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What Baseball Can Teach You About Using Data to Improve Yourself

It seems like every business is struggling with the concept of transformation. Large incumbents are trying to keep pace with digital upstarts., and even digital native companies born as disruptors know that they need to transform. Take Uber: at only eight years old, it’s already upended the business model of taxis. Now it’s trying to move from a software platform to a robotics lab to build self-driving cars.


An athletic mediocrity by most Major League measures, Trevor Bauer became a $13 million-a-year All-Star pitcher thanks to his singular talent: a relentless, data-driven focus on self-improvement.

“I wasn’t a natural born athlete,” Bauer told a Sports Illustrated reporter in August 2011. “I’m not that strong. I’m not fast. I’m not explosive. I can’t jump.” So how was he selected third overall in the major league draft? “I was made.”

Bauer’s declaration captures the credo and ethos of The MVP Machine: How Baseball’s New Nonconformists Are Using Data To Build Better Players, a recent book by sports-nerd journalists Ben Lindbergh and Travis Sawchik. Even MBAs who don’t know what an ERA is — or who hear it and think of the Equal Rights Amendment — will grasp the book’s essential message: next-generation technologies and analytics radically transform top-tier talent development and technique. “This new phase is dedicated to making players better,” they write. “It’s Betterball. And it’s taking over.”

From accelerometers to wind tunnels to high-speed, high-resolution digital cameras, the data for getting better at getting better is getting better. With personalized data, analytics, and training, motivated mediocrities can literally become All Stars. That insight is far bigger than baseball.

Bauer wouldn’t have won a second glance, let alone a multi-million dollar contract, without cheap tools for computational introspection and training. He’s the symbol and substance of the “quantified self” revolution, obsessively measuring everything, from arm speed to ball spin, that might better performance. Bluntly, he’s what the future of world-class professional development now looks like — and not just on the pitcher’s mound.

That’s why The MVP Machine often reads more like a digital disruptor’s user manual than Moneyball 2.0. Chock-full of contrarian characters and detailed case studies, MVP describes what happens when committed competitors embrace data science with both a vengeance and a bottom-line emphasis on cultivating human potential. Any resemblance to other multi-billion-dollar, talent-driven industries is strictly intentional.

Just how ready, willing, and able are people to “Bauer-ize” themselves to win? The recent World Series pennants adorning the Cubs, Red Sox, and Astros clubhouses suggest a healthy injection of analytics enhances performance even better than steroids do.

Six key lessons emerge from MVP‘s narratives and interviews. Lindbergh’s perspectives here are especially intriguing because they’re informed by his previous book, The Only Rule Is It Has To Work, that told the story of his efforts (with a colleague) to bring sabermetric sophistication to the minor league Sonoma Stompers. (Their results were entertainingly mixed.) In MVP, he explicitly addresses the very player development issues he avoided as a minor league moneyballer. His thoughtful commentary deserves serious attention by leaders committed to cultivating high-performance data-driven talent.

Winners are data-driven, not data dilettantes.

Cutting sharply against established baseball culture, the 2017 World Series champion Houston Astros dramatically reduced their scouting staff, deciding that headquarter quants could deliver greater value than more people in the field. Traditionalists were shocked and angry, but the results speak for themselves. In another example, Red Sox pitching coaches relied on high-speed cameras and ball trackers to diagnose biomechanical delivery flaws that were diminishing the effectiveness of two of their best relief pitchers. Their simple remedy worked. The Red Sox won.

Gut feelings aren’t data. Quality data deserve deference; personal experience does not. As Lindbergh puts it, baseball’s most successful franchises commit to acting on their data and analytics. The data’s not there to justify or ratify existing decisions. Data must be actively and measurably used to learn what’s better or best. Analytics should clearly impact game-day decisions and choices. The Trevor Bauers aggressively seek out new data to drive their improvement. If granular data isn’t continuously driving team and player performance development, something is wrong.

Winners invest in growth, not just efficiency

“This is a new phase of analytics,” says Lindbergh. “Describing the difference between Moneyball [published in 2003] and us is like describing the difference between a value stock and a growth stock.”

He argues that today’s high-performance talent markets make Moneyball’s buy-and-hold strategies inferior to analytic augmentation. Where earlier sabermetricians combed stats to spot player value inefficiencies and mispricings, “that strategy soon got co-opted by every other team.” And, while assembling portfolios of underappreciated talents worked as a buy-the-numbers transaction, it didn’t as sustainable growth-oriented investment.

Moneyball 1.0’s success did profoundly shift major league baseball’s analytic investment focus, however. MVP’s 2.0 approach celebrates human capital’s cultivation over its acquisition: improving underdeveloped players is increasingly a better bet than identifying undervalued ones. More importantly, says Lindbergh, growth-oriented investment philosophies are enormously appealing to players who, for both personal and professional reasons, want to become more valuable.

“Teams that have invested in development have made themselves more attractive to players,” Lindbergh notes. “You will have a better shot of attracting talented and motivated players to your team.”

Winners empower – and measure – a culture of data-driven self-improvement.

As MVP documents, a growing embarrassment of technical riches — mobile phones, biomechanics software, and Edgertronic cameras (named for MIT’s famed “Doc” Edgerton, the genius high-speed photography innovator) — has made terabytes of new baseball data fast, cheap, and easy to process. And that computational self-awareness is essential to self-improvement.

Says Houston Astros GM Jeff Luhnow, “We know what every person is doing on the field at all times. We know what the bat and the ball are doing on the field at all times. We now have information we didn’t dream we’d have a few years back.”

Omnipresent “situational awareness” makes “self awareness” professional development’s new normal. KPIs are everywhere. Everyone now expects that information will not just help monitor everyday performance but measurably improve it. That requires players and coaches alike to be open to data-driven analytics and insights. They need recalibrated attitudes, as well as aptitudes, to work.

The same science-flavored self-improvement gurus who have dominated business best-seller lists — notably Grit’s Angela Duckworth, Growth Mindset’s Carol Dweck, and Deliberate Practice’s Anders Ericsson — are now required reading in clubhouses.

“They kept coming up in our conversations,” Lindbergh noted. “Teams have now assigned [those books] to their players and coaches. This is now part of their expectations.” Winning baseball franchises have reoriented not just their technologies but their cultures around facilitating self-improvement.

Winners relentlessly revisit and review the fundamentals

Perhaps MVP‘s biggest surprise for baseball fans and casual readers is just how little the sport knew about its fundamental physics and biomechanics. “There were too many things that were taken for granted that shouldn’t have been,” says Lindbergh.

Misbegotten “conventional wisdoms” didn’t just inhibit on-field improvement; they actually contributed to player injury. The mechanics of gripping, throwing, and releasing baseballs; the impact of weights (weighted bats and weighted balls) on training; how bat speed and angle really shaped the likelihood of a hit; how pitches actually rotate, spin and slide – these phenomena required serious scientific and technological analysis to understand.

The better the technologies got, for example, the clearer it became that baseball “best practices” around throwing fastballs were inefficient, ineffective, and wrong. The art, science, and biomechanics of Major League pitching had to be rethought. So did the medical and economic challenges of protecting and preserving the pitcher’s arm. Given how valuable, expensive, and fragile great pitching talent can be, the industry’s fundamental ignorance seems shocking.

MVP’s “new non-conformists” succeeded in subverting Major League Baseball’s unscientific shibboleths by refusing to take the fundamentals for granted. To the contrary, they embraced skepticism and technology with enthusiasm. In effect, they “hacked” the game they loved.

Kyle Boddy, one of MVP’s heroes, was a 27-year-old engineering student drop-out who worked at an Olive Garden. But he was baseball-crazy, launching an analytics blog and building his own DIY laboratory to test his unconventional theories about data-driven baseball. Boddy, who coached Little League on the side, bootstrapped his self-taught technical skills into Driveline Baseball, a modestly successful coaching clinic. Cleverly leveraging YouTube videos and CraigsList ads, his research attracted attention from ambitious players desperate for an analytic edge. His most important quantitative collaborator in this quest for insight: Trevor Bauer.

Of course, MVPs have been built as much by baseball’s professionals as by talented amateurs. But a ruthless rejection of received wisdom was common to both. Just as importantly, these innovators understand that technologies need to personalize, customize, and specialize their analytics for individual players. People must have the power to revisit, review, and revise their own fundamentals. They need the ability to reap the self-improvement benefits of self-quantification.

Winners explicitly seek to balance top-down and bottom-up innovation

Moneyball 1.0 was a top-down data-driven revolution, says Lindbergh; the organization’s own analytics determined value and efficiencies. MVP’s 2.0 approach, by contrast, represents a democratization of analytic innovation. While the Astros, Cubs, and Red Sox have built and effectively centralized their own MVP platforms, the Trevor Bauers have cultivated their own bespoke analytics and training regimens. Indeed, Bauer’s quantified-self sophistication dramatically outstripped that of the Cleveland Indians who signed him.

In an industry where the average salary exceeds $4.2 million a year, top talent has every rational incentive to invest in itself. Superstars and stars can, and do, hire their own analysts, trainers, and sabermetricans to sharpen their competitive edge. If a $300,000 data-driven self-improvement investment can help extend a $5.5 million contract by another year, the payback is obvious.

“We’re not far from all players investing in their own development,” says Lindbergh. “We already see this in professional golf… We’re getting closer to that point in baseball. If you do it right, it’s going to pay for itself many times over.”

The institutional challenge, of course, is what happens when individual initiative and innovation conflict with team standards and protocols. Aligning analytics and insights between teams, trainers, managers, and talent will be both a competitive opportunity and a cultural threat.

Winners deploy effective ‘conduits’

As potent and persuasive as MVP’s technological and analytic innovations may seem, they simply weren’t enough to win Major League hearts and minds on their own. Cheap high-resolution imagery or entrepreneurial geeks couldn’t close the deal, says Lindbergh. The key to successful was technology transfer from the geeks to former players who combined nerdy chops with sporting cred. Baseball culture explicitly privileged ex-jocks over quants; you had to have played the game. In the book, Lindbergh calls them “conduits.”

A Bauer and a Boddy only got a franchise so far. The paradigmatic conduit was Boston’s Brian Bannister, whose title at the Red Sox is VP of Pitching Development (really). His impact on the team’s pitching staff was enormous. “He was a pioneer, a trailblazer, and a trendsetter,” Lindbergh says of his immediate influence. “Suddenly every team was hiring their own Bannister.”

Bannister, who had made it to the Majors as a decent if not innovative pitcher, brought a professional photographer’s eye and technical sophistication to framing a pitch. “Everything I learned about pitching development, I learned from Ansel Adams,” he said, comparing “his process to Adams’s zone system, a technique for ensuring optimal film exposure and development.”

“I believe coaching baseball players is the same thing,” Bannister says. “Half of it’s art, it’s experience, it’s creativity, and then half of it is just knowing the pure science and knowing the data you’re working with and being able to manipulate it in the direction that will benefit the player the most.”

Because conduits are critical, baseball’s top franchises are making sure that MVP coaches in the organization are culturally compatible. As the Astros Luhnow told McKinsey, “We decided that in the minor leagues, we would hire an extra coach at each level. The requirements for that coach were that he had to be able to hit a fungo, throw batting practice, and program in SQL. It’s a hard universe to find where those intersect, but we were able to find enough of them.”

Lindbergh can’t help but observe that the analytic transformation he and Sawchik chronicled seems to be accelerating. Many of those “non-conformists” are now, one way or another, a major player in the sport. “So many people who were outsiders when we started were insiders when we finished,” he notes. ”The pace of adoption is kind of incredible.”

Yes, and not just for baseball.

Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed in

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