4 Analytics Lessons From Professional Sports

Nate Silver, Michael Lewis, Mark Cuban and other sports luminaries share analytics advice you can apply to your business.

Doug Henschen, Executive Editor, Enterprise Apps

March 4, 2013

8 Min Read

Want a career in data-driven decision-making? Nate Silver, the famously accurate predictor of the 2012 presidential election, advises neophytes to "run a fantasy baseball team or an NCAA Tournament pool" to learn applied statistics.

"Sports is a pure laboratory, and there are very good data sets available, especially in baseball and basketball," Silver said during a panel discussion on Friday at the Seventh Annual MIT Sloan Sports Analytics Conference in Boston. "There is luck involved in who wins and who loses, but you have good criteria for measuring success, so you can test ideas, make predictions and see how well it works."

Silver is the author of the FiveThirtyEight blog on NYTimes.com. But those who have him pegged only as a politico don't realize that he got his start in statistical analysis through sports, as the developer of the Player Empirical Comparison and Optimization Test Algorithm (PECOTA) system for forecasting the performance and development of Major League Baseball players. He sold that business to Baseball Prospectus in 2003 and continued to manage it until his political blog took off.

[ Want more on the use of analytics in sports? Read Analytics Drives Next Generation Of Moneyball In Sports. ]

It was fitting that the panel moderator was Michael Lewis, author of the seminal 2003 book Moneyball, which chronicled the pioneering use of data analytics in baseball by the Oakland Athletics. Moneyball also helped shine a light on the use of analytics in other sports, as championed by co-panelists Mark Cuban, owner of the NBA's Dallas Mavericks; Paraag Marathe, chief operating officer of the NFL's San Francisco 49ers; and Daryl Morey, head of basketball operations for the NBA's Houston Rockets.

What advice did these analytics pioneers have to share on numbers-driven decision-making? Here are four themes that apply no matter what industry you're in.

1. Stick With It.

Major League Baseball has been steeped in statistics for generations, but that doesn't mean teams, coaches and scouts were receptive to numbers-driven decision-making. Lewis recounted how shocked he was by how controversial Moneyball was when it came out and by how angry baseball traditionalists were about the book.

"They were angry because it was costing people their jobs," Lewis said. "Moneyball made the argument that the general manager, for example, might be relatively more important than the coach." Silver recalled that when he attended the winter MLB meetings in 2003 right after Moneyball was published, "it was like the Sharks versus the Jets" – that is, two rival gangs: one group embracing analytics and the other clinging to their experience, instinct and intuition. As numbers have proved their worth, however, acceptance has grown.

"The business has gradually realized that almost all your return on investment in baseball comes from drafting and developing young players who then get priced substantially above the market rate," Silver said. "The scouts use stats as a baseline now on which you improve. All these players have stat lines, and it helps you know that one player is a lot more likely to be successful than another." Another sign that the baseball business has embraced statistics is the fact that Sabremetrics -- stats compiled by the Society for American Baseball Research and used by analytics advocates -- are "light years ahead of where they were even five years ago," Silver said.

The lesson here is that perseverance pays off, and that proven stats will breed demand for more breakthroughs in data analysis.

2. Gather More Data.

In any field, you encounter diminishing returns as you add complexity to analytic models, Silver noted. Yet even in baseball, with its seemingly endless array of stats, "we're not at the point where the possibilities have been exhausted," he said. Relatively new scouting data sets have yet to be exploited.

"For example, we now know all about the break on a pitching prospect's curve ball, and we have years of that data now so we can go back and see how predictive" those characteristics might be, Silver said.

Silver was referring to video-captured data showing the coordinates of the pitcher, his arm, the ball and ball movement in multiple dimensions in every pitch during every game. Baseball is in the process of adding similar multidimensional fielding data that will help show, for example, the difference between players who truly hustle and sometimes get charged with errors versus error-free players who are playing it safe and allowing more hits per chance.

The NBA is working on similar player-tracking data, and at least 13 teams are installing the six special cameras required throughout a stadium to track the position of each player, the ball and each and every shot, rebound and steal during every second of every game. That setup obviously adds up to the collection of really big data, and professional basketball has just begun to discover what it can do with that information, said Morey, the Houston Rockets GM.

"Today we're nowhere in terms of use of analytics to determine how the game is played on the court," Morey said. "Once we get this [player-tracking data], you're going to be able to find patterns in play and be able to exploit inefficiencies. What teams will run offensively and defensively in 10 years will be completely different." 3. Master The Art Of Communications.

Marathe, the 49ers COO, entered the sports world when the legendary, late 49ers GM and head coach Bill Walsh hired him to develop an algorithm to calculate the value of football draft picks. That was in 2001, and mirroring the rise in analytics in sports over the last decade, Marathe rose in the team's ranks, eventually taking on salary-cap management, contract negotiations and all player personnel decisions.

The NFL hasn't been as advanced as MLB or the NBA in the use of analytics, Marathe said, so he had to work that much harder to communicate the value of the data-driven decisions. "It's threatening to people if they're not comfortable with analytics and some guy shows up with all these charts and graphs telling you why you need to do things differently," Marathe explained. "After five or six years, I realized that the analytical work that you do is less than 50% of the challenge. The hard part is communicating your analysis so people believe in it and embrace it."

[ How does pro basketball share its data? Read NBA Launches SAP Hana-Powered Basketball Statistics Site. ]

Getting buy-in from the owner, head coach and scouts, Marathe said, required him to constantly communicate and shape ideas so that they eventually became collective, group decisions and not just ideas from "the little Indian guy with the charts."

It's a lesson that aspiring analytics professionals in any industry must learn. Indeed, in InformationWeek's recent examination of "Top Big Data Analytics Masters Degrees," we found that courses on communicating with stakeholders are typically required and extensive.

4. Look For The Next Frontier.

The embrace of analytics isn't a once-and-done deal. The push for new measures and analyses in baseball didn't stop with the on-base percentage stat favored by Oakland A's GM Billy Beane, and it shouldn't stop with one or two hot stats in your industry.

Cuban, the maverick Dallas Mavericks owner, said basketball has "just scratched the surface" of data analyses. "We're looking to extend data capture not just in-game but also in practices and in training," Cuban said. "Unlike baseball, we have a much more difficult time developing talent, so we have to look at what can we do to gather more information" on up-and-coming players.

Another new application in basketball and other sports is in sports medicine. "It's not just for injury avoidance, but for optimal use of treatments," Cuban said. "We're doing genetic testing to determine what the best anti-inflammatories are so guys can play more minutes and play in more games."

In football, Marathe pointed to player endurance, injury prevention, player mental aptitude and in-game strategy as uncharted or nascent areas of data analysis. Another area is analysis of team chemistry, "making sure that the offensive side and the defensive side have complementary skill sets," he said. "Individually the offense and defense might be really good, but when you put them together, they might not mesh."

As Cuban observed, a city won't hold a parade for you after you rack up a really good year in business, but everyone is looking to boost performance and results. Use these pointers to improve your game.

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About the Author(s)

Doug Henschen

Executive Editor, Enterprise Apps

Doug Henschen is Executive Editor of InformationWeek, where he covers the intersection of enterprise applications with information management, business intelligence, big data and analytics. He previously served as editor in chief of Intelligent Enterprise, editor in chief of Transform Magazine, and Executive Editor at DM News. He has covered IT and data-driven marketing for more than 15 years.

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