Germany Wins the World Cup with Big Data at Its Side In a stunning display of ta

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Germany Wins the World Cup with Big Data at Its Side

In a stunning display of talent, resilience, and teamwork, Germany won the 2014 World Cup, defeating Argentina 1–0. Argentina had Lionel Messi, the 2014 World Cup’s best player. Brazil, which the Germans defeated 7–1 in the semifinals, was considered the overwhelming favorite. Exactly how did Germany pull this off?

The German team sparkled with individual talent in every position and was praised for playing brilliantly as a team, but the winners had another behind-the-scenes advantage: Big data was at their side. The German team was able to use information technology to analyze massive amounts of data about teams’ performance and then use what it had learned to improve how it played. Each of the 32 competing 2014 World Cup teams had a dedicated video and performance analyst, but Germany appears to have been the only one that employed a special database and software to measure and analyze individual and team performance and strategies.

In 2012, the German Football Association collaborated with German software giant SAP AG to create a custom match analysis tool called Match Insights that collects and analyzes massive amounts of player performance data. Match Insights analyzes video data from on-field cameras that capture thousands of data points per second, including player speed and position. These data are organized and stored in an SAP database. Match Insights uses SAP HANA in-memory computing and analytic software to analyze vast quantities of such data in real time (see Chapter 6). Match Insights allows coaches to target performance metrics for specific players and give them feedback through their mobile devices.

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Data about the teams and players that would be competing for the World Cup, including every play they had run, were fed into the Match Insights database. The system assigns each German and opposing soccer player a unique identifier, so that their movements can be tracked digitally. Match Insights analyzes these data to measure key performance indicators, such as possession time (the percentage of time a team has the ball in a match); the number of touches controlling the ball; and movement speeds.

Improving speed was a major objective for the German team in 2014. Match Insights enabled the team to analyze statistics about average possession time and reduce it from 3.4 seconds to about 1.1 seconds. Better possession time enabled the German team to improve its aggressive, fast-paced style of playing that brought it to World Cup victory.

Match Insights can show the team virtual defensive shadows, indicating how much area a player can protect with his own body. This information helps the team visualize and exploit weak links in an opponent’s setup.

The German team also used Match Insights to evaluate the performance of its competitors. For example, the Germans were able to see before playing against the French that this team was very concentrated in the middle but left spaces on the flanks because their fullbacks did not push up properly. The German team also reviewed extensive data about Brazil’s preferred routes, its players’ reactions in pressure situations, and its players’ responses when fouled.

Match Insights was able to make its vast trove of performance data available to team members’ mobile phones or tablets. A mobile app sends short clips of analysis to individual players or groups of players. Right after a game, every player receives several visual examples of himself doing things well and poorly and may also receive visual data about the opposition. Some commentators have described Match Insights as Germany’s 12th man.

SAP is now offering Match Insights to other clubs and soccer federations, so Germany’s big data secret weapon will no longer be exclusive. It looks like international soccer teams will be playing on a level field again.

Sources: Andreas Schmitz, “Germany’s World Cup ‘Secret Weapon’ Goes Mainstream,” SAP News Center, www.news-sap.com/, January 23, 2015; Jack Rosenberger, “Germany’s Secret World Cup Weapon: Big Data,” CIO Insight, www.cioinsight.com/, July 18, 2014; Steven Norton, “Germany’s 12th Man at the World Cup: Big Data,” Wall Street Journal, July 10, 2014; and SAP News, “SAP and the German Football Association Turn Big Data into Smart Decisions to Improve Player Performance at the World Cup in Brazil,” June 11, 2014.

Soccer is one of a growing number of sports being transformed by big data, including baseball (think Moneyball), basketball, and tennis. Data analytics are just starting to be used in soccer, The chapter-opening case shows how advanced analytics helped the German team win the 2014 World Cup by providing very detailed information about individual player and team performance that could help it make better decisions about how to improve its game. The opening case has important lessons for other organizations and businesses as well, such as that you can be more efficient and competitive if, like the German World Cup team, you know how to use data to drive your decisions.

The chapter-opening diagram calls attention to important points this case and this chapter raise. World Cup Soccer is one of the globe’s most competitive and highly charged sports, and the German team was not favored to win the 2014 competition. However, it appears to be the first World Cup team to take advantage of new opportunities from information technology, including tools for capturing, storing, and analyzing big data regarding player and team performance. Earlier models of decision making that didn’t take advantage of leading-edge technology hamstrung other teams. The German World Cup team collected vast quantities of detailed statistical and visual data and could devise a better set of metrics for analyzing player and team performance. Match Insights helped World Cup managers, coaches, and players make more precise, fine-grained decisions on how best to play the game.

Here are some questions to think about:

How did using Match Insights change the way the German World Cup team made decisions?

Give examples of two decisions that were improved by using Match Insights.

What can businesses learn from the German 2014 World Cup victory?

Explanation / Answer

1. As mentioned in the case, SAP AG partnered with Germany football team during the world cup to analyses the match and performance data to create patterns and work on improving the weakness. The technology has evolved to such as extent that every movement, every split second action and reaction are captured and can be analyzed in real time. The team was able to analyze its own performances, identify the weak areas and improve on it. The data on others team also helped in identifying the opposition’s weakness and exploiting it. One such example is during the match between France and Germany. The Germans won the match 1-0 but the attacking talent of French was kept at bay because Germans were able to identify the patterns of French attacks and stop the momentum before it started. It was identified that the full backs stayed back most of the time and this helped in over loading the wings which enabled maximum attacks during the game play. The system also gave insights and short video clips of the mistake which were done in the previous matches which helped in rectifying the same for further matches.

2. One example of improvement using SAP match insights is how the team was able to reduce the ball possession before making a crucial pass or shot was reduced. It usually took 3.4 secs but it was identified that it was too long and could be significantly reduced. The team identified it and worked on reducing the ball possession before attacking swiftly. The team was able to reduce the possession to 1.1 secs. This enabled quick transition from defense to attack and helped in counter attacking the teams well. Another area which was improved how the players could exploit the weakness of opposing players. The tool had the ability to show the virtual defensive shadow of a player – which meant the maximum area the player can cover with his body. This enabled the players to exploit the areas which were unprotected by the opposing players and get passed them easily.

3. Business team can learn a lot from the manner in which sports is using data analytics. Right from Moneyball to Match Insights the data analytics team have been able to provide significant returns on the investments. Teams across the world have changed the way they work. Players with underlying numbers are being targeted which can result in significant return on the money with which they are brought. Also the team analysis and feedback using SAP systems are helping in continuous improvement of the players. In the business world lot of lessons can be learnt from this. Teams can analyze data and identify good areas of investments and strategic acquisitions. Teams can also identify the root cause of problems and make changes in small modules which can be monitored and scaled up. The analytical nature of business can help in ensuring the continuous improvement process takes place. Big Data and analysis can provide a method to look focused in to the wide pool of information within the organization.

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