Analytics and Hedgehogs: Lessons from the Tampa Bay Rays

The Tampa Bay Rays spend significantly less on payroll than some of the wealthier teams in Major League Baseball, but get results that are sometimes better than those that wildly overspend. The Tampa Bay Rays success boils down to two things – understanding how to be a hedgehog, and continual application of statistics and analytics into daily processes.

Tampa Bay RaysGreek poet Archilochus once said: “The fox knows many things, but the hedgehog knows one big thing.” Many interpretations of this phrase exist, but one characterization is the singular focus on a particular discipline, practice or vision.

According to a Sports Illustrated article “The Rays Way”, while Major League Baseball teams such as the Los Angeles Angels load up on heavy hitters such as Albert Pujols and Josh Hamilton, the Tampa Bay Rays instead have a hedgehog-like and almost maniacal spotlight on pitching.

For example, SI writer Tom Verducci says “The Rays are to pitching what Google is to algorithms.” In essence, the Rays have codified methods (on how to raise up young pitchers and injury prevention techniques) and daily processes (including exclusive stretching and strengthening routines) into a holistic philosophy of “pitching first”.

But enabling that hedgehog-like approach to pitching is a culture of measurement and analysis.  To illustrate, the SI article mentions that pitchers are encouraged to have a faster delivery (no more than 1.3 seconds should elapse between a pitch and hitting the catcher’s glove). Pitchers are also instructed to throw the changeup on 15% of deliveries. And while other pitchers try and focus on getting ahead of batters, the Rays have discovered it’s the first three pitches that matter, with the third being the most important.

In terms of applying analytics, the Rays rely on a small staff of “Moneyball” statistical mavens that provide pitchers with a daily dossier of the hitters they’ll likely face, including they pitches they like and those they hate. And analytics also plays a part in how the Rays position their outfield and infielders to field balls that might otherwise go into the books as hits.

The Rays are guarded about sharing their proprietary knowledge on processes and measurement, and for good reason, as last year they had the lowest earned run average (ERA) in the American League and held batters to the lowest batting average (.228) in forty years. Even better, they’ve done this while spending ~70% less than other big market teams and winning 90+ games three years in a row. That’s nailing the hedgehog concept perfectly!

Seeing a case study like this, where a team or organization spends significantly less than competitors and gets better results, can be pretty exciting. However, an element of caution is necessary. It’s not enough to simply follow the hedgehog principle.

The strategy of a hedgehog-like “focus” can be highly beneficial, but in the case of the Tampa Bay Rays, it’s the singular focus on a critical aspect of baseball (i.e. pitching), joined with analytical processes, skilled people and the right technologies that really produce the winning combination.

For Simplicity’s Sake – Learn from Peyton Manning!

Future NFL Hall of Fame quarterback Peyton Manning is tough to beat. What’s his secret? Is it accuracy, the ability to throw a “catchable ball” or capability to diagnose defenses quickly?  The answer is probably all of the above, to some degree.  Yet stated another way, Manning’s offensive excellence comes down to two things –simplicity and ability to execute.

As Chris Brown writes for Grantland, Peyton Manning’s offense is simple, simple, simple. Brown says; “(Manning runs) the fewest play concepts of any offense in the league. Despite having one of the greatest quarterbacks of all time under center, the Colts eschewed the conventional wisdom of continually adding volume to their offense in the form of countless formations and shifts.”

Image courtesy of USA Today and NFL.
Image courtesy of USA Today and NFL.

A small number of plays that “fit together”. That’s it, with various personnel groupings. Chris Brown mentions that sometimes Manning’s offense uses three wide receivers and a tight end. Sometimes, two wide receivers and two tight ends. The simplicity of the offense means that Manning can quickly come to the line, diagnose what the defense is doing, and then execute the best play possible.

Manning is essentially saying, “You’ve studied up on what I’m going to run, now try to beat me.” And while the Baltimore Ravens did just that in 2013’s NFL Divisional playoff game, Manning’s regular season record of 13-3 suggests few teams could “out-execute” the Broncos.

There are parallels in commerce. For many years, the rage was to get bigger and more diversified.  For example, under CEO Dennis Kozlowski, Tyco Corporation acquired more than 1000 companies in a ten year stretch.  But too much growth and diversity resulted in an unwieldy business model. Thus, in 2002 Tyco was forced shed businesses and consolidate into four companies to better meet customer needs.

Some companies are known for a simple business model. Case in point, Priceline is a discount travel site. But during the early 2000s dot.com boom, Priceline got off track as its founders believed the “name your own pricing” model could be translated to car sales, travel insurance and groceries. It was only after re-focusing on its core business of booking unsold hotel rooms, did Priceline’s market value zoom from $226 million (January 2000) to $33.41B today.

Applying the concept of simplicity also holds for products and services. I’ve previously written about how some companies such as AWS are subtracting mental clutter in cloud computing services. From design of the cloud management console, to the actual service offers, AWS is baking simplicity into very complex “behind the scenes” products.

Please don’t get me wrong. Once in a while you’ll have to add complexity to your business. And of course, there’s nothing wrong with purposeful M&A or adding new product lines to your business stack.

However, in the dash to add grow at all costs, instead take a page from the Peyton Manning playbook and choose the concepts of simplicity (possibly through consolidation, better design, or being pickier about additions) and precise execution (doing things better than competitors).

It Takes Courage to Compete on Analytics

Billy Beane’s “Moneyball” approach to developing and staffing a professional baseball team has come under intense scrutiny as long time major league scouts and analysts take delight in sub-par performances of the Oakland Athletics.  Beane however seems undaunted in using statistical analysis to undercover market place inefficiencies.  Indeed, it takes courage to solve problems in a whole new—perhaps heretical—manner. And while plenty of folks take pleasure in Beane’s recent comeuppance, there’s a good chance he’s already had the last laugh.

By now, most business professionals have either read Michael Lewis’ “Moneyball” or at the very least seen Brad Pitt’s rendition of Billy Beane in Hollywood’s version.  Beane’s claim to fame is that he had the courage to defy decades of “common knowledge” in assembling a winning baseball team.  For many years, winning teams were accustomed to paying top dollar for the best hitters and pitchers evidenced by metrics such as batting average and earned run average respectively.

However, in the small-town Oakland market, Beane didn’t have the luxury of affording a high payroll. And if he wanted to compete, he needed to find a different (and perhaps better) method of keeping up with the likes of the Boston Red Sox and New York Yankees.

Beane knew there were “inefficiencies” in baseball markets that could be exploited; he just had to discover those undervalued metrics.

Enlisting the help of his Harvard friend Paul Depodesta, Beane used statistical analysis to discover metrics that—in his opinion were more indicative of baseball success—such as on base percentage and slugging percentage for batters, or the ability to get “outs” for pitchers.  Then Beane went about acquiring a motley bunch of players that most teams discarded as near worthless, and positioned each player for success based on his analytical models.

And for a while it worked. With one of the league’s lowest payrolls the Oakland A’s competed with teams spending 2-3x in overall salaries. In fact, a Sports Illustrated article mentions; “From 2000 to 2006, (Oakland A’s) averaged 95 wins, captured four AL West titles and made five playoff appearances.”

Alas, all competitive advantage is fleeting. As Paul Depodesta left for greener pastures, and a few of Beane’s disciples took other league jobs, major league baseball teams figured out what made Moneyball work and effectively neutralized the Oakland A’s advantage.  With other teams adapting to Moneyball tactics, from 2007 and beyond, the Oakland A’s were a .500 team at best, and sometimes much worse.

And in the process of adopting Moneyball tactics, Billy Beane made plenty of enemies. The “old guard” definitely had a preferred way of defining baseball success and would often scoff at the “fat bodies” Beane employed, and laugh at the un-orthodox pitchers he would trot out in the ninth inning.  And in regard to the lackadaisical record of the Oakland A’s in recent years, one scout sneered; “So much for the genius … He doesn’t look so smart anymore, does he?”

Competing on analytics takes courage. In response to analyst and pundit criticism leveled at Beane for his Moneyball approach, author Michael Lewis says; “Beane had the nerve to seize upon ideas rejected, or at least not taken too seriously, by his fellow Club members, and put them into practice.”  For a while, Beane had the advantage. It might not have been pretty, but the Oakland A’s were effective at winning as many games as other prestigious teams, for half the cost.

And Beane ultimately may have the last laugh. Today, there are teams like the Boston Red Sox that take the best of Moneyball tactics and overlay them with powerful financial resources to take baseball competitiveness to a whole new level. Baseball has been changed forever from a “gut feel” business to one that’s analytically driven.

Fortunately for today’s analytical professionals, “competing on analytics” is taking hold as more companies understand they’re sitting on literal goldmines of structured and multi-structured data just begging for analysis. But analytics users be forewarned, there’s still plenty of “old guard” that will put up roadblocks, enlist subterfuge and even openly mock your new data driven approaches.

Michael Lewis notes that Beane didn’t invent sophisticated analytical analysis—he just had the courage to use it to create competitive advantage and shake up a traditionally stodgy industry.  There are plenty of industries ripe and ready for the business value that analytics will eventually unlock. Are you ready for the challenge?

Are Data Scientists the Next Masters of the Universe?

Back in the late 1970s, traders buying and selling mortgages were pushed aside for new masters of the universe—“quants” or individuals that used mathematics to slice and dice mortgages into debt tranches. And in the same way, today’s traditional Business Intelligence (BI) professionals must be looking over their collective shoulders as business and IT publications tout the emerging role of “data scientist”.

Before Lew Ranieri came on the scene, mortgages were a very staid business. Banks would loan money and keep assets on the books for up to thirty years (depending on how quickly the loan was paid back). Except for underwriting skills, there wasn’t much complexity to the mortgage business.

As a trader for Salomon Brothers, Lew Ranieri changed all that.  Ranieri’s insight was that mortgages could be bundled together and then sliced into different tranches of varied risk.  This slicing exercise was quite complex because of a buyer’s ability to prepay their loans early or refinance.  Michael Lewis, of Liar’s Poker fame writes; “Mortgages were acknowledged to be the most mathematically complex securities in the marketplace. The complexity arose entirely out of the option the homeowner has to prepay his loan…mortgages were about math.” 

Suddenly the very boring business of home loans became a very complex business challenge in how to slice the pie based on risk profiles and cash flows from interest and principal. Lewis writes; “Different investors place different prices on risk. Risk could be canned and sold like tomatoes.” And this mathematical complexity demanded a new skill set—quantitative analysis—to perform the necessary mathematical modeling to ensure investment banks remained profitable in this new business.

Pushed out by a new breed of mathematical whizz-kids, many former investment bankers and traders either retired or left for smaller financial firms. And the rise of the quants—or the new masters of the universe—was complete by the mid-1980s.

Is a similar shift happening in the field of Business Intelligence with the emerging “data scientist” role? The skill set of today’s data scientist is much more robust than one who solely performs BI or ETL application development.  With new sources and types of data (i.e. multi-structured), the data scientist must be able to develop new data driven products such as churn models, create recommendation algorithms, assist marketers with behavioral segmentation and targeting and more.

But that’s not all. Fellow SmartDataCollective contributor Daniel Tunkelang says the data scientist; “Also needs to possess creativity and strong communication skills. Creativity drives the process of hypothesis generation, i.e., picking the right problems to solve that will create value for users and drive business decisions.”  Tall order to find all these skill sets in one person, much less build an internal competency center with such talent.

Perhaps for the foreseeable future, there’s room for both traditional BI professionals and the new breed of data scientists, as today both are valuable contributors in the field of analytics. However, with data growth on a fast paced exponential curve, much less the complexity and velocity of multi-structured data, it’s easy to see how the mix of skill sets to succeed in the future will tilt more in favor of the data scientist role.

The mortgage bankers never saw Lew Ranieri coming. Regarding the rise of data scientists—should traditional BI professionals be worried?

Unintended Consequences of Combining Speed with Technology

Technology is often hailed as innovation vehicle, productivity booster, and enabler of a higher standard of living for all global citizens. However, the field of finance provides an interesting backdrop for what happens when an industry is pushed to its technological limits in the pursuit of automation and speed.

Since advent of the telegraph, and all the way until early 1970s, stock prices were displayed on a ticker tape printed in near real time.  The ticker tape (via telegraph technology) was a drastic improvement in delivery of information, since brokers could gain stock prices with only a 15-20 minute delay from original quotation.

Setting the dial now to the year 2011, we now see super computers trading stocks—not with humans—but, with other super computers. Forget delays in minutes or seconds, today’s super computers trade in microseconds and are increasingly “co-located” near stock exchange servers to reduce the roundtrip time for electrons passing through networks. In fact, on most trading floors, human brokers are obsolete as algorithms are now programmed with decision logic to make financial instrument trades at near light speed.

We’ve come a long way since the decades of ticker tape, says Andrew Lo, professor at Massachusetts Institute of Technology (MIT). At a recent conference Professor Lo mentioned while technology has opened markets to the masses (i.e. day-trading platforms) and reduced price spreads, there are also downsides to automation and speed.

First, he says, there is the removal of the human element in decision making. As super computers trade with each other in near light speed, there are smaller and smaller windows of latency (between event and action) and therefore fewer opportunities for human intervention to correct activities of rogue algorithms or accidental “fat finger” trades.

Second, with fiber optic networks spanning ocean floors and super computers connecting global investors and markets, we’ve essentially taken a fragile system based on leverage and made it more complex. Automating and adding speed to an already “fragile” system generally isn’t a recipe for success (i.e. the May 6, 2010 Flash Crash).

Based on these trends, it’s easy to imagine a world where financial networks will intensify in complexity, capital will zip across the globe even faster, and relationships between market participants will increasingly grow more interconnected. Where loose correlations once existed between participants and events, markets will soon move in lockstep in a tightly coupled system.

To be sure, the confluence of technology and finance has been a boon to society in many respects. However, as Lo says, there are “unintended consequences” in the application of the most advanced and fastest technologies to an already fragile system.  Whereas the buffer of “time” to fix mistakes before or even as they occur once existed, now we’re left to clean up the mess after disaster strikes.

In addition, as markets become more tightly coupled and complex, the butterfly effect is more pronounced where the strangest and smallest event in a far away locale can potentially cause a global market meltdown.