The Moneyball Lessons

I love numbers.

I first realized this around the age of ten, when I started collecting baseball cards pretty seriously.  On the back of each card was a bunch of numbers, each player’s ‘stats’, important metrics of how well he played.  Hits, batting average, runs batted in (RBIs), runs scored, and so on.Elston Howard

It later developed that much of what we took as gospel was wrong.  Not wrong, really — it’s just that there was a more productive way of looking at the game.  I’m speaking of sabermetrics, ‘the empirical analysis of baseball’ popularized in the book and movie Moneyball.

The main implication of sabermetrics was that the numbers that everybody knew and had been following were not always the best for predicting the ultimate value goal:  winning games.  Winning is the means by which professional ball teams (which are businesses) fill stadiums and make money.  By correlating each player’s performance with how that player contributed to that value goal, a much better set of metrics was developed.

The team that first operationalized these insights (the early-2000s Oakland Athletics) was able to put together a winning team on a player budget representing a fraction of  that of, say, the NY Yankees.  Once their story was written, other teams  started using the same system, and the comparative competitive advantage eroded.

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    COMPETING IN THE KNOWLEDGE ECONOMY is written by Timothy Powell, an independent researcher and consultant in knowledge strategy. Tim is president of The Knowledge Agency® (TKA) and serves on the faculty of Columbia University's Information and Knowledge Strategy (IKNS) graduate program.

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