Marines vs Etincelles Statistics & Analysis

May 16, 2026 - 13:00
2 1.45
3 1.25
xG Accuracy: 53%
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Tracked markets vs full-time result

Each row compares the model’s highlighted side (or lean) to what happened at full time.

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

AI match briefing

AI Match Summary

Below is a compact, numbers-first snapshot aligned with the same engine as the cards above.

  • League: National Soccer League
  • Fixture: Marines vs Etincelles
  • Kickoff: 2026-05-16 13:00:00
  • 1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
  • xG (showing): Marines 1.45 — Etincelles 1.25 (total xG ≈ 2.7)
  • Best +EV line (same label as hero card when Primary thresholds are not met): Under 2.5 goals
  • Model: 62.3% · Implied: 55.8% · Probability edge: +6.5 pts · Est. EV: +4.0%
  • BTTS (model): Yes 46.8% · No 53.2%
  • Correct score (top bin): 1-0 (13.9%)

Use the cards for tiering; this text only restates the same inputs in narrative form.

Early match state can move realised goals away from pre-kick projections.

Best Bet + Reason

Strongest +EV line among tracked markets (not shown as a full Primary under current engine thresholds): Under 2.5 goals.

If 1X2 looks tight, the engine may still find clearer structure in totals or BTTS — that is intentional.

Edges shrink quickly if prices move; always re-check the number on your book.

FAQ

Is the most likely correct score a good bet?

Usually no as a standalone bet: the “most likely” scoreline is still a low absolute probability tail event (often single digits, sometimes low teens). Use it as context; keep any correct-score stake in the “fun / small” bucket.

What changes first if odds move?

Implied probabilities and EV move immediately with price; model probabilities in this snapshot do not update until the pipeline is re-run. Refresh after material line moves.

Why might 1X2 look unattractive while totals do not?

Tight 1X2 prices often embed a fair three-way split, so EV on match-winner can sit negative even when Over/Under or BTTS still diverges from the model — compare the 1X2 row on the market cards to O/U and BTTS.

Who has the edge in the match-winner market?

Use the 1X2 model percentages in the summary and the 1X2 market card: the side with the highest model % is the model lean, but check EV — a lean can still be -EV after prices.

Risk Factors

  • 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%).

Methodology

  • Inputs: Same structured facts bundle as the public prediction page (xG / Poisson snapshot, market EV where available, decision engine v2).
  • Compliance: Educational framing only; not personalised advice.

Last Updated

May 18, 2026 (UTC)

How to use this
  • Focus on the Primary line when you want one actionable idea.
  • Do not parlay many thin-edge picks together; edges do not add reliably.
  • Treat longshots as optional, high-stake-sizing plays only.

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Back to Statistics
National Soccer League National Soccer LeagueStandings
# TEAM MP W D L PTS
1 Al Hilal Omdurman 32 23 5 4 74
2 APR 31 16 11 4 59
3 Al Merreikh 30 16 10 4 58
4 Rayon Sports 32 13 13 6 52
5 Kiyovu Sports 32 13 12 7 51
6 Police 32 11 15 6 48
7 Mukura 32 12 10 10 46
8 Marines 32 11 8 13 41
9 Bugesera 32 11 8 13 41
10 Musanze 31 10 9 12 39
11 Gorilla 32 9 10 13 37
12 Gasogi United 32 8 11 13 35
13 Gicumbi 32 9 8 15 35
14 AS Kigali 32 7 11 14 32
15 Etincelles 32 5 16 11 31
16 Amagaju 31 8 7 16 31
17 Muhanga 32 7 7 18 28
18 Rutsiro 31 5 11 15 26
# TEAM MP GS GC +/- PTS
1 Al Hilal Omdurman 32 72 22 +50 74
2 APR 31 43 27 +16 59
3 Marines 32 43 40 +3 41
4 Al Merreikh 30 38 17 +21 58
5 Rayon Sports 32 37 29 +8 52
6 Gicumbi 32 35 50 -15 35
7 Mukura 32 34 35 -1 46
8 Kiyovu Sports 32 33 20 +13 51
9 Musanze 31 33 32 +1 39
10 Bugesera 32 32 37 -5 41
11 Police 32 28 19 +9 48
12 Gorilla 32 28 37 -9 37
13 Rutsiro 31 28 41 -13 26
14 Gasogi United 32 27 40 -13 35
15 Etincelles 32 26 39 -13 31
16 AS Kigali 32 24 36 -12 32
17 Muhanga 32 21 43 -22 28
18 Amagaju 31 18 36 -18 31