Bigger Than Baseball: Leadership Lessons From The 2017 World Series

Last week’s World Series is now history. But before you turn the page on that baseball story, take one more look, in the context of a bigger conceptual map. The tale has some interesting strategic lessons about a paradigm broader than our national pastime, about leadership in a world increasingly dominated by automated intelligence and the continuing convergence of machine and human learning, reports Forbes.

Embracing Moneyball

Okay, so how about those Houston Astros? They won a thrilling World Series against the Dodgers in seven dramatic games, capping a multi-year strategy to take the trophy. Hats off to them, climbing from “worst to first,” from league-losing laughingstock to the spirited victors that bested three of the most formidable (and bigger payroll) rivals in the final month of play. As Ben Reiter of SI.com nicely chronicles (and even predicted in 2014!), the Astros’ gutsy rebuilding plan yielded a major turnaround. They did it thanks to great new talent and a relentless embrace of the now famous “Moneyball” model.

Moneyball (popularized by the pioneering statistic-based management of the Oakland A’s Billy Beane) has been its own revolution for baseball: a transformational innovation driven by empirical analysis of past player performance, which coaches use to more strategically hire and orchestrate their squads against competitors. Analytics-driven strategy has been evolving for a couple of decades since Beane's breakthrough, and is now followed in some version by most teams.

But Also Embracing More

Five years ago, Houston’s owner, Jim Crane, went looking for a general manager to take Moneyball up another notch. He hired Jeff Luhnow, a mid-level recruiter for the St. Louis Cardinals; Luhnow was also (more significantly) an ex-McKinsey consultant and tech entrepreneur, trained in engineering and economics.

Luhnow soon built a computer-savvy, scientific and quantitative research team unmatched in pro baseball. Before long, “the Astros developed into the one of the most industry’s most analytically driven organizations, relying almost entirely on data to navigate through a full-blown rebuild,” as sportswriter Jared Diamond reports. After a few years of experimentation, the investment in super analytics started to pay off. The Astros began winning much more consistently.

Discovering Both/And

But as Luhnow also conceded in his interview with Diamond, the new Astros magic was not just science and technology but also “the human element--especially “blending them together.” The sports reporting suggests that the strategy succeeded because, over time, it moved beyond the all-too-common “either/or” choice of automation vs people, and instead adopted a fusion of “both/and.” Astros-style Moneyball was beating other teams because of the more human touch the club brought to algorithm management.

For example, the Astros made recruiting choices that innovatively combined “softer” people-related information(e.g. players’ personal backgrounds, health, swing idiosyncrasies, etc.), with more objective performance stats; their managers also took care to leverage the subjective intuitions of their scouts and seasoned staff during hiring and strategy processes. Numbers-driven talent development was continually buttressed by extra human effort along the way too. At a critical moment, for example, the front office added key (and more expensive) older players to mentor younger newcomers and build more team spirit. Luhnow and players also give plenty of credit to Astros field manager (head coach), A.J. Hinch for his strong leadership, motivational communication and personal trust-building belief in the team.

Evolution And Learning

The analytical-human fusion developed in stages. At first, some players resented being turned into a number. Luhnow reportedly went through his own epiphany, and then launched an educational and relationship-building effort to help players understand how and why analytics could make such a difference to them as a team. The campaign helped further humanize the culture.

As the Astros approached the final games of the championship, Luhnow and Hinch once more added to the people side of the ledger, by turning the local city’s hurricane disaster into community-spirit opportunity. The managers endorsed team members’ efforts (including their donations, volunteering, and public discussions) to support Houston fans looking for solace in the wake of Harvey. No number-crunching can explain the motivational chemistry that arose between this Cinderella team and the 40,000 screaming fans showing local pride in the hometown bleachers.

One Size Won’t Fit All

In the wake of the World Series victory, Luhnow acknowledged the importance in rebuilding the winning team of people, culture and analytics together. But he was also quick to downplay any universal formula. As he told Tyler Kepner of  the New York Times, “Not every plan makes sense for every team . . .  But where we started, with the worst team in baseball . . . we really had no choice. We had to focus on developing our own, and when the time is right, adding to it.”

But Lessons Nonetheless

Luhnow rightly waves off proclaiming a generic blueprint for World Series victory—every Major League team is different, and no step-by-step replication of Astros 2017 will guarantee someone else the next trophy. There are just too many variables in play.

But the Astros' story does hold a few deeper lessons for leaders, beyond baseball. I’ll mention four.

Lessons No. 1 and 2 affirm what strategist Michael Porter long ago taught. First, if the rules of competition shift—e.g. baseball brings quantitative analytics to what was once just a gut-judgment sport—you have no choice but to join the arms race. Owner Luhnow knew he needed to play Moneyball too, if he wanted to have any hope of bringing the Astros back. Whatever your game, you can't fall behind the  protocols of today's competition.

But then Lesson No. 2 says: Getting in the new arena is simply the price of admission. To win the now more serious game, you have to also go above and beyond—be different or better than everyone else. The Astros raised the stakes in the Moneyball wars by putting extra horsepower into their analytics—but then went  another step further, artfully combining human and organizational strategy with the hard numbers. What will you do, to be better and different in your game?

Lesson No. 3 echoes the wisdom of more recent strategy thinking: innovative business models iteratively evolve. The new leaders of the Astros grabbed analytics full-on, but then dynamically adapted, adding more human aspects, based on year-by-year experience. Like the Astros, you need to have the patience and fortitude to keep experimenting and learning as you develop your strategy over time.

A Deeper Insight For The Future

Lesson No. 4 takes us into more subtle issues, of a global economy increasingly transformed by technology-enabled knowledge. As we hurtle towards a new reality of algorithms everywhere, growing artificial intelligence and robotic work, how will leaders in fact make strategy? Or does the forthcoming “singularity” now trivialize the question?

Nobody knows, of course. But savvy bettors believe that at least for the foreseeable future, victory will go to the entities that most adeptly combine computing and human intelligence,  both learning aided by technology and learning still in people’s head and hands. The Astros are a micro-case of an organization that successfully combined the two for their sports challenge. But this is only one more small datapoint in an ongoing trend. How will you find the right combination of algorithms and people for your challenges?

Each New Algorithm Forces More Human Innovation

For every leader today, success in building tomorrow’s strategy must begin by exploring the relative value-added of machine versus people in doing work—and then deeply understanding the boundary where the utility of algorithms stops and human effort still matters.

That search continues a process traceable to the dawn of civilization (as nicely explained in Philip Auerswald's Code Economy). Since earliest time, man’s knowledge discoveries have been progressively turned into tools and technology, at each stage creating a platform of codified intelligence on which the next phase of human endeavor then improves. Cave symbols allowed for writing, which in turn spawned printing, which then led to industrial machines. And then on to calculators, computers, algorithms. With every inflection point, a new need—and opportunity—emerges for the next phase of human creativity to develop. Each new smarter machine impels yet another S-curve of even smarter human work.

The cycle continues today.

So pay attention to it. The final and deeper lesson of a small historical event called World Series 2017 is to think intentionally, now more than ever, about the ever-shifting boundary between codified knowledge and the spirited creativity of people—in whatever you do. The more acute your understanding of that boundary and the more clever you are in finding new ways to bridge it, the more likely you are to hit a really big home run.