Argentina 3–1 Switzerland: how the Jarvis supercomputer's prediction held up
Nhipbong Analytics
Jarvis prediction desk
Argentina closed out their match against Switzerland with a 3–1 win, exactly the direction the Jarvis supercomputer model had leaned before kickoff. But there is one notable gap: the match produced four goals, well above the 2.2 expected, and Switzerland even drew level at 1–1 to take the game into extra time, despite the model's lower-scoring lean. Let me walk through the numbers side by side.
- Argentina beat Switzerland 3–1 at the FIFA World Cup.
- Before kickoff, Jarvis leaned toward Argentina with a 57% chance to win, 28% draw, 16% Switzerland.
- The model's predicted score was 2–0 for Argentina; the actual result was 3–1, right winner but more goals than expected and no clean sheet.
- Total expected goals sat at just 2.2, yet the match produced four goals, clearing the over 2.5 goals mark too (38% pre-match).
- Both teams scored, though the model gave that scenario only a 40% chance.
- Confidence was labelled LOW before kickoff, and the secondary numbers did drift even though the winner was correct.
| Outcome | Argentina | Draw | Switzerland |
|---|---|---|---|
| Jarvis probability | 57% | 28% | 16% |
| Metric | Jarvis forecast |
|---|---|
| Expected total goals | 2.22 |
| Predicted score | 2-0 |
| Chance of 3+ goals | 38.2% |
Head to head: Jarvis prediction vs the actual result
| Category | Prediction (pre-match) | Actual | Correct? |
|---|---|---|---|
| Winner | Argentina (57%) | Argentina (3–1) | ✓ Right |
| Predicted score | 2–0 | 3–1 | ✗ Off |
| Expected total goals | 2.2 goals | 4 goals | ✗ Off |
| Chance of 3+ goals | 38.2% | Yes | ✗ Off |
How it finished: Argentina win 3–1
The match between Argentina and Switzerland at the FIFA World Cup closed out 3–1 in Argentina's favour after 120 minutes. Alexis Mac Allister opened the scoring on 10 minutes, Dan Ndoye equalised for Switzerland on 67, and it was 1–1 at the end of the regulation 90. Argentina's two decisive goals came in extra time: Julián Álvarez scored on 112 minutes and Lautaro Martínez sealed the 3–1 at 120+1.
What is clear from the 3–1 scoreline is that both sides found the net, and Argentina closed the game out with a two-goal cushion, even if that cushion only arrived late in extra time. A four-goal match with both teams scoring is, on its own, exactly the kind of result worth setting against the numbers the Jarvis supercomputer model produced before kickoff, and that comparison is where I want to spend most of the space below.
Jarvis checked against the result: pre-match forecast vs what actually happened
This is the part I want to take slowly and carefully, because the real value of a post-match piece is putting the forecast next to the result to see how far the model's read actually held up. Before kickoff, Jarvis leaned toward three outcomes: Argentina to win at 57%, a draw at 28%, Switzerland to win at 16%. The 3–1 result landed exactly in the scenario rated highest, so on the single most important question, who wins, the model got it right from the start.
But the deeper I go into the secondary numbers, the wider the gap gets, and the standout is the goal count. Jarvis expected just 2.2 goals for the whole match and sketched a lean, predicted 2–0 scoreline. The reality was four goals, well above the 2.2 expected, a bigger miss than any other figure here. If I had to pick one line to sum up where the model came up short, it is the total-goals line: it turned what was pictured as a tight, controlled match into an open one with four balls hitting the net.
In the same vein, Jarvis gave the match only a 38% chance of going over 2.5 goals, meaning the model leaned toward a lower-scoring game; the actual four goals pushed the match well past that mark. The 2–0 predicted score also modeled Argentina would keep a clean sheet, but Switzerland still broke through for their 67th-minute equaliser. So the model got the winning side right, and got it right that Argentina would score more, and only slipped on the idea that their goal would stay untouched.
The last detail is the both-teams-to-score chance, which Jarvis set at 40%, meaning the model leaned toward one side shutting the other out. The opposite happened. Putting it all together, I think the LOW confidence label Jarvis attached beforehand describes the picture fairly honestly: the biggest call was right, while nearly every secondary detail leaned toward the outcomes the model had rated lower. This is exactly the kind of match a forecast that flags its own low confidence should be warning about, and on that front Jarvis was being straight with us.
57% is not a promise
One thing I always point out on Nhip Bong: a pre-match figure of 57% for Argentina is not a guaranteed win, it is simply where the lean sits among three possible outcomes. Even when one side is rated clearly ahead, the rest of the picture, a 28% draw and a 16% Switzerland win, never goes away and can absolutely play out.
This match is a good example of why the LOW confidence label matters. The model read the winner correctly, but derived numbers like total goals, the 2.5-goal mark, and both teams scoring all leaned against the pre-match direction. Probability does not commit to every single detail, it only describes a range of what can happen, and inside that range there is always room for the lower-rated scenarios to surface unexpectedly.
That is also why I always encourage reading these numbers as a layered probability picture rather than fixating on one figure and expecting it to land perfectly. The main layer, the winner, being right does not mean every secondary layer has to follow suit. Argentina against Switzerland is a tidy example of exactly that.
Data sources and notes
All the pre-match forecast figures in this piece, the win-draw-loss probabilities, the predicted score, total expected goals, the over 2.5 goals chance, and the both-teams-to-score chance, come from the Jarvis supercomputer model. The final 3–1 scoreline is the actual result of the match, used here to check against those figures.
The scorers and goal times (Mac Allister 10', Ndoye 67', Álvarez 112', Lautaro Martínez 120+1') come from the match record.
This is statistical analysis, not betting advice.
Frequently asked questions
What was the final score between Argentina and Switzerland?
Argentina beat Switzerland 3–1 at the FIFA World Cup.
Which team did Jarvis rate higher before the match?
Argentina was rated highest among the three outcomes, with a 57% chance to win, compared with 28% for a draw and 16% for Switzerland to win.
Was Jarvis's 2–0 predicted score accurate?
Partly. The model got the winner right and correctly had Argentina scoring more goals, but the actual result was 3–1, more goals overall and including a Switzerland goal, unlike the clean-sheet scenario the 2–0 score modeled.
Why did the actual total goals (4) come in so much higher than expected (2.2)?
Most of the gap opened up in extra time. It was 1–1 after 90 minutes, two goals in all, and the other two, from Julián Álvarez (112) and Lautaro Martínez (120+1), came in the extra 30 minutes. The forecast set also carried a LOW confidence label from the start.
Who scored in this match?
Alexis Mac Allister put Argentina ahead on 10 minutes and Dan Ndoye equalised for Switzerland on 67. In extra time, Julián Álvarez (112) and Lautaro Martínez (120+1) scored to give Argentina a 3–1 win.