Is Artificial intelligence turning real football into a game like Football Manager?

Is Artificial intelligence turning real football into a game like Football Manager?
Generated using artificial intelligence. / Générée à partir d'une intelligence artificielle.

We sometimes talk about football intelligence. In the age of data, this is now being rivalled or complemented by artificial intelligence. Many teams use AI solutions to assess the performance of their players, determine which tactics to adopt and anticipate the movements of their opponents. This has both sporting and economic consequences…

For more than thirty years, football fans have been playing Football Manager (and its ancestors), a video game where you manage your professional football team, essentially on the basis of statistics, even though real football includes non-statistical data and being a good coach on Football Manager would probably be very different from being one in real life, where elements such as charisma and human relations come into play. But today, AI is bringing real football closer to Football Manager football.

The performance revolution

Artificial intelligence (AI) has completely changed the way performance and market value are evaluated in sports analytics, especially in football. As the French Olympic Games approach, it is becoming increasingly sophisticated. AI is revolutionizing performance evaluation by analyzing and interpreting massive amounts of data and providing new insights that might improve player growth, team tactics, and overall game results.

Historically, football clubs’ valuation relied on financial metrics such as revenue from ticket sales, merchandise, and broadcasting rights, when player performance has been evaluated using basic metrics such as goals, assists, and defensive actions. While these statistics are essential, they do not capture the full picture of a player’s or team’s contribution to the game, leading to inaccurate valuations and missed investment opportunities. Therefore, the complexity of football, with its fluid and dynamic nature, demands a more nuanced approach to performance analysis. The advent of advanced technologies, including AI and machine learning, has transformed this field. AI can process data from various sources, such as player tracking systems, video footage, and physiological metrics, providing a more comprehensive and objective assessment of performance.

A new way of understanding football

AI technologies enable the analysis of vast amounts of data with unprecedented accuracy and speed. This allows for a more nuanced understanding of both individual and team performances, considering variables that traditional methods might overlook. As an example, advanced AI models, such as the Complex Multiplex Passing Network (CMPN), differentiate various types of passes and interactions within a game. These models provide insights that traditional statistics might miss, such as the strategic importance of certain passes or the versatility of players. This detailed analysis contributes to more accurate club valuations. Moreover, machine learning models, such as multiple linear regression and random forest models, have been developed to predict player salaries based on performance metrics and characteristics. These models account for non-linear relationships between variables, offering a more accurate prediction compared to traditional methods.

One of the key areas where AI has made significant strides is in analyzing player movement and positioning. AI algorithms can track players’ movements on the field, assessing their positioning, speed, and decision-making. This data helps in understanding how players contribute to both offensive and defensive phases of the game, beyond traditional statistics.

Heatmap of player movements

Heatmaps like this one, generated using AI, illustrate the areas of the pitch where a player is most active, providing insights into their influence on the game.

4-4-2 or 3-5-2?

AI can dissect team tactics by analyzing patterns of play, formations, and transitions. By evaluating how teams maintain possession, press opponents, and exploit spaces, AI provides insights into the effectiveness of different strategies. Coaches can use this information to refine their tactics and adapt to opponents more effectively. Indeed, in a recent study of Team A’s matches, AI analysis revealed that switching from a 4-4-2 formation to a 3-5-2 formation increased their ball possession by 12% and created 20% more goal-scoring opportunities.

Predictive analytics powered by AI can forecast future performance based on historical data. By analyzing past matches, player fitness levels, and other variables, AI models can predict outcomes such as the likelihood of scoring, conceding goals, or the impact of certain player substitutions. This predictive capability is invaluable for strategic planning.

For instance, the DAxT model can predict the likelihood of different match outcomes based on historical data, current form, and various situational factors.

For players and fans

AI also plays an essential role for managing and preventing injuries. AI is able to detect early indicators of exhaustion or potential injury by keeping an eye on players’ physical state and workload. This preemptive strategy enables prompt treatments, lowering the possibility of injuries and guaranteeing that players are fit for important games. For instance, AI systems in elite teams have lowered injury rates by as much as 30% by giving players’ physical strain data in real time and recommending rest times or customized training regimens.

AI also enhances fan engagement by providing deeper insights into the game. Augmented reality (AR) and virtual reality (VR) applications powered by AI offer fans immersive experiences, such as interactive match analyses and virtual tours of stadiums. This enriches the viewing experience and fosters a deeper connection with the sport. AR and VR technologies allow fans to engage with the game in new and exciting ways, providing a more immersive experience.

To enhance Decision-making

During the Olympics or the UEFA Euro 2024, teams will rely on AI to optimize performance in real-time. By analyzing live data from matches, AI can offer actionable insights, such as suggesting tactical adjustments or identifying players who may need rest or substitution. As a matter of fact, during a critical match, AI can detect a drop in a key striker’s sprint speed, prompting the coach to substitute him before fatigue led to a potential injury through AI based solution like those provided by. Statsports company is used by several premier league teams like Arsenal and Liverpool.

Therefore, private equity firms investing in football clubs benefit significantly from AI’s capabilities. AI can analyze historical data, predict future performance, and identify potential investment opportunities. This leads to more informed decision-making, optimizing returns while mitigating risks. Indeed, there is a notable correlation between football clubs’ performance and their stock prices.

Football clubs’ performance and stock prices

For instance, wins can significantly boost stock prices, reflecting the direct impact of match outcomes on financial valuations. Borussia Dortmund FC for instance saw it’s stock price rising from 2.80 euros to 4,50 euros per share after their successful Semi-final in 2012-2013 UEFA Champions league. AI models can analyze such correlations, providing insights that help investors make better-informed decisions. AI also enables the analysis of social media sentiment, which can predict stock price movements. For example, positive sentiment on platforms like X or Instagram can lead to stock price increases, while negative sentiment can cause declines just as the stock price of Manchester united dropped in 2021 after the announcement of forming a European super league. Hashtags like #NoToEuropeanSuperLeague and #GlazersOut trended worldwide.

As the negative sentiment grew, particularly the threat of fan boycott and loss of sponsorships, and due to the sudden drop of the stock price, the club started withdrawing from the ESL in response to the backlash. Integrating social media analytics with traditional data sources provides a more comprehensive understanding of stock price dynamics.

All in all, the integration of AI in football valuation offers a competitive edge. AI not only enhances the accuracy of valuations but also provides deeper insights into performance metrics and market dynamics. By embracing AI-driven analysis, football continues to evolve, ensuring that every aspect of the game is meticulously evaluated and enhanced. The future of football performance valuation is here, and it is intelligent, data-driven, and remarkably precise.

Shirine DekimecheStudent of the Programme Grande Ecole and the Msc Corporate Financial Management at SKEMA Business School.

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Iness LhamidiStudent of the Programme Grande Ecole and the Msc Corporate Financial Management at SKEMA Business School.

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Sabrina ChikhSabrina Chikh, Professor of Finance, SKEMA Business School

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