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1 posts from December 2013

December 31, 2013

Missing Message of Moneyball

“It’s about getting things down to one number. Using the stats the way we read them, we’ll find value in players that no one else can see. People are overlooked for a variety of biased reasons and perceived flaws. Age, appearance, personality…mathematics cut[s] straight through that. Billy, of the 20,000 notable players for us to consider, I believe that there is a championship team of 25 people that we can afford, because everyone else in baseball undervalues them.” - Peter Brand (played by Jonah Hill) tells his boss Billy Beane (played by Brad Pitt) in the movie “Moneyball”

I was reading an article in Institutional Investor about Moneyball’s applicability for hedge funds by Daniel Nadler. In the article, Mr. Nadler makes the cogent case that big data adoption in sports is a prelude to big data adoption in investing. His article crystalized a view for me that there is a dichotomy in the message of Moneyball. The two sides are that Moneyball makes decisions simple and Moneyball makes decisions complex. The complex interpretation of Moneyball looks at the advancement of quantitative analytics in sports and points to Billy Beane as the spark that lit the fire. Most professional teams have an on staff stat PhD and use complex camera systems to measure new statistical variables. Basketball has expanded from field goal percentage to field goal percentage per spot on the floor. Baseball has evolved from a batting average to a batting average per pitch location. There are even ways to score defensive position based on cameras measuring distance from opposing players. While I completely agree with the premise that, without Billy Beane, sports would not be as stat driven as they are today, I also believe that Moneyball’s lasting impact is that it focus on making decisions simple.

The simple message of Moneyball is that Billy Beane distilled the decision process into basic elements and made better decisions. A decision maker must determine their objective, then determine the variables that most directly impact the objective, and create a process using the variables to make decisions. In baseball, the objective is to win games. Billy Beane learned that On-Base Percentage (OBP) was the variable that most directly impacted winning percentage and he created a repeatable process using OBP to price baseball players so that he could make better draft and player management decisions. I believe, without question, this is the most important impact of Moneyball (for more on why a simple model is so important, check out my previous post: The Beauty of Robyn Dawes).

Is there a metric like On Base Percentage for investing? Let’s start by determining the objective. In baseball, it is maximizing winning percentage. In investing, it is maximizing risk-adjusted return. Next step, who are the players on the investing baseball team? It is the stocks, bonds, real estate, commodities, etc. that we invest in. What is the variable for each investment that has the greatest bearing on my portfolio’s objective of maximizing risk-adjusted return? It is the Expected Return (i.e. how much I'm going to make if I'm right vs. how much I'll lose if I'm wrong). Of course there are other factors, but this is the one with the greatest influence. So step one in applying Moneyball to hedge funds is to calculate an Expected Return for every investment in your portfolio (upside * probability of upside + downside * probability of downside). This gives the firm the "one number" upon which all the rest of the investment process can build. Over time, factoring in the other variables that influence your objectives will make the decision process better and more repeatable. Like the natural evolution of Moneyball from simple to complex.