The beginning of March also signals the start of preseason exhibition games of Major League Baseball, known as Spring Training, in winter-free Florida and Arizona. March also brings around one of the most famous analytics events: the Sloan/MIT Sports Analytics Conference. When it was first held 10 years ago, nearly all the content focused on baseball. This is understandable; the sport has a special place in the hearts of those inclined towards math and numbers. Children for decades have collected baseball cards, a pocket sized paper database of key statistics of their favorite major league players. The game is easily the most data friendly sport. Businesses would be thrilled to have this level of data capture - hundreds of discrete events per game - and a long history of accurate data, from over 2,000 games a year for over a century. When computing power exploded in the 1990s, it was no surprise that Major League Baseball became the vanguard of analytics in sports.
The most famous story about analytics in baseball, Moneyball, introduced to the public the growing use of data analytics in making decisions. The Oakland Athletics, a low budget franchise, began using statistics and data mining to find undervalued players. Those who have seen the movie or read the book know that the dramatic tension came from old guard skepticism of this new data and analytics based approach. The false paradigm dramatized in Moneyball pitted a winner-take-all struggle between old school talent scouts and the new school geeky computer nerds using analytics.
Since the book was published, some of the media classified organizations into these two paradigms of “old school” and “new school”. All the while, many teams have been investing heavily in analytics. Recent MLB champions supposedly have a very traditional scouting heavy "old school" approach. The San Francisco Giants have won 3 of the last 5 World Series titles and were hailed as old school; very quietly, the team closest to Silicon Valley created an analytics department that merged neatly into baseball operations. They expanded their scope through successful initiatives around fan experience. The perennial loser Kansas City Royals which went for 20 years without a winning season invested in an analytics team, with player predictive analytics a key focus. They built a team with an uncommon profile and great defense, and have won the last 2 AL pennants and the 2015 World Series.
Amongst more recent poor performers, new leadership of the Chicago Cubs and the Houston Astros invested heavily in analytics around player development and improving in game performance. Major League Baseball’s recent investment in telemetric systems has created reams of new data. Through analytics of PITCH F/X, which tracks ball movement and speed, the two teams have done especially well at helping middling pitchers change their approach and pitch breakdown. The Cubs acquired Jake Arrieta from Baltimore in a trade, and identified that his slider should be used more. Houston looked at Dallas Keuchel to change his approach to match their innovative defensive shifting strategies. Both pitchers, middling a few years back, won the Cy Young Award in 2015 in their respective leagues. Both teams made the playoffs for the first time in a decade.
Still, the skeptic would say, should the coaches be the ones to implement and work with the analytics people? As in business, it comes down to cultural buy-in and systems. These firms have employed coaches willing to merge their long baseball resumes with new data from analytics. The role of the analytics group within baseball clubs work at any level of the organization with strong support from senior leadership, this may sound familiar too. The actual challenge comes from the best way to merge approaches: how do we integrate analytics into existing operations?
Among all the MLB teams, one dinosaur persisted. My favorite club, the Philadelphia Phillies, had a decade of success through traditional scouting and player development. In the last 5 years, they went from the best record to the worst record in the sport. Their player development pipeline dried up, as young prospects failed more often than not; they tried to make it up through spending on the open market, but failed there as well. They failed to recognize the industry had done a better job with predicting successful prospects and pricing contracts better.
We see this in our practice and in our clients. Businesses are reluctant to develop the capability because it doesn’t fit into their current operating model, and think they are doing fine with the old methods. However, it is probably a good idea to start investing early, even if the business is doing well, in order to gain deeper knowledge into how the business works and experience in new technologies. The investment in analytics often pays off in unexpected ways. You can see this in particular in retail, where many old school retailers are scrambling to catch up with Amazon.
Sure enough, this offseason the Phillies changed tack with their management, hiring an executive level “nerd” from Google to partner with strong existing baseball guys. The Moneyball revolution is complete in baseball. The question remains: for your industry, are you on the path to analytics success?
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