AI vs. Bookmakers: What 104 Matches of the 2026 World Cup Revealed

Published: September 19, 2026. Analytical review of BET RATING.

Can a neural network predict football better than the betting market? For a player, this question sounds practical: is it enough to ask artificial intelligence to study lineups, injuries, and recent results to gain an advantage? The problem is that a detailed explanation does not yet prove the quality of the prediction. Even a convincing text with percentages and statistics can create more confidence than the underlying data deserves.

The reason for the analysis is a preprint comparing four AI assistants at the 2026 World Cup. We separate the published results of the experiment from their interpretation and explain what readers should really check in AI prediction services.

What exactly did the researchers check

In the work WC2026-Agents, published on arXiv on July 20, 2026, the authors describe an experiment on 104 matches. Claude, ChatGPT, Gemini, and Grok participated. The models provided probabilities of outcomes and selected virtual bets; after the matches, they analyzed the results. For comparison, probabilities calculated from bookmaker odds with the margin removed were used. This is research, not a BET RATING report on its own testing.

According to the authors, the models selected the same most likely outcome in about 92% of the matches. The betting market showed a better Brier score — a measure of the error of probabilistic predictions. However, the results of virtual bets varied:

Source: full text of the study, Tables 2–3. The conditions and amounts of virtual bets varied; these figures do not promise profitability.

Disclaimer: The abstract mentions the unprofitability of bets against the market for all models, however, Table 3 shows a positive ROI for five such bets by Grok. Therefore, this general thesis cannot be repeated without clarification. The work is presented as a preprint; results should be considered in light of the methodology and limitations.

Why a guessed outcome does not yet mean a profitable bet

Prediction and price assessment are two different tasks. The first answers the question of which outcome is more likely. The second assesses whether the offered odds correspond to the actual probability. A favorite may often win, but betting on them can still be unprofitable if the payout is too low relative to the risk.

Consider a hypothetical educational example. The probability of an event is 50%, and the decimal odds are 1.80. A bet of one hypothetical unit will yield a net profit of 0.80 units for a successful outcome, while an unsuccessful one will result in a loss of one unit. The expected result: 0.5 × 0.80 − 0.5 × 1 = −0.10. Half of the predictions may turn out to be correct, but the expected value remains negative.

This is not a recommendation for playing, but an explanation of why the percentage of successful predictions says little without odds. If a service shows only "hit rate," the reader does not see the most important part of the picture. Historical prices, calculation rules, and a complete list of results are also needed.

How to read accuracy and profitability metrics

Different metrics have different tasks. The proportion of guessed outcomes assesses how often the main choice matched the result. It does not distinguish between a cautious prediction with a probability of 51% and a confident assertion with a probability of 95%, if both predict the victory of one team. Meanwhile, the implications of such a difference for risk assessment are significant.

The Brier score takes into account the distribution of probabilities and the actual outcome: a lower value indicates a smaller mean squared error. Calibration answers a different question: how well the stated probabilities correspond to the frequency of events in a sufficiently large sample. If events estimated at 70% occur significantly less often, the forecaster's confidence requires reassessment.

ROI shows the ratio of net results to the total amount of bets. Profit in monetary units also depends on the overall turnover. Therefore, the largest win, the highest ROI, and the best probabilistic prediction may belong to different participants. Reducing them to a single ranking of the "smartest AI" without explaining the criteria is incorrect.

Why agreement among several neural networks does not guarantee results

Imagine that a reader asks the same question to three assistants and receives the same answer. This looks like independent confirmation. But to draw such a conclusion, one needs to know where each assistant got their information. If all read one news article, one review, or one line of odds, the answers may reflect a common source.

Different formulations also do not prove the independence of the analysis. One text may talk about a strong attack, another about the depth of the squad, and a third about home advantage. If the final probabilities are derived from the same data, the variety of explanations does not turn into additional proof.

For verification, it is more useful to ask questions about the origin of the information: when were the lineups updated, is the injury confirmed, is the primary source indicated, at what point do the odds refer to? A matching opinion without verifiable basis should not be perceived as a guarantee.

One tournament does not prove a sustainable advantage

A short period can yield an unusual result even for a system without a stable advantage. The outcome is influenced by randomness, the chosen competitions, the frequency of draws, and several matches with large odds. Success in one set of events does not mean it will be repeated in another tournament or the next season.

A serious test requires pre-established rules. It is necessary to determine which matches are included, when the prediction is published, where the odds come from, and how absences are accounted for. If these conditions change after the result, the comparison loses its meaning: successful decisions can be retained, while inconvenient ones can be excluded.

It is especially important to test the system on new events that were not used for its tuning. Constant adjustment to past matches can create beautiful statistics without practical value. Assessments of uncertainty are also useful: a difference in several successful outcomes does not always indicate real superiority.

What to check in AI prediction services

The model name and flashy interface do not replace a transparent history of performance. Before trusting the statistics of a service, it is worth checking several specific points.

• Publication time. The prediction should be recorded before the event starts, and changes should be visible in the history.
• Completeness of the archive. Losing and canceled results should be stored alongside successful ones.
• Odds. The source of the price and the time of its recording are needed, not just the final mark of "win."
• Type of outcome. A win in regular time, advancing further, and a win including extra time are different markets.
• Result calculation. Returns, transfers, cancellations, and expense accounting rules should be clear.
• Comparison base. It is important to understand what simple benchmark the model is compared to and why that particular one was chosen.
• Limitations. An honest service explains when the data is incomplete and why a confident conclusion is impossible.

A separate alarming signal is the promise of guaranteed profit. Technical terms like “neural network,” “algorithm,” or “big data” do not eliminate the uncertainty of a sporting event. If only screenshots of winnings are provided instead of a verifiable archive, it is impossible to assess the quality of the system.

Why It’s Important to Differentiate Betting Markets

In football, the phrase “the team will win” can be ambiguous. In a knockout match, a team can advance after a draw in regular time. For the viewer, this is a success for the team, but the outcome of a specific bet is determined by the conditions of the chosen market.

Therefore, the analytical material must clearly specify which outcome is being evaluated. Mixing results from 90 minutes with the outcome of extra time or a penalty shootout can distort statistics. This applies to both human predictions and automated systems.

This kind of verification may seem less impressive than comparing flashy model names, but it is what makes the calculation reproducible. Without a clear definition of the outcome, even a precise table can compare different things.

Why Virtual Results Differ from Real Experience

Research calculations usually fix specific conditions. In the actual operation of the service, a user may see a prediction later when the odds have already changed. Therefore, a result calculated at one price cannot be automatically transferred to another. Even a correct outcome does not eliminate this difference.

Expenses also matter. If the analytical service is paid, its cost reduces the user's financial result. Commissions and currency conversion should also be considered where they actually apply. Publishing a nice percentage without explaining the composition of expenses leaves the comparison incomplete.

Another question is the availability of the proposed conditions to the entire audience. The archive should allow understanding where and when the stated price existed. Otherwise, it cannot be verified whether the result was reproducible or merely theoretical.

This is not a reason to dismiss virtual experiments: they allow comparing approaches without monetary losses for participants. However, there remains a separate verification stage between the research table and the quality of the commercial product.

It is also important to distinguish between a prediction that the model truly made in advance and an explanation compiled after the match. The latter can be a useful analysis, but it is not proof of the ability to foresee the outcome. The date, version of the model, and immutable publication history are more important here than the attractive design of the report.

Where AI is Useful to the Sports Analytics Reader

A sensible role for an assistant is to help organize information. It can compile a list of questions for the review, explain the meaning of metrics, or structure information from provided sources. These tasks are useful in themselves and do not require a promise to predict the match result.

At the same time, factual statements need to be verified. The date of the match, team composition, active disqualifications, and tournament rules should rely on current publications. If there is no source, it is more accurate to note the uncertainty than to fill the gap with a confident statement.

Our editorial conclusion: AI predictions should be evaluated based on verifiable data and methodology, not on the persuasiveness of the text. Research of this kind is primarily useful because it allows for specific questions regarding the quality of the analysis.

Frequently Asked Questions

Can a neural network guarantee a win?

No. A probabilistic prediction allows for multiple outcomes. Even a correct assessment of probability does not turn a single event into a guaranteed result.

Why is a high percentage of correct predictions insufficient?

Because the financial result also depends on odds, amounts, and calculation rules. The percentage of correct outcomes describes only one aspect of the quality of the prediction.

Does a positive ROI prove that the service is reliable?

By itself—no. A complete archive, sufficient sample size, pre-defined methodology, and verification on new events are needed. It is important to exclude selective publication of successful results.

When was the research published?

The preprint was posted on July 20, 2026. This BET RATING material was published on September 19 as an analytical review, not as a report on the current release of the research.

Sources: preprint card WC2026-Agents; full text and result tables.

Read also: BET RATING rating methodology and principles of responsible gambling.

This material is for informational purposes only. Betting involves the risk of losing money and is not a source of guaranteed income. Participation is only possible in compliance with age restrictions and the laws of your country.