Data analysis at the European Championship and World Cup

The use of data analytics in football, particularly at major tournaments such as the UEFA European Championship (EURO) and the FIFA World Cup (WC), has evolved dramatically over the years. The history of this development, and in particular the practice of manual tagging, offers fascinating insights into the evolution of sports technologies and strategies. This blog post provides an overview of the history of data usage at football tournaments and highlights the changes that have taken place over the years.

1990s Data collection from football matches

Comparisons and revolutions in football tournament data collection

The evolution of data collection and analysis at major football tournaments such as World Cups and European Championships reflects technological advances and the growing importance of data science in sport. Here's a look at the key changes and comparisons between different eras.

1980s: The beginnings

In the 1980s, data analysis methods in football were still in their infancy. Most of the data was collected manually and the concept of systematic analysis had barely been developed.

  • Manual recordings: Analysts noted events such as shots on goal or fouls on paper.
  • Limited statistical depth: The data collected was mostly limited to simple metrics such as goals, shots and fouls.
  • No real-time analysis: The data was often only used after the game for debriefing.

1990s: The beginning of systematic analysis

With the advent of the first computer programmes, teams began to use more systematic forms of data collection.

  • First software tools: Programmes for data collection and analysis are introduced, even if many processes are still manual.
  • Beginning of video technology: Games were recorded and analysed manually, which enabled subsequent but more detailed analysis.

2000s: The digital revolution

The 2000s brought a major technological leap with the introduction of automated systems and advanced analysis tools.

Introduction of Prozone: This tool enabled more detailed performance analysis through automated tracking systems.

  • GPS and RFID technologies: Player movements and positions could now be tracked accurately and in real time.
  • Analytical reports: Data was not only collected but also thoroughly analysed to improve tactics and performance.

2010s: Big data and AI

The use of big data and artificial intelligence has further revolutionised data collection and analysis over the last decade.

  • Machine learning: Algorithms can now recognise patterns and strategies that are too complex for human analysts.
  • Real-time feedback: Tools such as SAP Sports One enable coaches to make decisions based on live data.
  • Detailed player profiles: Each player can be analysed using a variety of physical and tactical metrics.

2020s: Future prospects

Current and future developments point to further automation and refinement of data.

  • Augmented reality and virtual reality: New technologies allow coaches and players to approach match analysis in innovative ways.
  • Integration of wearables: Players are increasingly wearing sensors that provide comprehensive health and performance data in real time.
  • Global data pools: The collection and analysis of data is becoming increasingly international and inclusive, further enhancing scouting networks and player preparation.

The history of data analysis in football shows a clear evolution from manual, paper-based methods to an era of digitalisation, automation and intelligent data management. Each decade has seen innovations that have not only changed the way games are analysed and understood, but have also had a direct impact on improving the performance of teams and players. The future promises to continue this revolution with even smarter and more connected systems that have the potential to transform football in unimaginable ways.

Modern form of data collection at football matches

Examples of success through data

Germany in the 1970s

Germany, particularly during the 1974 World Cup on home soil, utilised advanced statistical recording to improve the team's performance. Under the leadership of Helmut Schön and later Franz Beckenbauer, the DFB began to keep more detailed records of match behaviour, including players' running routes and the distribution of passes. These manual analyses helped to develop match strategies aimed at exploiting opponents' weaknesses.

Italy in the 1980s

Italy, who won the 1982 World Cup, also relied on traditional methods of data collection to gain deeper insights into their players' performances. The Italian Football Federation used match reports and manual statistics to analyse the fitness and tactical behaviour of its players. This data was used to strengthen the defensive tactic known as "catenaccio", a strategy that relies heavily on solid defence.

Spain (Euro 2008, World Cup 2010, Euro 2012)

Spain's dominance, winning the 2008 European Championship, the 2010 World Cup and the 2012 European Championship, was in part the result of sophisticated data analysis.Spain used data to perfect the famous tiki-taka system of play, based on precise passing, possession and utilisation of space. Analysing player patterns and evaluating opponents' strategies allowed coaches to maximise the effectiveness of their system of play.

Germany (World Cup 2014)

The success of the German national team at the 2014 World Cup in Brazil is a prime example of the effective use of data analytics, with the German Football Association (DFB) working closely with SAP to develop a special analytics tool that could be used to analyse player performance in detail and develop optimal match tactics, helping the German team to better analyse their opponents and adapt their own game strategy. The convincing victory against Brazil in the semi-final and the triumph against Argentina in the final showed how data analysis can help to optimise performance and tactics.

Success through data in football

The role of data analysts at FIFA and UEFA

Major football organisations such as FIFA and UEFA recognised the potential of data analysis early on and invested in personnel accordingly. At major tournaments such as the World Cup and European Championships, more than 100 people were often employed per match to collect and record all relevant data. These teams of data analysts and statisticians were tasked with tracking every action on the pitch and manually entering it into specialised systems.

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The logistics behind manual tagging

At major football tournaments, FIFA and UEFA utilised impressive logistics to ensure the quality and quantity of data collected. 

Each team of analysts was responsible for specific aspects such as player actions (shots, passes, tackles), match events (goals, cards, substitutions) and positions on the pitch.

Manual tagging, especially in an environment as dynamic and fast-paced as a football match, presents significant challenges:

  • Accuracy: the accuracy of the data depended heavily on the attention, size and experience of the team of analysts.
  • Consistency: With so many people involved, standards and training had to be strictly managed to ensure consistency in data collection.
  • Real-time processing: Data had to be captured and processed quickly enough to provide useful information to coaches and teams while the game was still in progress.
The mass of analysts it took to manually collect data from a game

Present and future

Today, data analysis and tagging in football is highly technical and automated. The use of AI and machine learning makes it possible to recognise complex patterns and strategies that human observers may miss. Recent tournaments, such as the 2020 European Championship and the 2022 World Cup, have utilised advanced tracking systems and analytics tools to provide detailed insights into almost every aspect of the game.

Artificial intelligence means that significantly fewer people are needed to collect data


The history of data collection and tagging in football clearly shows how technological advancements have changed the way we understand and analyse games. From the days of manual tagging to modern real-time analysis, data science has had a profound impact on football. While major tournaments such as the European Championship and the World Cup continue to serve as a stage for the latest innovations, it remains exciting to see what developments the future will bring.

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