Qatar vs El Salvador Prediction
Qatar vs El Salvador: Model Favors Tight Contest in World Cup 2026 Friendly
International Friendly · World
Qatar
AI Predicted Score
1 - 0
El Salvador
Win Probability (1X2)
Best Value Pick
BTTS: Yes
@ 5
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Expected goals · model says 1.30
Expected goals · model says 1.05
Over 2.5 Goals
42%
BTTS: Yes
47%
Likely Scores
1-1 · 1-0 · 0-1
Play this match once with the probabilities above — every run is different.
Powered by the same Poisson scoreline model behind the prediction. For fun and exploration — not betting advice.
Qatar vs El Salvador Prediction Confidence at a Glance
How strongly the model leans on each market.
Who do you think wins: Qatar vs El Salvador?
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The Case For This Qatar vs El Salvador Prediction
Qatar and El Salvador face off at BMO Stadium in a World Cup 2026 warm-up, with the model seeing little between the sides despite Qatar's slight Elo advantage. The analytics project a low-scoring affair, with both teams struggling for recent form and offensive output. Qatar's historical edge and marginally higher expected goal rate give them a modest edge, but the probabilities suggest a close encounter where any result is plausible. The model's most likely outcomes are a 1-1 draw (13.1%), a narrow 1-0 win for Qatar (12.8%), or a 0-1 win for El Salvador (10.2%). With both teams showing muted attacking momentum and defensive vulnerabilities, the numbers point to a cagey match where moments of quality could decide the result.
Key Factors at a Glance
Recent Form
- →Blended expected goals: Qatar 1.28, El Salvador 1.03 after momentum adjustment
Attack
- →Both sides show negative recent-form momentum on attack (0.85 multiplier)
Match Context
- →Qatar holds an Elo edge of +83 points (1425 vs 1342) and home rank advantage
- →Top correct scores: 1-1 (13.1%), 1-0 (12.8%), 0-1 (10.2%)
- →Model sees under 2.5 goals as more likely (59.5%) than over
Elo Power Ratings
World Football Elo ratings — the model's baseline for relative strength.
Expected Goals & Scorelines
Elo and xG combine into a blended scoring rate per side, then a Poisson model gives the exact-scoreline grid.
From Elo
1.47 · 1.13
From xG
0.99 · 1.09
Blended
1.28 · 1.03
Hybrid blend: 60% Elo-derived, 40% xG-derived, then adjusted for recent-form momentum.
Recent-form momentum: Qatar's cold attack trims their goals ×0.85; Qatar's leaky defence boosts the opponent ×1.08; El Salvador's cold attack trims their goals ×0.85; El Salvador's leaky defence boosts the opponent ×1.18.
Exact-scoreline probabilities (rows = Qatar goals, columns = El Salvador goals). Darker = more likely.
| 0 | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|
| 0 | 10 | 10 | 5 | 2 | 0 | 0 |
| 1 | 13 | 13 | 7 | 2 | 1 | 0 |
| 2 | 8 | 8 | 4 | 1 | 0 | 0 |
| 3 | 3 | 4 | 2 | 1 | 0 | 0 |
| 4 | 1 | 1 | 1 | 0 | 0 | 0 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 |
Most Likely Scorelines
1-1
13.1%
1-0
12.8%
0-1
10.2%
How the Model Reached This
The model starts by assigning Qatar an Elo advantage of 83 points, reflecting their stronger historical results and higher global ranking. This baseline translates to a home expected score of 0.617 for Qatar and 0.383 for El Salvador, indicating a modest but clear edge for the hosts. Next, the model evaluates each team's attacking and defensive profiles. Qatar's attack factor (0.76) is below average but is slightly boosted by above-average finishing (1.2). El Salvador's attack and defense are set at baseline (1.0), due to limited data. Both sides' recent form is factored in via a momentum multiplier of 0.85 on attack, reflecting a downturn in goal-scoring output over the last five matches. This adjustment lowers the expected goals for both teams, resulting in final expected goals of 1.28 for Qatar and 1.03 for El Salvador. Finally, these adjusted scoring rates are run through a Poisson distribution to simulate exact scorelines and match probabilities. The model outputs a 42.2% chance of a Qatar win, 28.0% for a draw, and 29.8% for an El Salvador win, with a slight lean toward under 2.5 goals (59.5%).
Where the Model Sees Value: Qatar vs El Salvador(2)
Selections where the model's probability beats the bookmaker's implied probability. Expected value is computed from real consensus odds, not opinion.
BTTS: Yes@ 5
Model probability of 46.3% (implied odds 2.16) vs bookmaker odds of 5.0 provides substantial value.
Over/Under 2.5: Over 2.5@ 5
Model probability of 40.6% (implied odds 2.46) vs bookmaker odds of 5.0 offers strong positive expected value.
More Friendlies Predictions
Where the Model Disagrees with the Odds: Qatar vs El Salvador
Comparing the model's probabilities to bookmaker odds reveals potential value on the draw. The model assigns a 28.0% chance to a draw (implied odds ~3.57), while bookmakers are offering 3.5, which is roughly in line but slightly favorable to the bettor. The strongest value, however, appears in the 'Both Teams to Score: Yes' and 'Over 2.5 Goals' markets. The model gives BTTS: Yes a 46.3% chance (implied odds ~2.16), while bookmakers are offering 5.0, and Over 2.5 Goals a 40.6% chance (implied odds ~2.46) with odds at 5.0. Both represent significant positive expected value opportunities.
Odds from Bet365. Positive edge = our model gives a higher probability than the odds imply. EV = expected value per £1 staked.
1X2
Over/Under 2.5
Both Teams Score
Best Value Markets Found by the Model
EV per £1 staked. Positive EV means this bet has mathematical edge over the long run. Past model accuracy is tracked on our predictions tracker.
Edge and EV are derived from model probabilities only and do not constitute financial advice. Odds fluctuate — always verify current prices with your bookmaker.
Qatar vs El Salvador Goalscorer Form
Goals scored in last 5 matches across all competitions. Rate = goals ÷ 5 games. XI badge shows player is in the expected starting lineup.
Qatarlast 5 matchesOccasional threat
Occasional threat
El Salvadorlast 5 matchesRegular contributor
Occasional threat
Recent form is underwhelming for both teams. Qatar have just one point from their last five games (LLDLL), averaging only 0.4 goals scored and 1.6 conceded per match. El Salvador's form is similarly poor (LWDLL), with 0.6 goals scored and 2.0 conceded on average. Both teams have failed to score in three of their last five outings, and neither has kept a clean sheet in that span. The model's momentum adjustment reflects these struggles, dampening both sides' expected attacking output.
Qatar vs El Salvador — Last 1 Matches
Concerns
- −4/5 losses in last 5 — poor form
- −Struggling to score — 0.4 goals/game
- −No clean sheets in last 5 matches
Concerns
- −Inconsistent — 3+ losses in last 5
- −Struggling to score — 0.6 goals/game
- −Leaky defence — 2.0 conceded/game
🚩 Qatar vs El Salvador Corners Prediction
medium confidenceOur corners model projects roughly 4.8 corners for Qatar and 3.3 for El Salvador, for an expected total of 8.1. That makes the model lean Under 9.5 corners (70%). Over 8.5: 43% · Over 10.5: 20%.
What History Says: Qatar vs El Salvador
Qatar and El Salvador have met twice before, with Qatar winning both encounters. Their most recent clash was a 3-2 win for Qatar in the 2021 Gold Cup, and before that, Qatar edged a 1-0 friendly victory. While the sample is small, Qatar's perfect record in this matchup provides a slight psychological edge, though the teams have not faced each other frequently.
Qatar vs El Salvador Head-to-Head History
Last 2 meetings between these sides
2
Qatar wins
0
Draws
0
El Salvador wins
Things to Watch: Qatar vs El Salvador
- ⚠️International friendlies can be unpredictable due to experimental lineups and motivation.
- ⚠️Recent form data is limited and may not fully capture each team's current strength.
- ⚠️El Salvador's analytics are based on sparse data, increasing uncertainty in projections.
- ⚠️Betting markets may move as team news emerges closer to kickoff.
This analysis is for informational purposes only and does not constitute financial advice. Betting involves risk — always gamble responsibly.
How This Prediction Was Made
The numbers on this page come from a structured statistical pack for Qatar vs El Salvador: recent results and scoring data for both sides, home/away splits, the head-to-head record, current league standings, known injuries and live bookmaker odds, all sourced from the API-Football data feed.
A deterministic model — The Analytical Engine — turns that pack into the prediction: Elo ratings set the baseline, expected-goals output and defensive suppression adjust each side's scoring rate, recent form nudges the totals, and a Poisson model converts the final rates into the scoreline and market probabilities shown above. The written analysis is AI-generated from those fixed numbers and is not edited by hand.
Last updated: 5 June 2026 (refreshed automatically as new team data arrives). Every prediction is scored against the real result once the match finishes — see the verified track record or read the full methodology. Football is inherently uncertain: treat this page as analysis, not advice.