Did Germany Cheat in World Cup 2014?

This is a guest post by Andy Thurai (@AndyThurai). Andy has held multiple technology and business development leadership roles with large enterprise companies including Intel, IBM, BMC, CSC, Netegrity and Nortel for the past 20+ years. He’s a strategic advisor to BigML.

Now that I got your attention about Germany’s unfair advantage in the World Cup, I want to talk about how they used analytics to their advantage to win the World Cup—in a legal way.

I know the first thing that comes to everyone’s mind talking about unfair advantage is either performance-enhancing drugs (baseball & cycling) or SpyCam (football, NFL kind). Being a Patriots fan, it hurts to even write about SpyCam, but there are ways a similar edge can be gained without recording the opposing coaches’ signals or play calling.

It looks like Germany did a similar thing, legally, and had a virtual 12^th man on the field all the time. For those who don’t follow football (the soccer kind) closely, it is played with 11 players on the field.

So much has been spoken about Big Data, Analytics and Machine Learning from the technology standpoint. But the World Cup provided us all with an outstanding use case on the application of those technologies.

SAP (a German company) collaborated with the German Football Association to create Match Insights analytics software, dubbed as the ultimate solution for football/soccer. This co-innovation project allows teams to not only analyze their own players, but also learn about their opponents as well to create a game plan. The goal of the co-innovation project between SAP and the coaches of the German national football team was to build an innovative solution that enhanced the on-field performance leading up to and winning the World Cup.

Considering that in 10 minutes, 10 players who touch the ball can produce as high as 7 million data points, imagine how many data points will be created in one game. When you analyze multiple games played by a specific opponent, your tape study and napkin cheat sheets won’t be that effective anymore.  By being able to effectively harness insights from this massive collection of data points, the German team had a leg up heading into the World Cup.

The proof was in the pudding when Germany used this program to thump Brazil 7-1 in the World Cup and then went on to triumph in the finals against Argentina. In their 7-1 win against Brazil, during a 3 minute stretch in the first half, 3 goals were scored while Brazil owned the ball for 52% of the time during that period. If you watched the game, Germany’s teamwork was apparent; but what was not apparent is that the software provided information on the possession time, when to pass, whom to hold the ball against vs. whom to pass against, etc. The Defensive Shadow analysis portion of the software shows teams exactly how to beat the opponent’s defensive setup based on a specific opponent alignment and movement.

In a recent article, Sophie Curtis of Telegraph explains a tactic used by the Germans, which was to reduce their average possession time from 3.4 seconds in the 2010 World Cup to 1.1 seconds in 2014. This not only confused the defenders, but also made the defenders uncertain whom to defend and quickly tired from randomly chasing their opponents.

If you watched the match closely, the passes and ball advancement all seemed to be executed with clinical precision. I thought they were just well coached or figured it out during the game, but apparently they had outside help which decided the win – before they ever set foot on the field. While the same advantage can be gained by watching tapes (or videos), the software’s uncanny prediction ability goes far beyond traditional mechanisms by converting all game data and player skills to actionable directives that can be implemented on the field. I don’t think you can make a more powerful statement than thumping a popular, soccer crazy host nation which fielded a decent football team. I doubt even the legendary Pele would have done any better against Big Data analytics.

The Match Insights tool is exclusive to the German team right now, but according to SAP they have plans to sell it more broadly in the future.  In the meanwhile, it is wise to choose Germany if you are betting with your friends.

Big Data is generally a massive collection of data points. Most companies that I deal with think just by collecting data, securing and storing it for later analysis, they are doing “Big Data.” However, they are missing the key element of creating real-time actionable intelligence so they can make decisions on the fly about their processes. Most companies either don’t realize this, or are not set up to do this. This is where companies like BigML can add a lot of value. BigML is a machine learning company (best of breed in my mind) which helps you do exactly that.

For example, in a recent blog post, Ravi Kankanala of Xtream IT Labs talks about how they were able to predict opening weekend box office revenues for a new movie using BigML’s predictive modeling. The point that caught my attention was that they made 200,000 predictions with 90%+ accuracy. This means their clients can look at these results and decide the best time to release their movie (day of the week, day of the month and the month) rather than treating it as a guessing game. This insight also helps studios segment and concentrate their marketing campaigns to improve box office results.

In a blog posted 3 days before the Super Bowl (on Jan 31, 2014), Andrew Shikiar used BigML to predict a Seattle win with an uncanny 76% accuracy. In the same article he also predicted Seattle covering the spread with 72% accuracy. But, if you were watching the pundits on ESPN, they were split 50-50% even an hour before the game with everyone of them yapping about the best offense ever going against a solid Seattle defense so the results are unpredictable.

While Moneyball, and Billy Beane, may have introduced the public to this concept of manipulating a sport based on statistical analysis, that approach was based on how overall statistical numbers can be applied to a team’s roster composition. But these precision statistics by BigML can help teams adjust every pitch (as in baseball), every throw (as in football), and every pass (as in soccer).

And the goodness doesn’t stop there. The BigML team is busy developing technology that underpins a growing number of predictive initiatives, including:

1. Predicting the symptoms of customers thinking of discontinuing subscription services and the best way to target them with exclusive campaigns, promotions, or incentives to retain them before their customers think of switching. – Churn rate analysis.
2. Predicting fraudster behavior amongst apparently normal customers.
3. Predicting future life events based on changing shopping patterns or suggest different shopping patterns based on life event/style changes.

The key takeaway here is that you can do more than just collect data and store it: with the right strategy and software you can get meaningful insights. If you gain an edge over your competitors, you can win your business World Cup too.

It sure pays to have an edge!