Mean Reversion, Small Sample Size and the Mets

Mets1Nobody saw it coming before the season and even well into the season (see, e.g., here, here and here). But, as of tonight, the New York Mets will be playing in the World Series for the first time in 15 years. Sports is so entertaining in large part because the combination of luck, skill and variation involved creates an unpredictable stew of drama, intrigue, greatness and disappointment. Today, that stew smells a lot like the Mets and, so far, it smells pretty tasty.

While their pitching has been sublime this postseason and is the team’s overall key to success, Met second baseman Daniel Murphy’s bat is the big story.

Prior to this postseason, Murphy was a good-but-not-great player whose career highlight was being a reserve All-Star selection in 2014. But this postseason has been one for the ages and the record books to this point thanks to his having homered in six straight games and having hit .421. We’re talking about a player the owner of the American League champion Kansas City Royals recently called “the reincarnation of Babe Ruth” but who never previously hit five home runs in a month. Continue reading

Just Put the Ball in Play

On account of the success of Moneyball (both the book and the movie, nicely satirized here), baseball management is often compared to investment management, and with good reason. Moneyball focused on the 2002 season of the Oakland Athletics, a team with one of the smallest budgets in baseball.  At the time, the A’s had lost three of their star players to free agency because they could not afford to keep them.  A’s General Manager Billy Beane, armed with reams of performance and other statistical data, his interpretation of which was rejected by “traditional baseball men” (and also armed with three terrific young starting pitchers), assembled a team of seemingly undesirable players on the cheap that proceeded to win 103 games and the division title. 

Unfortunately, much of the analysis of Moneyball from an investment perspective is focused upon the idea of looking for cheap assets and outwitting the opposition in trading for those assets.  Instead, the crucial lesson of Moneyball relates to finding value via underappreciated assets (some assets are cheap for good reason) by way of a disciplined, data-driven process.  Instead of looking for players based upon subjective factors (a “five-tool guy,” for example) and who perform well according to traditional statistical measures (like RBIs and batting average, as opposed to on-base percentage and OPS, for example), Beane sought out players that he could obtain cheaply because their actual (statistically verifiable) value was greater than their generally perceived value. 

In some cases, the value difference is relatively small.  But compounded over a longer-term time horizon, small enhancements make a huge difference.  In a financial context, over 25 years, $100,000 at 5%, compounded daily, returns $349,004.42 while it returns nearly $100,000 more ($448,113.66) at 6%.

We live in a world that doesn’t appreciate value.  In the investment community, indexers are convinced that value doesn’t exist or can’t be reliably measured and most other would-be investors don’t have a good process for analyzing the data and are too focused on trading to recognize value when they see it.  While proper diversification across investments can mitigate risk and smooth returns within a portfolio, too much portfolio diversification (“protection against ignorance,” in Warren Buffett’s words) requires that value cannot be extracted. 

Similarly, behavioral finance shows how difficult it is for us to be able to ascertain value.  Our various foibles and biases make us susceptible to craving the next shiny object that comes into view and our emotions make it hard for us to trade successfully and extremely difficult to invest successfully over the longer-term.  Recency bias and confirmation bias – to name just two – conspire to inhibit our analysis and subdue investment performance.  Investing successfully is really hard.

To expand the idea (perhaps to the point of breaking), we must always resist the urge to trade – even a good trade – when investing makes more sense.  While I don’t mean to suggest that a one-off trip to Vegas for a week-end of fun can never be a good idea, too many trips like that can come between you and your goals and can thus be antithetical to a rewarding future.  My ongoing analysis of human nature suggests that we are not just subject to the whims of our emotions.  We are also meaning-makers, for whom long-term value (when achieved) can be fulfilling and empowering.  It simply (it is simple, but not easy) requires the process and the discipline to get there.  What we really need is not always what we expect or want at the time.

This point was driven home to me anew recently by the terrific movie, 50/50, written by Will Reiser about his ordeal with cancer.  As Sean Burns noted in Philadelphia Weekly, Reiser’s best friend was the kind of slovenly loudmouth that you’d usually find played in the movies by Seth Rogen, except that his best friend really was Seth Rogen.  Rogen’s fundamental, unexpected decency and supportive love grows more quietly moving as the film progresses.  Rogen was undervalued generally and his love and support provided great value to Reiser. 

As the cliché goes, nobody lies on his deathbed wishing he’d spent more time at the office.  We appreciate meaning and value more in the sometimes harsh reality of the rear-view mirror rather than in our mystical (and usually erroneous) projections into an unknown future.

All of which brings me back to baseball.  In today’s Grantland, Rany Jazayerli makes a persuasive case in his article, The MLB Prospect Bubble, that while teams once got great value by trading for minor league prospects, the landscape has changed such that prospects now tend to be over-valued. Note in particular his analysis of a recent trade involving Jarrod Parker (the prospect) and Trevor Cahill (the solid major leaguer), suggesting that in this instance Billy Beane got too cute by at least half:

Parker will probably rank somewhere around no. 30 when the Top Prospect lists are unveiled next spring. It will mark the fifth straight season that Parker ranked in the top 50 on Baseball America‘s list, which is itself a dubious distinction. But here’s the thing — in 2009, Cahill ranked no. 11. Cahill was judged to be a better prospect in his time than Parker is now, and Cahill has spent the last three seasons living up to expectations. How is it, then, that the A’s were willing to trade six years of Trevor Cahill for six years of Jarrod Parker?

It’s true that Parker’s ceiling is higher than Cahill’s, but if Parker were guaranteed to reach his ceiling, he wouldn’t be a prospect — he’d be a major league pitcher, probably an All-Star. Increasingly, teams have decided that they’d rather bet on that ceiling than take the guaranteed return.

Read that last line again: “Increasingly, teams have decided that they’d rather bet on that ceiling than take the guaranteed return.”

That’s a (a-hem) trade the I see all the time in the money management world because, after all, chicks dig the long-ball (oh the delightful irony): 


And it’s too bad.  Most investors would be well-served most of the time by resisting the urge to (over)swing for the fences in order simply to put the ball in play.  Reasonable returns together with loss mitigation is a powerful combination.  We should look for them, indeed value them, far more often.


Emanuel Derman’s new book, Models.Behaving.Badly, is a cautionary tale.  Derman, a former Goldman Sachs quant, examines why confusing illusion with reality can lead to disaster on Wall Street and in life.  You can read a bit about Derman (in his own words) here and here; his occasional blog is here. He is currently a professor at Columbia University, where he runs the financial engineering program, and is also a principal at Prisma Capital Partners, where he co-heads risk management.

As Derman puts it, the book is “about metaphors and analogies, about the nature of modeling and theorizing, the difference between them, why financial models will intrinsically and always at best be very limited approximations to reality, and what to do as a consequence.”

I had this general point driven home to me over the week-end in a surprising way.  On Friday, my better half and I went to see Moneyball.  I have been a big fan of the Michael Lewis book since it first came out back in 2003 (see my comments here) and was looking forward to the movie.  I was not disappointed.  It’s a great yarn with interesting applications to business and to life.

A small but significant role in the film was the Yankee cast-off, David Justice, played by Stephen Bishop (the actor, not the musician), a former minor league baseball player turned actor.  Bishop and Justice look a lot alike (see below) and Bishop had spent many hours as a kid idolizing Justice and mimicking his swing.   

Since Bishop and Justice had a bit of a relationship before the movie, Bishop drew on that experience and called on Justice for help.  No less an authority than Justice’s wife (not ex-wife Halle Berry) approved of the result, acknowledging that Bishop “nailed it.”

Happily, Moneyball the movie doesn’t deviate from reality nearly as much as the typical “based on a true story” Hollywood blockbuster.  But it is still a far from perfect representation of reality.  Obviously, movie-makers have much more of an interest in telling a good story than in scrupulously sticking to the facts.  But the limits of film vis-à-vis reality are far greater than those relating to telling a good story. 

On Saturday, I went to a youth football game near my home.  One of my wife’s students had written the best persuasive essay in her class.  Since the essay argued why his teacher should go watch him play football, we were in the stands.  David Justice was one of the coaches roaming the sidelines. I’m not interested in violating anyone’s privacy here, so suffice it to say that the real David Justice was significantly different from the David Justice character I had seen portrayed on screen the night before, at least in that instance.*  I shouldn’t have been surprised by that, but I was.  

Derman argues that in finance, models can only hope to provide a simplistic and very limited approximation to reality.  Movies — as in the portrayal of David Justice — cannot aspire to more and do not.  Indeed, they do not even aspire to that much. We would all do well constantly to bear in mind the limits of financial modeling in general as well as the limits of any specific model.  They aren’t representational; at best they are illustrative.

The resemblance of the Moneyball David Justice to the real David Justice is more than coincidental.  But it was still a long ways from reality — just like economic models.


* Obviously, people are more variable and complex than any model, which means that it would be easy to push this metaphor too far.  However, one instance of inaccuracy is sufficient to falsify a model, yet it is not enough to conclude that it is altogether useless.  Indeed, it might be highly accurate overall, just mistaken in the particular instance I witnessed.  That said, based upon what I saw, “highly accurate overall” seems unlikely.