Baseball has been a harbinger of the recent explosion around relying on analytics to drive decision making. “Moneyball”, written by Michael Lewis, is the story of the 2002 Oakland A’s and how their General Manager, Billy Beane, leveraged predictive analytics to compete against richer competitors in Major League Baseball (MLB). He used data to discover that on-base percentage(OBP) was a key factor in a baseball team’s success. He also had an insight that OBP was not something that was valued in the market. Fortunately, he was in a position to capitalize on that insight by changing the way he spent money, thereby building a team that was cheaper but still successful. Since that time, the use of analytics and statistics in baseball has completely changed how players are valued and how baseball teams are run and games are managed. As an avid Cubs fan, I was fascinated by the successful use of this same philosophy by the Cubs starting back in 2011. Theo Epstein joined the Cubs in 2011 as President of Baseball Operations and brought many of these “Moneyball” principles to the organization and, along with manager Joe Maddon, another proponent of data and analytics, went on to win the 2016 World Series.
Right now, analytics and data, along with cloud, are the biggest drivers of innovation and new technology across all industries. Big data, IoT, data lakes, real-time data streams, and machine learning are being discussed daily amongst CIOs and recent college graduates. With data quickly becoming a commodity, data science moves to the forefront as companies try to develop unique insights for their company. The challenge is often around what to do with the insights. You looked at the data, you have an idea about what the data is telling you to do, so what happens next?
The 2016 Cubs leadership team used data that was widely available and derived their own insights to gain an advantage in finding players and, eventually, winning games at the major league level. Ken Rosenthal’s book, ‘The Cubs Way’, describes many examples interwoven with the story of the 2016 World Series. There is one story that highlights the challenge of reacting to what the data is telling you. It is one thing to get the data and see the pattern, but if you can’t move quickly to take advantage, the value of the insight is greatly reduced.
B.A.T.S., by Sydex Sports, is a video software system that tracks the at-bats across MLB. Many teams, including the Cubs, used the software and had access to the same data. The Cubs developed scouting reports and pitching strategies based on how they thought the data applied to the skills of their team. Each club would naturally develop different insights into this data, applying unique experience and skills to develop their own plans. The Cubs went one step further, though, and installed their ‘secret weapon’: catching instructor Mike Borzello. Borzello would work with the data team and the pitching coach Chris Bosio to develop the game plan based on the data from BATS. Mike would then sit in the dugout and talk to the catchers every inning. His presence gave the Cubs the ability to adjust based on what was happening in the game, or just reinforce earlier plans. The team did not simply rely on the initial plan, but instead The Cubs changed the way they operated to take full advantage of their insights. By putting Borzello in the dugout and having a manager that would consult him, they were able to make a more informed decision in real time. The Cubs did not simply add analytics as a data point into the same process (in game decisions by the manager), instead they developed a whole new way of working to get as much value out of the data as possible.
That is the challenge to every company as the move to predictive analytics gains more and more momentum. Data will produce insights that will require a change in direction. Will your company be able to move beyond the ‘way we’ve always done it’ in order to take advantage?