What baseball can tell us about financial crises | FT Alphaville

What baseball can tell us about financial crises

FT.com reporter Jason Abbruzzese submits this guest post for FT Alphaville.

Yogi Berra, 1973:

“It’s not over til it’s over.”

Timothy Geithner, 2006:

“In the financial system we have today, with less risk concentrated in banks, the probability of systemic financial crises may be lower than in traditional bank-centered financial systems.”

Probability, statistics, sabermetrics – these are the new tools of both the baseball world and the financial world. Our ability to manipulate maths would seem to give us the ability to predict anything and everything. Oh, the hubris.

As you may have noted, many writers have argued that the financial crisis was not an improbable collapse, but rather an inevitability based on a series of bad decisions and general idiocy.

And that’s how one would have to describe the collapse of both the 2011 Boston Red Sox and Atlanta Braves. (These are baseball teams, cricket fans.)

This is without a shred of grandiose hyperbole – last night was the most incredible night of baseball, of any sport, that this blogger has ever seen. For brevity’s sake, let’s refrain from going through all the insanity, but you can find good summations here and here. But if last night was written in a movie, nobody would buy it, a prime example of truth being stranger than fiction.

Though if you want to get some sense of the swings and arrows of outrageous fortune involved here, check out this chart from fangraphs:

In retrospect, the anatomy of these two collapses has some interesting similarities. Apologies to Braves fans, but this will focus on the Red Sox.

The statistical revolution of the last 50 years has seriously impacted both the financial markets and professional sports. The Red Sox are among the teams that have attempted to most directly embrace this numbers revolution – often called Sabermetrics, which was written about by Michael Lewis in ‘Moneyball’ – in theatres now.

In finance, computer-based trading allowed the best and brightest to set their minds to figuring out just which algorithm can take advantage of increasingly complex financial instruments.

Looking back on each of these situations, we see that these numbers are often only as good as the people they’re built on and the people who built them. Statistics didn’t predict the fatal flaws in CDOs. Indeed, it was a formula that felled Wall Street.

Statistics did not predict the history-making crash of the Boston Red Sox – widely regarded before the season by statisticians and overweight sportswriters alike as the best team in baseball.

Nor could they. Statistics by their nature are backward-looking and can only give us an idea of what to expect. Nate Silver does an excellent job doing some back-of-the scorecard calculations on the odds of last night. Here’s the gratuitous pull quote:

The following is not mathematically rigorous, since the events of yesterday evening were contingent upon one another in various ways. But just for fun, let’s put all of them together in sequence:

· The Red Sox had just a 0.3 percent chance of failing to make the playoffs on Sept. 3.

· The Rays had just a 0.3 percent chance of coming back after trailing 7-0 with two innings to play.

· The Red Sox had only about a 2 percent chance of losing their game against Baltimore, when the Orioles were down to their last strike.

· The Rays had about a 2 percent chance of winning in the bottom of the 9th, with Johnson also down to his last strike.

Multiply those four probabilities together, and you get a combined probability of about one chance in 278 million of all these events coming together in quite this way.

That’s a staggering number. But of course no matter how a baseball game turns out, the chances of it happening that way were infinitesimally small. But we wouldn’t be calculating this if both games ended up 8-3.

Silver adds: “When confronted with numbers like these, you have to start to ask a few questions, statistical and existential.”

Just like how statistics did not predict nor create the financial crisis, numbers didn’t cause Jonathan Papelbon to throw a fastball down the center of the plate when he was ahead in the count. That was human error, just like the financial crisis.

But looking at the financial crash, it seems like it could happen no other way. The dominoes were set up; it was just a matter of triggering the fall.

Ask any baseball fan (or at least any Boston or Atlanta fan right now), and they would tell you this could not have happened any other way. These dominoes were set to fall as well, and they came down with righteous fury.

Down to their final out, Rays batter Dan Johnson, batting an anaemic .119 (that’s bad, cricket fans), with two strikes, launched a ball out of the park that tied a game that the Rays would go on to win.

The chances of that are small, to say the least, yet not a single person in Red Sox nation looks back at it with surprise. And that’s exactly how many of us look back at the financial crisis – it was inconceivable, but now none of us are surprised.

But hey, there’s always next year.

Related links:
Reliving The Final Day in the AL, Visually – Fangraphs
The Best Four Minutes Of Baseball Last Night, Presented In Split Screen – DeadSpin