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Match Insights is the software that was used to collect and analyze big amounts of data collected from performance of players. These involved collecting visualized data from on field cameras that would capture massive amounts of data over thousands of data points per seconds, including the physics the motion and position the players followed. The players were then trained according to the patterns and methodical tactics that were developed from the analysis of this big data and therefore, the players were trained with the help of information technology that was sampled over hundreds of thousands of hours worth of data. This would become integral to why the German team from 2014 ended up winning the World Cup. The time for big data analysis to go to the next level in sports was on.

Two decisions that were improved by using Match Insights can be:

1. The position and locations in the formations of the players.

In football, it is absolutely critical where players are positioned for both defense and offense. And that being the case, the players were told manually up until that point where they should be positioned and what would be the best approach to attack or defend. After the introduction of Match Insights and big data analysis, the positions of the players were decided by the coach with the help of the big data analysis. And it was proven that massive data analysis had led to an objectively better positioning strategy that helped the Germans play objectively better.

2. Possession time was analysed by Match Insights.

With average possession of the ball being a very important part of the game, the software enabled the team to analyse statistics about the average possession time and helped decrease it from 3.4 seconds to about a little more than a second. Better possession time helped the German team to further improve on their aggressive style of playing that ultimately brought them victory.

3. The idea of virtual “defensive shadows”

This referred to the fact that players could protect a certain amount of area with their own bodies from the offence who would constantly try to take the ball away. This visual data would help the German team understand the opposition better and make the best use of weak links in their setup.

4. Analyzing data collected on other teams (potential opposition)

The German side managed to make use of data on other teams and by doing this, the Match Insights system was used to analyze the performance of the competitors. Example: The French side was thought to be very concentrated in the middle but the fact that they left spaces on the flanks was deduced by the system. Also, the way the Brazilian team reacted in situations that involved pressure and how they responded when they were on the receiving end of fouls led to even more exploitation of weaknesses on the Brazilian side.

Businesses today and in the future can take advantage of the fact that a multi-billion dollar industry like the world of football has recently started to integrate data analysis and data science into their game. This will eventually and inevitably mean that businesses will be given incentive more and more to give employment opportunities to those who have a background relating to data, software and analysis. Needless to say that a quality analysis team and software in today’s day and age is profitable in general, but trying to get into the industry where football and technology to create football strategies will inevitably be a much bigger industry in the near future.

By: A Computer Science student and football enthusiast

A computer science student with interest in data and analytics. Hobbies include illustration, writing and music.