I am going to go out on a limb and predict that the 2010 NBA champions will be the Houston Rockets. I don’t do this on the basis of any psychic ability. Nor do I do it out of loyalty for the Rockets–I’m a fan of the Minnesota Timberwolves. Instead it is because the Rockets management team is subjecting its entire roster to the power of quantitative analysis.
Quantitative analysis in professional sports is nothing new. It was the subject of Michael Lewis’s best-selling book, Moneyball, and has been cited by baseball experts as the reason why the Oakland A’s, in spite of having one of the lowest payrolls in professional baseball, are consistently among the league’s better teams.
Quantitative analysis is even credited with helping the 2004 Boston Red Sox break the Curse of the Bambino and end its eighty-six-year-old quest to win the World Series. Both teams’ general managers, Oakland’s Billy Beane and Boston’s Theo Epstein, admit to regularly using quantitative analysis to determine everything from how a trade for a particular player will impact the team’s on-field performance to where a certain player should be inserted in the batting rotation on any given day.
Translating baseball’s more linear nature–where it is relatively straightforward to isolate a player’s individual performance by discerning the difference between, say, a single and a home run–is far easier than figuring out the relative value of a basketball player. This is due to the complex ways in which a basketball team’s five players interact with one another while on the court. For instance, is a basket more valuable than the assist that made it possible? What about the value of a rebound as opposed to a blocked shot? And which has more impact on a game’s outcome, a player’s ability to steal two passes a game or his skill in consistently setting good picks?
These have been vexing questions, but economists have now developed an algorithm to help measure a player’s “wins produced” for his team, and the Rocket’s general-manager, Carroll Dawson, has an MBA from MIT’s Sloan School of Management and is applying these algorithms to select the players he believes will best help his team win.
To this end, one of the reasons the Rockets signed former Duke standout Shane Battier had little to do with his 10.1 points per game average or his high shooting percentage (.488); it had more to do with his rebounds per game, his dramatic defensive ability, and his skill at quickly moving the ball around to his open teammates.
Time will tell if my prediction about the Houston Rockets will pan out, but the net effect of this emphasis on algorithms is that it is helping a number of businesses make better decisions today. For instance, Shell Oil is using complicated mathematical algorithms to help determine where to drill for oil, and it likely played a leading role in assisting Chevron scientists locate that company’s new massive Jack2 oil discovery in the Gulf of Mexico. And with Google Trends, businesses of all sizes can analyze and better understand how, where, and by whom its products are being used.
Exponential Insight
Whether you’re looking to improve your on-base percentage, take your game to a new level, or just find a new discovery, crunching the numbers can yield some surprising findings.
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