Last Updated on June 18, 2026 by Maurya
With the 2026 FIFA World Cup now in full swing across the United States, Canada, and Mexico, a new kind of pundit has muscled its way into the conversation: artificial intelligence. Microsoft Copilot, Anthropic’s Claude, OpenAI’s ChatGPT, Google’s Gemini, and Alibaba’s Qwen are all being asked to call match scores before kickoff. Major outlets, including USA Today, have leaned hardest on Copilot, feeding it all 104 fixtures and publishing its picks. So the obvious question for fans, bettors, and the merely curious is simple: how accurate is Microsoft Copilot at predicting World Cup matches, really?
The short answer is more interesting than a single percentage. Copilot has produced a handful of jaw-droppingly precise calls—and some equally spectacular misses. Below, we break down exactly where it has succeeded, where it has failed, and the well-documented reasons why a tool that can draft your emails struggles to outguess a goalkeeper having the night of his life.
Good at the Expected, Blind to the Chaos
If you want the headline before the deep dive, here it is. Microsoft Copilot is reasonably reliable when a heavy favorite meets a clear underdog and the favorite wins as predicted. It has even matched a few exact scorelines, which is genuinely impressive. But Copilot is systematically poor at the two things that define World Cup drama: upsets and draws. It regresses toward the “sensible” result, and in a tournament built on the improbable, that conservatism is a real weakness.
In other words, the surprise cuts both ways. Copilot is more precise than you would expect on routine matches, and more helpless than you would expect once the football gets weird.
How Copilot’s 2026 World Cup Predictions Were Tested
This is not a controlled academic experiment so much as a public, real-time stress test. Ahead of the tournament, USA Today Sports prompted Copilot to simulate the entire competition—group stage, a full knockout bracket, and score predictions for every round. Notably, Copilot first stumbled out of the gate: it initially tried to use the old 32-team field from the 2022 World Cup in Qatar rather than the expanded 48-team format. Once corrected, it produced a complete bracket, ultimately picking France to beat Brazil in the final, praising Les Bleus’ depth and tournament experience.
From there, USA Today and Yahoo Sports published Copilot’s specific score predictions on a day-by-day basis, then compared them against actual outcomes. That running scorecard is the cleanest public record we have of how the model performs when reality answers back. The findings below are drawn from those published results and from broader reporting on the tournament’s AI experiment.
Where Microsoft Copilot Got It Surprisingly Right
Let’s give the machine its due, because the hits are the part that genuinely surprises people.
On the opening day, Copilot called the curtain-raiser between Mexico and South Africa as a 2-0 Mexico win—and that was the exact final score. It also predicted South Korea 2-1 over the Czech Republic, again matching the result, including the comeback nature of the win. Perhaps most strikingly, it forecast a 1-1 draw between Brazil and Morocco, which is precisely how that match finished, with Morocco holding the Seleção.
Three exact scorelines in the opening rounds is not nothing. Predicting a winner is one thing; nailing the actual goal tally is a different order of difficulty, where luck and skill blur together. For a general-purpose chatbot that was not built for sports forecasting, landing several scorelines on the nose is a legitimately eye-catching result and the reason “the results may surprise you” is more than clickbait.
It is worth noting Copilot was not even the standout AI on this front. Qwen drew the most attention by predicting the Mexico–South Africa scoreline and flagging the red-card risk that materialized, plus correctly calling South Korea’s comeback. But Copilot held its own among the favorites.
Where Microsoft Copilot Fell Apart
Now the other side of the ledger—and it is a long one.
The clearest illustration came on a single day of group-stage action that produced a cluster of draws Copilot never saw coming. It had predicted Spain to thrash Cape Verde 3-0, Belgium to edge Egypt 2-1, Uruguay to beat Saudi Arabia 2-1, and Iran to nudge past New Zealand 1-0. In reality, every one of those matches ended level. Belgium–Egypt and Uruguay–Saudi Arabia both finished 1-1, Iran and New Zealand traded goals in a 2-2 thriller, and—most memorably—debutants Cape Verde held a star-studded Spain to a goalless 0-0, with goalkeeper Josimar “Vozinha” Dias turning in a viral, heroic shift.
The tell was not just that Copilot got the scores wrong. It was that a draw never appeared in its reasoning at all. Reporting on its Spain–Cape Verde logic noted the model assumed Spain’s attackers would pile up so many shots that Cape Verde’s defense would eventually crumble. It is a perfectly logical narrative. It simply isn’t what happened, and the model had no mechanism to weight the possibility that it wouldn’t.
Across the tournament’s early matches, Copilot’s full set of pre-tournament predictions has shown the same pattern: it captured chalk results but missed genuine shocks, reportedly failing to anticipate outcomes like Australia beating Turkey and Japan holding the Netherlands to a draw. The conservatism is consistent, and consistency in the wrong direction is its own kind of inaccuracy.
Why Copilot Struggles to Predict Football
This is where it helps to understand what Copilot actually is. A large language model (LLM) is a pattern-completion engine trained on text. When you ask it to predict a match, it is not running thousands of probabilistic simulations the way a dedicated sports model does. It is generating the most plausible-sounding answer based on patterns in its training data—and the most plausible-sounding answer is almost always that the better team on paper wins by a comfortable margin.
That intuition is backed by formal research. A pre-publication study testing top AI models on their ability to forecast short, three-to-fifteen-minute segments of soccer matches found that even the best-performing model was correct only about 43 percent of the time. Human forecasters, by contrast, hit 58.9 percent and—crucially—stayed well-calibrated, meaning their confidence levels matched reality. As the researchers put it, “humans reach 58.9 percent overall and remain well-calibrated, in contrast to” the AI models. Calibration is the technical heart of the problem: Copilot doesn’t just guess wrong, it is often confidently wrong, assigning near-certainty to outcomes that are anything but.
Other benchmarks tell the same story about depth of understanding. On the SportQA benchmark, which grades sports comprehension by difficulty, GPT-4 performed roughly 45 percent worse than human experts on the hardest, scenario-based reasoning questions. On the multimodal SPORTU benchmark, the strongest model tested topped out around 69.5 percent overall but slid to roughly 52.6 percent on the hard problems. Models can recall facts and rules well; they falter exactly where football lives—in messy, contingent, real-world situations.
The deeper reason upsets are invisible to Copilot is that an upset is by definition a low-probability event poorly represented in training text. The model has read far more about Spain winning than about Cape Verde holding Spain, so it weights toward the former. The very thing that makes a World Cup memorable is the thing an LLM is structurally biased to ignore.
Copilot vs. the Specialists: A Useful Reality Check
It is tempting to conclude that AI is simply bad at this, but that overstates the case. The honest framing is that general-purpose chatbots are bad at it, while purpose-built statistical models do considerably better.
Look back to the 2022 World Cup. Al Jazeera’s dedicated prediction engine, nicknamed Kashef and built on Google Cloud’s modeling tools, finished the tournament at roughly 67 to 68 percent accuracy and correctly called seven of the eight knockout-stage matches. Even so, it shared Copilot’s signature flaw: it played it safe in the group stage, failed to foresee any of the major upsets, and never picked eventual champions Argentina. Separately, a University of Southampton research team that fused machine learning with the language of human sports journalists reported about 63 percent accuracy, a meaningful step up from raw statistics alone.
The lesson is not “AI can’t predict football.” It is that a model designed and trained specifically for match forecasting, fed structured data like FIFA rankings, expected goals, and squad ratings, will outperform a chatbot improvising from text every time. Copilot is a generalist asked to do a specialist’s job.
There is also a humbling historical footnote. In 2010, an octopus named Paul “predicted” eight matches correctly by choosing between flag-marked feeding boxes. Several commentators have already drawn the comparison this year, and it stings precisely because it lands: a tool costing billions to develop has not clearly outperformed a mollusc’s lucky streak.
A Glimmer of Hope: AI Crowds and Human Input
One nuance keeps the picture from being entirely bleak. Research on the “wisdom of the silicon crowd” found that an ensemble of a dozen LLMs, aggregated together, can rival the accuracy of a human forecasting crowd—and that an individual model’s predictions improve by 17 to 28 percent when it is shown the median human forecast first. In plain terms, Copilot gets better when it borrows from human judgment rather than going it alone, and AI predictions are sharpest when blended with people, not used to replace them.
That points to how these tools should actually be used during the World Cup: as one input among many, a fast way to generate a baseline or a talking point, not an oracle to bet the mortgage on.
So, Should You Trust Copilot’s World Cup Picks?
Here is the responsible bottom line. Treat Copilot’s predictions as informed entertainment, not investment advice. The model is a reasonable guide to which side is favored, and it will occasionally stun you with an exact scoreline. But it cannot price in the chaos—the red card, the wonder-save, the underdog’s once-in-a-lifetime night—that makes the tournament worth watching. If you are wagering real money, remember that the published research shows these models are frequently overconfident, and that a goalless draw from a debutant nation is exactly the kind of result Copilot is built to overlook.
The genuine surprise, then, is not that an AI got some games right. It is the shape of its performance: eerily precise on the predictable, and almost endearingly clueless about everything that makes football beautiful. For now, the safest prediction is that human intuition, the betting markets, and yes, perhaps even an octopus, still have something the machines don’t.
FAQs
How accurate is Microsoft Copilot at predicting World Cup matches? Copilot is fairly reliable on matches where a clear favorite wins, and it has matched several exact scorelines at the 2026 World Cup, including Mexico 2-0 South Africa and Brazil 1-1 Morocco. However, it consistently fails to predict upsets and draws, and academic testing found the best AI models forecast soccer segments correctly only about 43 percent of the time, versus 58.9 percent for humans.
Did Copilot predict any exact World Cup scores correctly? Yes. In the opening rounds of the 2026 tournament, Copilot’s published predictions matched the exact final scores of at least three matches: Mexico 2-0 over South Africa, South Korea 2-1 over the Czech Republic, and a 1-1 draw between Brazil and Morocco.
Who does Copilot predict will win the 2026 World Cup? In a simulation run by USA Today, Copilot picked France to defeat Brazil in the final, citing France’s squad depth, tournament experience, and tactical flexibility.
Why is AI bad at predicting football upsets? Large language models generate the most statistically plausible outcome from their training data, which overwhelmingly favors stronger teams. Upsets are rare, low-probability events that are underrepresented in that data, so models tend to dismiss them entirely—often with high, poorly-calibrated confidence.
Is Copilot better or worse than dedicated sports prediction models? Worse. Purpose-built statistical models, such as the engine Al Jazeera used in 2022 (around 67–68 percent accuracy, seven of eight knockout games correct), outperform general-purpose chatbots because they are trained on structured match data rather than improvising from text.
