

If lines move materially, re-run generation or refresh — implied probabilities and any future EV readouts will change first.

Historical Recommendation

Historical Decision: No Primary Bet

Outcome: Validated — Pre-match lean validated against the full-time result.

Risk Factors Considered Before Kickoff

  • Price movement: implied probabilities and EV move with odds.
  • Sample / data gaps: low-information leagues widen forecast bands.
  • In-play state: goals and red cards are not modelled here.
  • Scoreline variance: the most likely scoreline is still usually a low absolute probability outcome (often well below 20%).

Last Updated

September 29, 2026 (UTC)

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Back to Statistics
A Lyga A Lyga — Standings
# TEAM MP W D L PTS
1 Kauno Žalgiris 28 14 9 5 51
2 Suduva Marijampole 28 13 11 4 50
3 TransINVEST Vilnius 28 13 6 9 45
4 FK Zalgiris Vilnius 28 13 5 10 44
5 Banga 28 11 8 9 41
6 Džiugas Telšiai 28 10 8 10 38
7 Panevėžys 28 9 5 14 32
8 Hegelmann Litauen 29 4 14 11 26
9 Šiauliai 29 3 8 18 17
10 FK Trakai 0 0 0 0 0
# TEAM MP GS GC +/- PTS
1 Kauno Žalgiris 28 56 20 +36 51
2 TransINVEST Vilnius 28 43 35 +8 45
3 FK Zalgiris Vilnius 28 41 34 +7 44
4 Suduva Marijampole 28 39 25 +14 50
5 Džiugas Telšiai 28 36 40 -4 38
6 Banga 28 32 28 +4 41
7 Hegelmann Litauen 29 31 48 -17 26
8 Panevėžys 28 29 43 -14 32
9 Šiauliai 29 24 58 -34 17
10 FK Trakai 0 0 0 0 0