Big Data

How big data won the 2017 World Series

Learn what the Houston Astros did to win the analytics arms race in Major League Baseball.

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Image: Getty Images/iStockphoto

On Wednesday, the Houston Astros won the 2017 World Series. Sports Illustrated predicted it three years ago. It was a slightly tongue-in-cheek prediction at the time, but it wasn't random. It was based on the Astros' prowess with data.

When the book Moneyball: The Art of Winning an Unfair Game came out in 2003, it spurred every Major League Baseball team to start taking data-based decision making more seriously. But some took it more seriously than others. Both the Astros and the Los Angeles Dodgers, which the Astros beat in the seventh game of this year's World Series, are big believers in data-driven analytics.

In 2011, Jim Crane purchased the Astros. That year marked the first time in the franchise's history that it lost 100 games in a single season. Crane's first move was to completely overhaul the team's structure with a focus on hiring experts to make data-driven decisions based on predictive analytics. And by the time Sports Illustrated featured the team on its now-lauded June 2014 cover, the team was on its way to greatness.

SEE: Field of digital dreams: Why MLB is betting its future on big data, Wi-Fi, apps, and AR (TechRepublic)

The Astros team was full of young players in 2014. But they were in a position to take it all within a few years as Sports Illustrated explained in its in-depth article. The team began using data analytics for both player assessment and in-game decisions and that made the difference and pushed them to the World Series.

"They really committed to a true teardown transformation," said Vernon O'Donnell, senior vice president of data operations for STATS, which is the official data provider for MLB.

Every team in the league is now using analytics to some degree, but the Astros, along with the Dodgers, the Boston Red Sox, the New York Yankees and the Chicago Cubs, are diving deeper than others on data. And it's working.

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The June 2014 cover of Sports Illustrated.

Image: Bloomberg Businessweek

"Data analytics played a key role in the Astros win (and the Dodgers' strong performance in 2017 as well). From player scouting, which has developed a much more sophisticated understanding of player value to analytical tools that help managers make smarter on-the-field calls around lineups, relief pitching and field position, the game looks decidedly different in 2017 than it did just a few years ago," said Kyle Bunch, managing director of strategy for R/GA Austin, which works on data strategy with the Dodgers as well as companies such as Verizon and Samsung.

And the Astros win will only accelerate the push for deeper dives into data analytics.

"Teams that aren't investing heavily in analytics have absolutely missed the boat. You see it not just in the Astros, but the Cubs last year and the Red Sox. The Yankees are a classic example. By investing in that way, it's changing how baseball teams are playing. They're getting more home runs, and deeper home runs," O'Donnell said.

Both the Astros and the Dodgers are known for their data analytic teams. Both are analytically driven and technically driven as well, which means making sure a team invests in the right resources from a technical perspective, said John Pollard, vice president of business development for Zebra Sports.

O'Donnell pointed out that data analytics is so important to the Astros that his company alone has lost six employees to the Astros in recent years.

SEE: Going Deep on Big Data (a ZDNet/TechRepublic special report)

"The Astros have really invested in data analytics talent. There are other teams who dabble in analytics work with data packages and video and a basic understanding of what it means, but they haven't built a robust internal team that can think about what it means to win in Houston as an Astro," O'Donnell said.

Bill Schmarzo, CTO of Dell Global Services, Big Data, said the Astros won based on the team's ability to use data to its advantage. "Their success in drafting high potential players like Alex Bregman, Carlos Correa, Dallas Keuchel and World Series MVP George Springer, plus the acquisition via the International Free Agency of Jose Altuve and Yuli Gurriel is a testament to the quality of their scouting analytics to assess and predict talent. Plus their in-game decisions—where to position players on the field, when to pinch hit, pitcher rotations—were also stellar. But analytics is not about being perfect, analytics are about being better. Analytics is about helping player development, managers and coaches to make better decisions; better decisions than what your competitors are making."

Data is so important to teams that they've even been caught stealing from one another. In 2016, CBS News reported that a scouting director of the St. Louis Cardinals had pled guilty to hacking into the player personnel database of the Houston Astros.

Moneyball has taught teams another lesson. "Data and analytic advantages are fleeting. It doesn't take organizations long to copy what others are doing. So to stay ahead, one has to constantly be innovating and looking for new sources of data and new analytic techniques. It's an analytics arms race out there," Schmarzo said.

The Astros have figured out how to integrate "soft" data with "hard" data, Schmarzo said.

"Baseball has always been blessed with lots of hard, statistical performance data. Heck, Sabermetrics has been trying to make sense of that bounty of hard data for decades. What leading organizations are doing now is incorporating the soft metrics—metrics like resilience, perseverance, heart and chemistry—into their analytics. And the source of these soft metrics is the all-important scouting and coaching staffs. They can purposely place players in stressful situations, and then see (measure) how they react. That's the real power—integrating data analytics with human intuition. The result will be better decisions," Schmarzo said.

SEE: Big data policy (Tech Pro Research)

Still, data analytics remains as much of an art as a science—such as determining which player to put in during a hitting streak, or keeping a pitcher in when he's doing well, said Vijay Mehrotra, associate professor at the University of San Francisco and an analytics consultant.

"Statistical models are based on long term averages. Overall you look at the data and how it compares to long term predictions," Mehrotra said. But in the heat of a game, a manager must make decisions based on what he sees on the field.

In this year's World Series, "you saw two sharp contrasts. The Dodgers were a team that was very much bought. It's the highest paid team in baseball. The Astros were much more data driven. They had to be because they didn't have the benefit of the huge payroll. That's your Moneyball parallel," Mehrotra said.

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About Teena Maddox

Teena Maddox is a Senior Writer at TechRepublic, covering hardware devices, IoT, smart cities and wearables. She ties together the style and substance of tech. Teena has spent 20-plus years writing business and features for publications including Peo...

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