How Dwayne Wade Got Screwed By LeBron James and The Future of The US Economy

Up until 2010, Dwyane Wade was the star of the Miami Heat. No, actually, Dwyane Wade was the Miami Heat. In 2006 he led his team to the NBA championship and won the NBA Finals Most Valuable Player award. And he didn’t stop there. In the following years he only got better, scoring more than 30 points per game in 2009, winning the NBA scoring title and becoming the NBA All-Star Game MVP in 2010. Wade was the hottest player in the league, and was paid accordingly, almost 16 million dollars per season.

But then, something terrible happened – the Miami Heat decided to recruit talent from another place and signed LeBron James. Suddenly everything has changed. Wade was no longer the star of his team. The number of his shots dropped, his assists were down by 30% and his salary was down by almost two million dollars from the year before. Basically, Dwyane Wade got screwed, but this is not the full story.

With James’s help, the Heat won more games, reached the NBA finals on their first year together and won the NBA championship a year later. They have become the most exciting team in the league, attracting many more viewers to their games and getting a lot more money through a new television deal. The entire team has enjoyed its new place in the spotlight. Dwyane Wade was no exception. His renewed stardom has landed him a 6 year contract worth over 100 million dollars, and one of the largest in history shoe endorsement campaigns with the Chinese athletic company, Li Ning.

The history of sports is full of organizations which transformed themselves and became successful after acquiring brilliant new talent. This is not limited to just sports teams. American companies have been attracting the best and the brightest employees from all over the world for decades, but the recent surge in unemployment has caused people to argue against this policy. While this arguments sounds reasonable, it is missing the big picture.

Over the last decades, hundreds of American companies were founded, led or contributed to by immigrants from all over the world. They were not born in the US, but still have made a significant contribution to the US economy. According to Forbes, 40% of the Fortune 500 companies were founded by either immigrants or their children and 75% of the companies who received venture capital funding had at least one foreign born core member. Google, PayPal and eBay all had immigrants as co-founders and currently employ large number of foreigners. Besides creating thousands of jobs for the people they currently employ, they have also created ecosystems that give jobs to millions of others. Great talent creates great opportunities that everyone can benefit from.

Without James, Wade would still be the star of his team. He would have scored the most points, have the highest exposure, and the highest salary. But the Heat would not be the most popular team in the league and its players would go mostly unnoticed. Success is achieved by increasing the size of the pie, not by preventing pieces from others.

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Bitcoin’s Winner’s Curse – What Auction Theory Teaches Us About Bubbles

I encountered bitcoin for the first time when I was researching anonymous web commerce. The exchange rate at the time, almost $10 for a single bitcoin, seemed as an awfully high premium for anonymity. I was dead wrong. When the total value of bitcoins in circulation rose above billion dollars, a media frenzy started and the increasing interest drove prices through the roof, reaching $266 at the peak.

For those unfamiliar with bitcoin, it is a new type of currency with the following differences from regular currencies:

  • Anonymous – customers of online contraband drugs or pornography – few of bitcoin’s main use cases – prefer anonymity. Unlike credit cards, bitcoins are not attached to a name of a person but rather to a large set of different computer generated ids.
  • Decentralized – like any commodity, the value of currency is driven by supply and demand. When a government increases the supply of a currency (for example, by printing more notes), it depreciates its value. People who do not trust their governments to handle fiscal responsibilities properly are always looking for alternatives to depreciating currencies. Since bitcoin is not regulated by a central bank, and its algorithm ensures that there will never be more than 21 million coins in circulation, bitcoins should hold their value better than centralized currencies.
  • Virtual – when the Cypriot government decided to tax the domestic bank accounts of all its citizens by almost 10%, people all over the world started looking for safe alternatives. Bitcoin’s virtual nature and the fact that there is no central repository that holds them, makes them nearly impossible to confiscate or tax by governments. This is similar to why peer-to-peer file sharing networks like BitTorrent are hard to shut down.

While bitcoin was created as an alternative for common currency, it is hardly used in online commerce. According to the bitcoin official website, less than 0.3% of bitcoins are used in trade. When the value of a currency constantly increases, the amount of bitcoins an item costs constantly goes down, stagnating trade in the process. But why the value of bitcoins increases?

People love get-rich-quick schemes. There is nothing better than to be the person who succeeded to do that and the center of everyone’s conversation. Since both the financial and the emotional rewards are high, the demand for such opportunities is high as well. According to auction theory, excess demand for an item of limited supply, leads to the overvaluation of those items. This phenomenon is known as the “Winner’s Curse”.

The winner’s curse states that in common value auctions (auctions where the value of the item is identical to all bidders) with incomplete information (auctions where people do not know the real value of the item), the winner of the auction will tend to overpay. Think about an auction of a jar of coins, the winner of this auction will be the one that thought that the jar contains more money than anyone else in the auction. The probability that the winner is the only one who is right and everyone else underestimated the value of the item is very low, which leads to overpayment. The higher the demand for an item, the higher the error. Bitcoin is limited in supply by design. The fact that most of the bitcoins are hoarded and not available to buy, lowers supply even further, leading to exorbitant prices.

The value of bitcoin can double or halve within a week so it cannot be used as a currency, and since it has no intrinsic value, it is no good as an investment. Historical trend driven valuations such as: the 17th century tulips, early millennia dot-com companies and recent real estate, have all eventually crashed. The catalysts for bitcoin could range anywhere between the lose of public interest in the coin to the rise of a competitor which will reduce the scarcity of virtual coins, the main driver behind bitcoin’s price.

Bitcoin is a fascinating achievement in both cryptography and new economic thinking that solves a series of real problems. Its only fault – it was cursed with success.

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The Unreasonable Effectiveness of Data Scientists

My romance with data science began when someone recommended the book “Moneyball” by Michael Lewis. If you haven’t read it, please do. At the very least, watch the movie. Moneyball is the story about the transformation of the Oakland A’s baseball team from being one of the worst teams in baseball to a team that set the american league record of 20 wins in a row. One of the main reasons for this transformation is their reliance on statistics instead of general gut feeling and domain expertise, the way baseball was always managed. To understand how amazing this transformation is, let’s look at baseball numbers. The average salary of a team is about 90 million dollars a year, which is roughly the average number of wins in a season. This means that each win costs a baseball team about a million dollars. I don’t know how much the A’s paid Paul DePodesta, their data scientist, but I’m sure he was very well worth it. Of course most data scientists do not work for sports teams, but the stakes are even higher with internet companies considering current valuations. Companies like Google, Facebook and my current employer, LinkedIn, have really grown immensely and become Fortune 500 companies with 10 times fewer employees than other companies on that list. There are a multitude of reasons for these companies’ success, but their proficiency in handling data is surely one of them.

In 2009, Google released a paper titled The Unreasonable Effectiveness of Data and though I agree with most of it, I would argue that the effectiveness comes first and foremost from the data scientists themselves. Most people see the role of the data scientist as one who takes a problem, gathers some data, applies some machine learning algorithm and gets results in a form of a chart. Let’s look at this process more closely.

Problems – More important than solving a data problem, is finding the right data problem to solve. The right problem is one that can move the needle on a metric important to your business. However, most of the time, this is not a data problem. Great data scientists use data to find a chain of causes and effects that leads them to a solution of a data problem that also solves the business problem. For example,

  1. Netflix wants to retain customers after their trial period – problem
  2. Customers who watch more movies are more likely to sign up – cause/effect #1
  3. Customers who discover good movies will watch more movies – cause/effect #2
  4. Build a system to recommend movies to customers – solution

At first glance it doesn’t seem that building a recommender system for movies helps Netflix to retain more customers. Using this technique, not only helps to solve the problem, it also provides valuable data and insights to the rest of the company and significantly lowers the business risk of the project.

Data – Without context, data is just bytes on your hard drive. In the age of big data, people tend to measure companies by the amount of data they store, this is no more reasonable than measuring software by the number of lines in it. Not all data is created equal. A good data scientist knows the value of each data set in her possession, a great one will also know the value of those which are not and how to get them. One of the best examples of this principle can be found on the Google Image Search project. The people who worked on this project realized that getting more labels on the images they have will yield better results than improving their machine learning algorithms. In 2006, Google released a game where two players would receive the same image and their goal was to describe the image they were seeing. If both players used the same word, they would get points, and Google would get an invaluable piece of information about this image. The conception of the game did not involve fancy PhD level statistics, but a very clever sense about general problem solving.

Results – This is what truly matters. Michael Lewis did not write the book about the excellent analysis performed by Paul DePodesta on his computer to prove that the way scouts analyze players is wrong. In fact, Sabermetrics, the field of baseball analysis that Paul DePodesta based his analysis on, has existed since 1964, almost 40 years before Billy Beane, the A’s general manager, implemented it. The legend was born only after the analysis led to something that mattered, a record for most consecutive wins and got into the playoffs despite losing their three best players at the beginning of the season. Same goes for data scientists, the work does not end once the analysis has been completed. In fact, it just begins. Great data scientists know the difference between theory and practice and will follow through with their ideas to see them through to completion.

In summary, data science is a great tool to have in a company’s toolbelt and can have a disproportionate impact on its achievements. Great data scientists understand the business needs of a company, use data to find the best solution and make this solution a reality.

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