Prediction Audit: Follo vs Rana Prediction, Odds & AI Betting Tips

Aug 16, 2026 - 11:00
1 1.26
3 1.34
xG Accuracy: 59%

AI correctly predicted the Rana win.

The match finished 1–3, validating the model's directional assessment.

Tracked markets vs full-time result

Prediction grade B-

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

  • Market Prediction Result Outcome
  • Over / Under 2.5 Under 2.5 Over 2.5 (4 goals) ✖ Incorrect
  • Both Teams To Score BTTS Yes Yes ✔ Correct
  • 1X2 Rana Rana ✔ Correct
  • Correct Score Insights 1-1, 0-1, 1-0, 1-2, 2-1 1-3 ✖ Incorrect

Model vs Closing Market

Moderate Disagreement

The closing market prices Rana higher than the statistical model.

Largest probability gap: Rana -6.8 pp

Outcome Model Closing Market Difference Signal
Follo 33.3% 30.6% +2.7 pp Aligned
Draw 29.6% 25.5% +4.1 pp Aligned
Rana 37.1% 43.8% -6.8 pp Market Higher

The closing market estimates Rana's win probability at 43.8%, compared with the model's estimate of 37.1%, a difference of 6.8 percentage points. This highlights a disagreement between the model and market consensus, without indicating which view is ultimately correct.

Model probabilities are generated from the statistical xG model using a Poisson distribution. Closing market probabilities are derived from consensus closing 1X2 odds after margin removal. Values represent implied probabilities rather than betting recommendations. Closing snapshot: PRE1.

After full time, the model's directional lean matched the result (Rana win 1–3).

Market Assessment

The market and model broadly agree on Rana. The remaining divergence may reflect differences in team-strength assumptions rather than a directional disagreement.

  • Monitor line movement before kickoff — not a betting recommendation.

Post Match Insights

What worked

  • Expected goals projected a high-scoring match (ΣxG 2.60) — 4 goals materialised
  • Both Teams To Score (Yes) matched the full-time result

What failed

  • Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (4 goals)
  • Exact score: outside the model's top score bins

Market lesson

Large model–market gaps do not automatically mean the market is right. Here the closing market priced Rana higher (43.9% vs model 37.1%, 6.8 pp), but the model's lean was validated (Rana win 1–3).

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Prediction Timeline

How this prediction moved from forecast to full-time review.

  1. Aug 16, 2026 · 10:59 UTC Forecast generated
    • Model 1X2 · Follo 33.4% · Draw 29.6% · Rana 37.0%
    • xG · Follo 1.26 — Rana 1.34
  2. Aug 16, 2026 · 10:30 UTC Opening odds snapshot PRE30
    • 1X2 odds · Follo 2.99 · Draw 3.59 · Rana 2.09
    • Implied 1X2 · Follo 30.6% · Draw 25.5% · Rana 43.8%
    • Bookmaker · Pinnacle
  3. Aug 16, 2026 · 10:59 UTC Closing snapshot recorded PRE1
    • 1X2 odds · Follo 2.99 · Draw 3.59 · Rana 2.09
    • Implied 1X2 · Follo 30.6% · Draw 25.5% · Rana 43.8%
    • Bookmaker · Pinnacle
  4. Aug 16, 2026 · 11:00 UTC Kickoff
  5. FT Full-time result Rana win · 1–3
  6. FT Prediction validated Directional lean matched full-time result
  7. Archived Prediction review

Historical Snapshot

Frozen at kickoff — the model output as it stood before the match started.

Historical verdict: Observe
Historical Decision No Primary Bet
Outcome Validated
Pre-match metrics (historical context)
Prediction Reliability 76/100 · High
  • Validation: Pass
Evidence ★★★★★
  • No strong statistical edge
  • Direction agrees with model lean
  • Validation passed
Pricing proximity (inverse gap) 66/100
Betting Confidence 79/100

Validation Report

Immutable Snapshot

Prediction Time: Aug 11, 2026 · 00:59 UTC Snapshot ID: dp-3601011

Closing Odds 2.09
AI Fair Odds —
CLV Pending
Final Result Rana win · Follo 1–3 Rana
Prediction ✔ Correct
Decision Grade B-

Model Performance

This prediction contributes to:

  • Primary Bets ROI (180d): -100.0%

Review FAQ

How accurate was the prediction?
This page grades directional markets (1X2, Over/Under 2.5, BTTS) against the full-time result. The prediction grade reflects how many of those tracked markets matched reality.
What does xG Accuracy measure?
xG Accuracy compares the model's pre-match expected-goals profile to the actual scoreline — not whether every market hit. A strong directional review can coexist with a moderate xG accuracy score.
Why wasn't the exact score predicted?
Correct-score outcomes are low-probability tails even when the model reads the match profile well. We highlight top score bins for context; missing the exact line does not invalidate a directional review.
Does this improve the AI record?
Each finished match is logged in our validation pipeline. Aggregated hit rates and CLV studies are published separately — this page is the per-match audit trail.

Predictions are for informational purposes only. Always gamble responsibly and within your limits. Past performance does not guarantee future results.

AI match briefing

AI Match Summary

Quick read on how the model reads this matchup.

  • League: 2. Division - Group 2
  • Fixture: Follo vs Rana
  • Kickoff: 2026-08-16 11:00:00
  • 1X2 (model): Home 33.4% · Draw 29.6% · Away 37.0%
  • xG (showing): Follo 1.26 — Rana 1.34 (total xG ≈ 2.6)
  • Value headline: None — no positive EV on tracked lines at current best prices (same as the decision block: no default bet).
  • Structural leans (not bets): Structural lean (model): O/U 2.5 Under 2.5 (Under 2.5 51.8% · Over 2.5 48.2%); BTTS Yes (Yes 54.5% · No 45.5%) Value lean (pricing): O/U 2.5 Under 2.5; BTTS Yes
  • BTTS (model): Yes 54.5% · No 45.5%
  • Correct score (top bin): 1-1 (12.5%)

When book depth is thin or odds are missing, EV may be unavailable even though the model still prefers one side on totals or BTTS — wait for cleaner prices or skip.

Most likely correct score stays a low-probability tail: use it for context, not as a must-bet story.

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 28, 2026 (UTC)

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Back to Predictions
2. Division - Group 2 2. Division - Group 2 — Standings
# TEAM MP W D L PTS
1 Eidsvold 20 15 3 2 48
2 Levanger 20 15 0 5 45
3 Kjelsås 21 14 2 5 44
4 Hønefoss 20 13 3 4 42
5 Grorud 20 11 4 5 37
6 Stjørdals-Blink 21 9 3 9 30
7 Lørenskog 20 9 3 8 30
8 Tromsdalen Uil 20 9 2 9 29
9 Junkeren 20 8 4 8 28
10 Rana 20 7 3 10 24
11 Skeid 20 5 2 13 17
12 Follo 21 3 5 13 14
13 Ull/Kisa 21 3 1 17 10
14 Trygg/Lade 20 2 3 15 9
# TEAM MP GS GC +/- PTS
1 Levanger 20 51 21 +30 45
2 Hønefoss 20 51 28 +23 42
3 Kjelsås 21 49 31 +18 44
4 Eidsvold 20 48 16 +32 48
5 Stjørdals-Blink 21 37 38 -1 30
6 Grorud 20 34 20 +14 37
7 Tromsdalen Uil 20 34 34 0 29
8 Rana 20 34 38 -4 24
9 Lørenskog 20 29 32 -3 30
10 Junkeren 20 29 33 -4 28
11 Skeid 20 27 43 -16 17
12 Follo 21 25 45 -20 14
13 Ull/Kisa 21 25 70 -45 10
14 Trygg/Lade 20 23 47 -24 9