Buzz Bissinger wrote a post for The New Republic yesterday entitled “Against ‘Moneyball’” in which he attempts to make the case that the Moneyball philosophy is not all it was cracked up to be. His lead off argument is the fact that the teams that are currently still standing – the New York Yankees, the Los Angeles Angels, the Philadelphia Phillies, and the Los Angeles Dodgers – spent a combined $528,620,438 on payroll this year. In Bissinger’s mind that proves that the Moneyball philosophy doesn’t work.  Bissinger suggests that the teams still in the game spent a ton of money to get there and spending money is what you need to do to win the big prize. I don’t disagree with him there. However, I do disagree with his implication that Moneyball has failed because getting to the playoffs and the World Series is still seemingly determined by the size of a team’s payroll.

It occurs to me that at this juncture I should take a brief respite from railing on Bissinger’s post to explain to you non-moneyballers out there what the Moneyball philosophy really is. Essentially the Moneyball philosophy suggests that some players are undervalued because baseball teams and scouts aren’t looking at the right stats.  One of the key stats, as used by Billy Beane in Michael Lewis’ book Moneyball, was on base percentage (or OBP). High OBPs meant players got on base. Beane’s argument was simply this: who cares if they get on base because of a hit or a walk? Good point. Beane found payers with high OBP that didn’t have stellar stats elsewhere on their baseball cards and this helped Beane get these players on the cheap. The high OBP then translated into more wins for Beane’s Oakland A’s than if he had targeted players based on overly exploited stats like batting average.

Now, back to Bissinger’s argument.

In his post in the New Republic, Bissinger seems to be arguing that winning overall pennants will be the only thing that shows Moneyball works. Since Beane, for example, has never won a World Series, has only made the playoffs in two of the last seven seasons and has had teams fall under the .500 mark for the past few seasons shows, in Bissinger’s eyes, that Moneyball doesn’t work.

While I appreciate the heart of Bissinger’s argument – that baseball needs something other than Moneyball (perhaps a salary cap) to right its issue with big spending teams always winning – I think he isn’t looking at the right things.

Sure, we can all agree that Beane, with a $62mm payroll, is probably never going to beat the Yankees, who paid out $201.5mm this year. No hidden stat that can save Beane money on players is going to overcome that. However, I am not sure even Beane himself would suggest that he could get the A’s to the promised land on statistical analysis alone.  All he can do is optimize his wins per dollar spent ratio.

I can’t take credit for thinking of this quick metric.  My Dad suggested it after reading Bissinger’s article.  I went ahead and did the quick analysis per my Dad’s suggestion.  Here is is:

As you can see, I ranked the teams by their “cost per win” from the lowest to the highest. I also kept their payroll ranking (1 being the highest) to the left of the team name so it would be somewhat easier to see the correlation (I would have made a nice visualization of this data but I am simply too tired…).

Let’s look at Beane’s A’s. Not too bad: 26/30 in payroll but 8/30 in cost per win.  That means the A’s are being pretty efficient with their dollars and that is all Moneyball can be expected to help with. (That said, they were not as efficient as they could have been. They should have been somewhere around 4 or 5/30 in cost per win.)

Another missing piece from Bissinger’s article is that once certain stats like OBP begin to be picked up by other teams, as it has been, the effectiveness of those stats to find players at a good value diminishes. In economic terms: the market becomes “efficient” and new stats need to be found to create a new advantage for the team that finds them.

Lastly, I should mention that Bissinger’s argument is partly tied up in giving Lewis’ book a hard time for only focusing on the good things that Moneyball did for the A’s while leaving out a lot of the not-so-great players that the Moneyball philosophy dug up.  Let’s be honest, Lewis is a writer trying to come up with an engaging story and to make a point.  Bissinger should probably realize that no one, even Lewis himself, really expected “Moneyball” to be academically rigorous.

At the end of the day I think Bissinger may just be fed up with the ridiculous uncapped salary situation in Major League Baseball (I am with him there) and he decided to take that frustration out on Moneyball. However, his tough stance on Moneyball is unwarranted. The Moneyball philosophy was never meant to make a small payroll team into a World Series Champion nor did anyone in their right mind think it ever would. It was simply meant to keep the teams that can’t spend the big bucks at least somewhat competitive and it seems to be doing just that.

Side note: I absolutely think the MLB needs a salary cap and fast. The fact that the MLB doesn’t have one is simply ridiculous and it is causing the game to become less fun with each passing year.  Contrast that with the NFL where any team can win on any given Sunday. That is a much more compelling proposition to fans across the country. I mean really, if you are an Oakland A’s fan, why do you even bother getting excited on opening day knowing that you don’t have a chance of winning a pennant? That’s a big problem for the MLB and it will come to a head sooner or later.

Vote for my SXSW 2010 panel

I had a fantastic time at SXSW last year but I was left wanting one thing and that was more finance and economics related content. There are so many interesting things happening at the convergence of interactive media, technology and finance and the field is ripe for innovation.  Given that, I was amazed at the lack of content around finance.

So, I decided to do something about it. I put forth an idea for a panel for SXSW 2010 called “Data is Money: How geeks are changing finance.” (Please vote for it here: http://bit.ly/LBApG and spread the word!)

This panel will bring together experts in finance and technology to talk about how the future of finance will be influenced by data geeks and technologists. We will explore new financial data formats, like XBRL, and discuss how these formats, along with other recent advances, will allow all of us to play a role in the creation of a better financial system.

Jesper Andersen, co-founder of Freerisk.org, is already on board for the panel and Charlie Hoffman, the father of XBRL and author of the forthcoming book XBRL for Dummies, will be joining us as well (schedule permitting of course).  I will also pick up one or two more speakers if the panel is chosen.

It should be a great panel and I hope all of you out there will vote for it so that it will be chosen. The topic is important and certainly relevant given where the economy is today.

Vote for my panel early and often: http://bit.ly/LBApG Thanks for your support!

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Other great economics and finance related SXSW panels you should vote for:

“Saving the New Economy from the Past”

“Banking 2.0: Financial services driven by people and emerging technologies”

Being back in school and taking statistics and finance classes again has reminded me how much I love data. I am not one of those crazy guys who loves data in an unhealthy way and thinks that more of it will solve of all of the problems in the world mind you.  Nor do I believe it can always be trusted. The world is too uncertain and it is often hard to use data to predict the “once every 10,000 years” events that often do the most to shape the world we live in (read The Black Swan by Nassim Taleb for more on that).

What is interesting to me as of late is that we really have reached the point of having free and ubiquitous data.  In fact, we probably have too much data. I would argue that the financial crisis happened partly due to the fact that too much data was available for humans to effectively analyze and understand. I mean, let’s get real, it isn’t like the data wasn’t there.  If you looked at the right data and crunched the numbers you could have seen, as some did, that we were in for a world of hurt.

So now that we are at the point where data is essentially a commodity of sorts we have to figure out better, faster and cheaper ways of analyzing and visualizing data.  The better we get at this the better decisions we’ll make and the more crises we can avert.

FlowingData posted a quote from Google’s Chief Economist Hal Varian that sums things up nicely:

I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?

The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.

I think statisticians are part of it, but it’s just a part. You also want to be able to visualize the data, communicate the data, and utilize it effectively. But I do think those skills—of being able to access, understand, and communicate the insights you get from data analysis—are going to be extremely important. Managers need to be able to access and understand the data themselves.

Hal Varian, The McKinsey Quarterly, January 2009

Since I have been thinking more about data and specifically how the financial crisis could have possibly been averted if we could have had a better handle on the mountain of data crammed into arcane forms like 10-Ks and 10-Qs, I have been reading up on XBRL.

XBRL, or Extensible Business Reporting Language, is an open standard for business reporting that I am very excited about.  It could do for financial data what feeds and xml did for content.  When all of the data is structured we can then start to set up smart software to analyze the large amounts of data that exist.  That’s a big deal.

The good news is that XBRL is slowly becoming mandated for public companies in the U.S. an abroad so we’re not far off from being able to analyze and visualize financial quickly and easily.

XBRL, as I tweeted earlier, is the intersection of some of my favorite things: software, structured data and finance.  I am planning on digging into XBRL much more and will probably write about my findings as I, well, find them. Stay tuned.