Can AI Predict Football Upsets? What the 2026/27 Champions League Has Revealed
Artificial intelligence has already changed how football analysts approach match predictions. AI systems are used to analyze thousands of data points, compare team performances, and estimate the probability of different results. However, it seems that AI still struggles with predicting upsets.
The 2026/27 UEFA Champions League has already provided several interesting examples. For instance, Liverpool’s defeat to Fenerbahçe, Como’s extraordinary victory over Lens, and Real Madrid’s narrow escape against AEK Athens all came as a surprise both to fans and to AI.
It has led many to question whether AI can even predict whether an underdog has a chance to win.
The 2026/27 Champions League Has Already Delivered Surprises
The fourth match day of the Champions League phase, played on November 3 and 4, featured several upsets that few saw coming. Liverpool’s 3-2 defeat to Fenerbahçe was particularly interesting. The English team won its first three games, and according to the crypto sports betting sites, it was seen as a favorite in Turkey.
Sites such as these allow players to place their wagers quickly, and the odds are based on numerous data points. According to CryptoManiaks, the crypto betting industry is on the rise in areas that otherwise limit betting options.
However, Fenerbahçe recovered from falling behind twice before Vedat Muriqi scored the winning goal in the 79th minute. The match showed how important home advantage can be.
Como produced an even more remarkable result, beating Lens 9-1 away from home. The Italian side scored six times after the interval, matching the biggest winning margin in Champions League history.
Real Madrid managed to win 3-2 against AEK Athens, but it struggled to do so more than anyone expected, securing the win in stoppage time.
How AI Models Identify Potential Upsets
AI uses several variables to estimate how likely the team is to win, lose, or for a match to end in a draw. The most important of these is the recent form. Models examine results, goals scored and conceded, as well as performances against opponents with comparable strengths.
Underlying statistics are often more important than final scores. For instance, expected goals (xG) is a measure of the quality of scoring opportunities. A team losing several matches despite consistently creating better chances might be stronger than its recent results suggest. It may mean there’s potential for an upset.
Important variables also include Elo ratings, squad strength, home advantage, player availability, and recovery time between matches. AI has been using travel distance as an indicator of performance, especially when it comes to matches in Europe.
Why AI Still Struggles to Predict Football Upsets
AI can’t account for the complexities of a football match, even though it’s capable of analyzing data. Models rely on historical information and past performance, and in a real upset, the result goes against that data. It can also be caused by injuries, unexpected mistakes, or a sudden tactical adjustment.
Football has much fewer scoring opportunities compared to other popular sports, and therefore each scoring opportunity matters. A deflected shot, controversial penalty, or goalkeeping error can determine the outcome, even when one team has controlled most of the match.
It’s also important to keep in mind that historic performance isn’t always a good indicator of how a match will turn out. Teams can change managers, introduce new tactical systems, or improve significantly during a season.
There’s also a matter of luck, which can’t be turned into data and can sometimes determine the outcome.
Can AI Become a Useful Tool for Football Predictions?
AI is best used to estimate probabilities, which is what bettors are looking for, but not to choose the outcome of matches. It shouldn’t be used to identify the stronger team, but to investigate whether an underdog has a realistic opportunity to win. The decision on how to wager remains one for bettors to make.
For example, Liverpool’s defeat to Fenerbahçe could encourage analysts to examine how home advantage, recent performances and defensive vulnerabilities influence future predictions. Liverpool’s next match is against Club Brugge on November 25, and it will be a great opportunity to test how the new data will affect AI predictions.
AI is therefore best used as a tool to improve statistical models, while human analysis remains key for success. AI can identify unusual patterns in performance data, while experienced analysts can consider tactical changes and circumstances that might not be fully represented in historical statistics.
Conclusion: AI Can Spot the Warning Signs, Not Guarantee the Result
The 2026/27 Champions League proved that surprises are always possible in football and that there are limits in how AI can be used to predict outcomes of matches. Machine learning can identify weaknesses in favorites, recognize improving underdogs, and calculate probabilities that challenge conventional expectations. However, some matches remain hard to predict.