Coach talent – in addition to how he builds the game of his team – has always been in intuition: in the ability predict your opponent’s actionsto be one step ahead. Analysis and the so-called “coach’s instinct” have been receiving serious attention for several years now. technological support in the form of dataallowing you to find patterns and see what goes beyond the limits of human perception.
And now that the style of play of a football team can be deciphered more deeply and more accurately, a logical question arises: When will it be possible to predict in real time exactly what your opponent will do? This is one of the most actively developing areas of research in the field artificial intelligence and machine learning in sports.
Game patterns and context: from Elo to series
One of the most advanced studies of gaming patterns, published in 2025was dedicated tactical changesthat the team experiences throughout the season. As part of the work, we analyzed 3,652 matches five leading European leagues – Spain, England, Italy, Germany and France – using Elo systems.
Originally developed for chess, this technique has been adapted to analyze many sports. She evaluates each match not only by the result (win or loss), but also taking into account opponent’s strength. Thanks to this, it is possible to determine whether a club is in good or bad shape, even if its position in the table does not formally change.
If the team chose defensive tactical schemethe prediction accuracy reached 85 %. And taking into account the club’s position in the standings the attacking approach could be predicted in 89% of cases.
Next, data on the tactical alignment of each team was compared with its position in the table. In this way, the AI was trained to find patterns and answer a key question: will this team lean towards attacking or defensive play in this context?
Predictive models for predicting play style
The results of the study confirmed that teams go through game phases. The key factor is risk leveldirectly related to the current dynamics of results.
When a series goes well, the team plays more boldly and opens up. In times of failure, on the contrary, there is a tendency towards caution and protection.
Using machine learning methods (Autoencoder and K-means) and testing various predictive models, the researchers concluded that Gradient Boosting proved to be the most effective tool for predicting a team’s playing style even before the match starts.
In particular:
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when choosing a defensive model, the forecast accuracy was 85 %;
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When taking into account the tournament position, the attacking style was predicted with accuracy 89 %.
Context is more important than the tactics board
The main conclusion of the study confirms the thesis that is increasingly heard in the professional environment: When preparing for a match, context is more important than scheme. It is not enough to simply analyze the opponent’s tactical model – you need to understand Who is this team and where are they in their journey?.
It is important to take into account its trajectory: whether it is on the rise or, conversely, experiencing a decline. This is the only way to correctly adapt the game plan, based not on an abstract scheme, but on real state of the opponent here and now.
Game theory in football: sustainable solutions against adaptive opponents
In addition to methods borrowed from chess, more and more attention is being drawn to potential of game theory in real-time match analysis. It is a branch of mathematics and economics that studies how two or more participants make decisionsif the result of each of them depends on the actions of the others.
And here again the key becomes context expansionwithin which the data is analyzed. In this study, the goal was to understand which solutions remain sustainable against an opponent who constantly adapts to them. In football teams continuously neutralize each otherand with the help of this approach it becomes possible to identify strategic equilibriacapable of reducing tactical vulnerability in real time.
Predicting your opponent’s behavior
Development of the described predictive approaches technologically feasible. It has already been proven in various scientific fields that tactical positions and trajectories of players’ movements during matches are predictable.
Using algorithms computer visionanalyzing video recordings of football matches, a system was developed that extracts for each player three types of information:
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position on the field,
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movement trajectory,
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ball movement.
Based on these three groups of data, the spatio-temporal development of the game is converted into sequence of measured parameterswhich can be processed by mathematical and computational models.
A model was created on this basis recurrent neural network (RNN)trained on historical match data. She demonstrates high forecasting accuracy:
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about 85 % when assessing the tactical movements of players,
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and up to 90 % when detecting and classifying them.
These results show that predicting opponent behavior is no longer a futuristic idea, but real instrumentgradually changing the approach to tactical analysis of football matches.
TacticAI and corner prediction
There are other systems that focus on more specific game episodesfor example – corner kicks. One of these projects is TacticAI. It analyzes the positioning of players of both teams in the penalty area before a corner kick and predictswho is most likely to receive the ball and whether there will be a shot on goal.
The model was trained on spatio-temporal player tracking data by 7 176 cornerserved in matches Premier Leagueand its task is suggest optimal player formations in standard positions.
The tool was tested together with analysts and coaching staff “Liverpool”. They were shown various corner plays and asked to evaluate which formation looked tactically most justified. As a result in 90% of cases, experts considered the option proposed by TacticAI to be more successful.
Machine learning can’t eliminate football’s uncertainty, but it helps along the way identify more and more hidden patternswhich previously remained invisible in the game.
Is it possible to predict match results?
If the level of forecasting game episodes is already so high, a logical question arises: why not use AI to predict match outcomes? Similar studies have already been carried out, in particular in Canada, using the example of Premier League matches.
Using open season data 2019–2022scientists took into account:
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results statistics,
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indicators of player fatigue and efficiency,
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as well as external factors – for example, weather conditions.
The goal was to determine when the home team wins. The forecasts obtained turned out to be comparable with the estimates of bookmakers, but didn’t surpass them. The final conclusion of the researchers: machine learning suitable for predicting outcomeshowever, its effectiveness directly depends on quality and selection of variables.
In other words – it’s a matter of time. Already, there are many AI initiatives aimed at assessing the likelihood of outcomes by comparing historical and contextual data. Machine learning won’t eliminate the element of randomness in football, but it will allow reveal more and more internal dynamics of the gamepreviously hidden from analysis. And if such tools are already becoming the norm in a professional environment, then over time they will inevitably come to amateur football.
AI is already a competitive advantage
It’s important to note that artificial intelligence is already being effectively used today to predict the behavior of opponents. Of course, he does not make decisions for the coach, but he offers set of possible scenarioson the basis of which the strategy is built.
Analysis of historical data combined with incoming information in real timeis one of the most significant applications of AI in modern football. These tools allow adapt tactical decisions to changing match conditions and adjust your strategy as the game progresses.
In the meantime, according to reviews of all scientific literature in this area, there are limitations:
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lack of data standardization,
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difficulties with reproducibility of studies,
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opacity of some models,
what’s stopping them mass and universal implementation.
However, the direction has already been set – and the role of AI in football will only grow.
Photo: FC Barcelona ©
