How Analytics helped Germany win the 2014 FIFA World Cup!

Who says analytics is only about facts, figures and insights? If you are smart enough, you can combine your career in analytics with unfulfilled aspirations and pleasure pursuits. In a nation gripped with a sports fever each time the World Cup happens, what other example could I give but analytics in sports?

Take for instance the recent 2014 FIFA World Cup where -time analytics was applied to this high-kick game of passion and fervor.  Are you aware that it was Big Data and Predictive Analytics that helped Germany score the World Cup?  No, I am not talking through the hat … just read on.

The football team of Germany is of course known for exceptional talent and great teamwork. But what you didn’t know was that pitted against some of the best world teams the German Football Association was in no mood to take chances. So they teamed up with SAP to make use of their SAP HANA technology based Sports Match Insights solutions for football.

“SAP Match Insights helped the team analyse matches by processing vast amount of data to help players improve their performance. Video data was captured from 8 on-field cameras and crunched into thousands of data points per second.  This enabled coaches to analyze performance metrics, such as player speed, position and possession time.  Coaches could then provide feedback to players via mobile devices to help them improve their game.”

Some areas where the Match Insights tool empowered the win:

Speed

The customised match analysis tool collected and analysed massive amounts of player performance data. The focus was on speed. The team was able to analyse stats about average possession time and cut it down from 3.4 seconds to about 1.1 seconds, he said.

Positioning

The Match Insights tool reveals virtual “defensive shadows” that help understand how much area a player can protect with his own body, or take advantage of the opponent’s weak links.

Training

The Match Insights tool allowed the German coach to identify and visualise the game vis-à-vis performance of individual players, which was send to teammates’ mobile devices. So if a coach wanted to adjust a particular player’s speed, position or possession time, stats and video clip from that day’s game would be sent to that player’s cellphone to look at  performance data.

Analysing competition

The tool enabled the German team to view and analyse the performance of its competition and make their moves accordingly. A lot of qualitative data for the opposition was made available. For instance, looking at the way Cristiano Ronaldo moves in the box,” and before the game against France, analyzing how “the French were very concentrated in the middle but left spaces on the flanks because their full-backs didn’t push up properly.” So the German team could target those areas.

Technological support

While an app developed in collaboration with SAP allowed the clipped analysis of live feeds to be sent to the player’s cell phones, Big data on players was accessed by the team. The German team also spend time reviewing  an extensive dataset that revealed how Brazil players reacted in pressure situations, their preferred routes and their response when fouled!

Video analytics and biometric data were leveraged using on-site digitization of the game. Three HD cameras were set-up in various locations at each arena that usedimage recognition to identify each of the 22 players, three referees, and the soccer ball. The system then tracked the XYZ coordinates of each of the above, to relay the visualisation “on a multi-screen, digital workstation where 74 people pour over the data on-site, aided by another 20 back in Italy”. Written algorithms helped calculate passing stats, ball possession, and other statistics. Such tracking information when relayed to a terminal, enables an operator to validate each action.

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The SAP DataViz team “pulled together game data from the qualifying stage and analysed the match-ups with SAP Lumira and SAP Predictive Analysis.”

Based purely on DATA such kinds of visualsiations, analysis, and infographics were presented during each stage of the tournament.

SAP “also used its Lumira and Predictive Analysis solutions to analyse data from all the Fifa World Cup teams in a bid to predict which countries would advance in the tournament. The key statistics used included goals, time of possession, participation in World Cups, and world rankings. Each individual match-up was compared and analysed on a team-by-team basis.”

India has also released the application, TCS SocialSoccer from Tata consultancy Services, that aggregates real-time analysis of all matches based on Twitter data, tracking sentiment and key talking points from commentators, footballers and fans from across the globe, and technology companies toying with the idea of introducing tracking devices in player shoes, we can hope to see some great real-time and spatial analytics in the arena of sports analytics!

So as a football or even cricket enthusiast, you can actually channelise your sports passion into a proprietary analytics application!

GOOOAAAALLLLL!!!!!

Football analytics

                         From The Economist’s World Cup Goal profile showing by minute, the time at which every goal from World Cup tournaments were scored.

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