Football has always been a sport of passion and unpredictability. However, in recent years, data analytics and artificial intelligence (AI) have attempted to try to quantify the ‘beautiful game’. There’s tension here, because not only is it difficult to control the near-infinite variables in football, but the mere attempt at reducing the game down into numbers is often rejected by fans.
However, some have not rejected it; they’re using it to push new boundaries. Those who enjoy a wager and want to find the true odds that win them bets are beginning to use more complex data pipelines. Managers and analysts, too, are intrigued by learning more about the game. So, how has data impacted football, and what are its limitations?
The Data-Driven Revolution in Football Prediction
The football prediction landscape has been transformed by the influx of data. Teams now collect and analyse vast amounts of information, including:
- Player statistics (e.g., passes completed, shots on target, distance covered)
- Historical match data
- Team performance metrics
- Player tracking data
Modern football generates an astonishing amount of data. According to recent statistics, each match can produce up to 1.4 million data points, with 10 data points collected per second for every player on the pitch. This wealth of information allows for more nuanced and accurate predictions than ever before. And, this is trickled down to ordinary people who browse ClubSport for the best odds.
The evolution of data collection methods has been equally impressive. GPS trackers, acceleration sensors and even heart rate monitors are commonplace. This first transformed sports science, where they could limit overtraining. Very quickly, this led to a higher standard of football, because we are closer to squeezing out every ounce of what the human body is capable of.
Data has also played a role in directing a football club, though. Arsene Wenger, who took inspiration from Moneyball, was one of the first to overachieve on a tight budget. Data can help select undervalued players by comparing their prices to their performance metrics. With xG, we can now see what players and teams ought to have scored. This helps determine performance when removing the final score (and finishing) from the equation. In fact, this technique led to Jurgen Klopp’s hiring at Liverpool, who found his Dortmund team to have performed much better than his final league position suggested.
Harnessing AI and Machine Learning for Match Predictions
Artificial Intelligence and Machine Learning have quickly become relevant to football forecasting. These technologies can process and interpret vast amounts of data far more quickly and efficiently than human analysts.
Key algorithms and techniques used in football prediction include
- Neural networks
- Decision trees
- Ensemble methods
Graphical models can be used, but there’s still a huge distance that isn’t close to being covered regarding the sheer number of variables and randomness of the game. So, researchers instead began with penalties and then moved on to set pieces. Here, the variables are limited, and the outcomes can be discovered within seconds.
TacticsAI
One of the most exciting recent developments in this field is DeepMind’s TacticAI. Developed in collaboration with Liverpool FC, TacticAI focuses on analysing corner kicks using geometric deep learning.
TacticAI’s capabilities include:
- Predicting ball receivers and shot attempts
- Generating tactical variations
- Retrieving similar set-piece tactics from historical data
The system’s impact on coaching and tactical decision-making has already been eye-opening. In tests, the suggestions made by the model were preferred 90% of the time by human experts, compared to the tactical setups they pitched them against in practice. This really highlights the potential of AI to enhance human expertise in football strategy.
The practicality of this can run downstream. Lower league managers may soon consult with AI tactics advisers, which perhaps are sold as an SaaS. However, research is still a long way off from having a big influence on open gameplay.
Challenges and Limitations in Football Forecasting
Despite the advancements in data analytics and AI, football forecasting faces several challenges:
- The unpredictability of human factors (e.g., player motivation, team chemistry)
- Ethical considerations in data usage
- Balancing algorithmic predictions with expert knowledge and intuition
- The potential for over-reliance on technology at the expense of traditional scouting and coaching methods
In theory, there is no problem with data gathering. Many football matches are filmed, and it’s computer vision is sufficient enough to classify actions and behaviour accurately. For example, a successful tackle can be automatically detected with high accuracy.
When it comes to tactics, the distances and coordination between 11 players are possible to measure, but difficult to train and produce significant results. It isn’t even certain that there’s a correlation between the tactical AI assistant side of the research and the capacity to predict results. This is because AI assistants may be hugely helpful in just set pieces, but this doesn’t have a big influence on the outcome of a game. Vice versa, it may be possible to determine outcomes through metrics, but these do not necessarily inform strategy in a detailed enough way.
Final Word
AI has already begun painting pitches, but will it replace coaches? Football forecasting is becoming increasingly accurate through sophisticated data gathering and analysis. However, in the short term, they may have more to say for analysts and coaches than for bettors and those predicting results. What is for certain, though, is that it’s a key driver in increasing the standard of football that we watch; from optimising training schedules to organising set pieces.
