So, how would you feel if they picked Brad Pitt to play you in the movie?

On one hand, you have to be walking tall as you look in the mirror and secretly enjoy the idea that the big money people in Hollywood saw enough of a resemblance to make that call.

On the other hand, if you are a baseball front office geek, no one knows who you are and what you look like anyway. They all probably assume there is nothing about you that looks like Brad Pitt.

Then there is your wife. Does she smile because her husband looks like Brad Pitt, or in a bad moment fall into a snarky look of disdain and mutter, ‘Well, I know you’re no Brad Pitt.’

I wonder if Billy Beane had any of those thoughts, or was just hoping that he comes off as well in the movie as he did in the book.

Michael Lewis is one of my favorite authors. He nailed ‘The Blind Side,’ combining the moving story of Michael Oher and the Tuohy family with the intricacies of the evolving pass rush strategies in the NFL. Having Sandra Bullock in the movie didn’t hurt.  Early books gave us the terms 'Liar's Poker' and 'The New New Thing.' 

Then when I read ‘The Big Short’ and finally began to understand the inner workings of synthetic credit default swaps and the role they played in bringing our economy to its knees in 2008 I thought a new cabinet position should be created with Lewis named as the first ‘Secretary of Explaining Really Complex Things to the American Public.’ We could use this role, especially now.

If you are late to the Lewis bandwagon, you may have missed his first big hit, ‘Moneyball.’Depending on when you read this, the movie is about to hit the screens and we’ll see Brad Pitt’s portrayal of Beane, the central character of the story. You can then come up with your own answers to those important questions raised at the top of this piece.

Here is a quick primer on Moneyball, though Google might help, too.

Baseball is clearly a sport of the ‘haves’ and ‘have nots’ in terms of both economics and championships. We’re guessing there is more than just a coincidental correlation. Large market teams like the Yankees and Red Sox have annual player payrolls larger than the market cap of most public companies. They dwarf the GDP of about 80% of the countries in the UN.

Conversely, the entire payroll of small market teams is about the same as what one star Yankee makes in a year. ‘We’ll trade you our entire team for your third baseman.’

Yet, both teams get 27 outs a game and only get to play nine players at a time.

The playing field is not balanced.

In 2002, the Oakland A’s, one of those small market teams, was surprisingly very competitive, hanging in there with teams that literally spent 3x more. Lewis, the teller of complex tales that have a human face, was intrigued and smelled a story.

The movie might have gotten made sooner if Lewis had found the secret to the A’s success to be a crusty old coach battling cancer or a one-eyed washed up dude from Kansas who finally got his chance at the big leagues and had that one glorious season. Unfortunately, the root of the mysteriously good play of the ‘other’ team by the Bay proved to be far less interesting.

Sabermetrics was the secret sauce.

Oakland was winning because they used some new-fangled advanced statistics.

Really? This was about a gutsy Beane willing to go against conventional wisdom and trust the insights of some goofy kid with a laptop?

Our American pastime, replete with colorful characters like The Babe and Jackie Robinson, was now being run by a couple of gadget heads with a bunch of spreadsheets? It almost took the taste of the hot dogs to talk about the grand old game as if you were analyzing a stock portfolio.

But there was Beane, the young general manager of the A’s and his ragtag bunch of castoff players in the thick of the 2002 playoff race with sabermetrics in hand.

At the time, Beane was a real innovator, downright radical even. His approach challenged the established incumbents. The clubby fraternity of baseball scouts, old school guys who sat in the sun all summer with a radar gun and a stop watch and a notebook stained by beer, were the ultimate arbiters of talent. They knew who could make it to the Show and who could not.

‘He’s just a warrior. You want to go to battle with that guy.’

‘A uniform just looks good on him. Draft him.’

‘I’ve seen that look in his eye when he is at the plate and the game is on the line. Make the trade.’

Oh, there were a few stats used. The old stand-bys that we knew from the back of a baseball card – home runs, batting average, stolen bases, strike outs, RBI, the like – were part of the scout’s report up to the bosses. Beane, however, shows up referencing a player’s ‘OBP’ and the establishment wondered if he was talking about the kid’s cholesterol levels.

What was unleashed by Beane, and accelerated when the book came out, is now standard fare in baseball and every other professional sport. Sabermetrics, the use of a broad range of objective data to assess talent and value, is now a given in the front office of your favorite sports team.

Statistics are a measurement of something and it turns out they can evolve and advance, even in the old game of baseball. When that happens, new insights are generated, which lead to new frameworks for making decisions.

For example, Beane deduced that a key part of the game was getting on base (hard to score when you don’t), so the ability of a player to get on base should be measured more directly. Rather than having to infer that from other measures and risk losing cause and effect in the discussion, Beane wanted to know exactly. The now standard ‘On Base Percentage’ stat was born.

Ironically, as sports salaries escalated, the geeks found an unexpected friend who also liked all the charts and graphs.

Imagine you just paid a gazillion dollars to buy a professional team. In your next meeting, your general manager, head coach, and player personnel guy all sit across your desk and tell you that you really should sign off on that $100 million contract for this 24 year old kid that wears polyester pinstripes to work and spits all the time.

Now, you probably got the cash to buy the team in the first place because you had been successful in business. You made your money because you worked hard and did your homework. You did not become successful making $100 million bets just because someone on your management team says ‘Hey, let’s acquire that company. I’ve just got a good feeling that it will be a winner.’ You are not an idiot.

Then as the owner you found this 28 year kid in the basement of your office who watches too much ESPN, but has a degree in engineering from some Ivy League school. ‘Why did we hire that kid?’ And he shows up with a bunch of numbers that attempt to validate why the big jock is, or is not, worth your committing to them a large chunk of your fortune. You like this kid. You have no idea if the star’s cutter fastball really falls off the table to right handed hitters as the scouts are telling you, but you can tell from the spreadsheet that he will likely produce five more wins than the other guy and you know what every win is worth to your team.

Finally, there is some objective way to think about the incredibly insane idea of a $100 million contract.

So it all took off.

Two quick examples make the point about how far things have come since little Billy started shaking up the game.

The Yankees, Darth Vader to Beane’s Yoda, now have 20 people in their data analytics department for player evaluation. That is more than two stat geeks for every position on the field. Talk about revenge of the nerds.

Also, for the past five years, MIT’s Sloan School of Management has hosted its annual ‘Sports Analytics Conference’ where owners, agents, probability experts, and PhDs in statistics gather to talk to an audience of 1,500 that probably never had the talent to play the game, but have the brains to figure out to measure every aspect of it. This conference is better known as ‘Dorkapalooza.’ I kid you not and it is on my bucket list to go some day.

Hopefully by now you have started to guess that this story is in a healthcare blog because we are on our own sabermetrics journey. I don’t want to insult anyone, but sports are probably a good parallel universe for us to consult. I mean, with all of the variables that affect athletic outcomes, how much complexity goes into trying to measure the value of a player? Have you ever sat in a sports bar and listened to two guys argue over who is the best ever – this guy or that guy? There are facts and data flying all over the place. But conclusions are hard to nail down because of the complexity.

As we try to make some sense of how we measure value in a system that also has a million variables, some independent and some dependent and thousands that are confounding, there may be lessons we can learn from the Billy Beane crowd. Here are three potential nuggets for us healthcare types.

First, the purpose of the sabermetrics created by the emerging sports executives was very specific and often gets lost in the story. Oakland, you see, could only afford a $40 million payroll when the Red Sox spent over $120 million. They were not measuring just to measure. They were looking for undervalued assets. They were looking for players who according to conventional measures and the collective wisdom of scouts were commanding a much lower salary but who could really produce. They were looking to exploit inefficiencies in the pricing of the market.

It is understandable when someone in healthcare gets excited about having this new sand box of data. They start measuring anything and everything and publishing cool charts and graphs. But we’re looking for an advantage. We’re looking for an inefficiency in the market that the traditional mindset has missed. We’re looking for a leverage point that can show us how to manage a certain disease state in a totally different way.

It is not about coming up with a new measure that makes you sound smart at a conference. It is about getting more by finding undervalued assets. And staying away from overvalued ones.

Just a random question here to stir the pot…It seems every health system in the country is buying up physician practices at a rapid pace. There is clearly a shared mindset about how to make this new delivery model happen. But is there any objective proof that the CEO should sign off on that big contract to that cardiology group? Sort of sounds like the Red Sox or Yankees, doesn’t it? What if someone walked in with some numbers that suggested a different model? Could the Oakland A’s of your healthcare market find a way to compete with the big boys by leveraging undervalued assets instead?

A second lesson is that while the geeks are on the ascendancy, don’t fire the old sages just yet. The sabermetrics movement in baseball put the old guard on notice that gut feelings would be challenged with data, but the best organizations find a way to blend the analytics with seasoned experts. Together, they get better decisions in those fuzzy places where the data still do not reach all the way through.

That model would seem to make sense in healthcare as well. We need far more data-based decision making and it needs to become standard, but there is still a critical role for the wizened clinician to put it all together and navigate those really nuanced blind spots, especially for extremely complex patients.

Finally, recognize the metrics race is never over. Many in baseball assumed that once Beane’s brain was picked by the book, every team would expand the stats it used and we’d have a new, if expanded, set of numbers on the back of the playing card. However, the strategy of the game constantly changes and thus, the sabermetrician’s must constantly hustle to keep up.

We’ll see the same thing in healthcare. As we learn how to more effectively deliver care to solve the big problems in front of us today, new challenges will emerge that do not succumb to the clinical measures that seem ‘oh so cool’ now.

The free advice today is to embrace our version of sabermetrics. New measures – clinical, financial, who knows – are going to show up and the organizations that embrace the paradigm first will have a huge advantage. As with baseball, the likely innovators will be the small, scrappy organizations who do not have the massive balance sheets. If you are one of those and you feel you are stuck in the shadow of the behemoth of your market, take heart. Grab your data dude and get to work. You can be the Billy Beane of your organization. Maybe Brad Pitt will play you in the movie.