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Tennis cheats: Is there an algorithm for corruption?

Like millions around the world, I’m glued to the Australia Open and captivated by the fitness and abilities I can only dream of. But the gloss of tennis has been tarnished for me ever since the joint Buzzfeed and BBC reports revealed suspected illegal betting in tennis matches and led them to claim that 15 players were involved in match fixing.

Investigative reporters analyzed thousands of tennis players and matches and found an algorithm for corruption: namely losses plus lopsided betting from syndicates in Russia, Italy, and Sicily equals possible match fixing.

There are experts who say the algorithm doesn’t indicate match fixing. But there are players who tell us otherwise. Novak Djokovic said he was offered $200,000 in 2007 to throw a first-round match in Russia. He refused the bribe and now has an estimated net worth of $90 million, proving that talent pays more than corruption.

Daniel Koellerer, who was banned for life in 2011 from professional tennis over alleged match fixing but denied the claims, went on camera for the BBC to explain how he was approached and how easy it would be to fix matches. 

Freakonomics writers Steven D Levitt and Stephen J Dubner made a book, movie, website and career using data to tell stories. The stories explained why drug dealers need to live at home with their mums (answer: they are poorly paid) and why estate agents are more likely to sell your house for a cheaper price than when selling their own homes. 

Raymond Fisman and Edward Miguel in their paper “Cultures of Corruption: Evidence from Diplomatic Parking Tickets” measured how diplomats used their immunity to avoid parking tickets. They found that diplomats from high corruption countries (based on TI’s Corruption Perceptions Index) had significantly higher parking violations than those from less high-risk countries.

There is no doubt data is a powerful thing. Fitness apps count your calories and steps, budget apps record your expenses (how much did you spend on coffee and alcohol this year?) and countless other free apps come at a price. Namely the ability to be able to use your data, track trends and sell it to insurance companies who will then build a profile of you (the story of you) and use it when calculating your annual premiums.

Data without a story is like tennis without a ball or diplomats without immunity. Levitt and Dubner presented data in a way that was not just easy to understand but also provoked interest, engagement and debate.  We can do precisely the same thing with the data we gather in compliance. 

As I talked about in a prior post for the FCPA Blog, information is only useful for story telling and teaching if it somehow comes to life — if it’s animated. My algorithm for compliance is simple: data plus analysis told in a story equals learning and understanding. Or in the words of Lewis Carroll “No, no! The adventures first, explanations take such a dreadful time.”

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Nicole Rose is CEO of Create Training. She’s a lawyer, trainer, writer and artist. Together with her team of animators and artists, Create Training has been making compliance training videos for learners around the world. It can customize training in any language and also has a collection of animated compliance training videos. Follow her on Twitter @createtraining2. Contact her here.

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1 Comment

  1. Perhaps not for corruption in sports but for business fraud such as Enron, Worldcom, Abengoa, I like to emphasize the usage of the OK-Score model, not only identifying any Business Failure beforehand but also identifying all major fraud-cases with its algorithm.

    In the mentioned website you may find all external compliance information about its usage,


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