Seven Steps To Imitating The Business Best Practices Of Sports Champions

Philadelphia Eagles celebrate their Super Bowl LII win with the city of Philadelphia (AP Photo/Christopher Szagola)

I recently caught up with Mike Macrie, the CIO of Land O’ Lakes, and a former winner of the Forbes CIO Innovation Award in his office in the northern suburbs of the Twin Cities. He grew up a Philadelphia Eagles fan, and had just watched his team beat the New England Patriots in Super Bowl LII in his adopted hometown of Minneapolis. He mentioned that some attributed the Eagles’ success to data analytics.  It brought to mind that the same has been said about the champion Golden State Warriors, and I wrote about the same topic relative to the World Series champion Houston Astros.

Macrie is a former starting center fielder for Cornell University and a sports junkie, by his own admission. As we spoke a bit more about the topic, it brought to mind the source of the data analytics revolution in sports, which many trace back to Michael Lewis’ profile of Billy Beane and the Oakland Athletics in his classic book,Moneyball: The Art of Winning an Unfair Game. I raised a question to Macrie that I had been contemplating for a while: was it ultimately to Billy Beane’s advantage to have been featured in that book? Sure, his legacy is etched in stone as an innovator, and Brad Pitt played him in the movie based on the book. That’s not bad, but the Athletics have yet to win a championship under Beane. 

Billy Beane, Brad Pitt and Jonah Hill attend Film Independent's screening of 'Moneyball'. (Photo by Alexandra Wyman/WireImage)

The book was published in June 2003, and that season, as well as three prior season, the team made the playoffs all four years, and averaged 98 wins across that span. In the 11 seasons between 2007 and last season, however, the team has only had three winning seasons. Part of the reason for the turnaround is that most baseball teams and indeed most sports teams have imitated what worked well for the Athletics. The book offered insights into the key role that Paul DePodesta, a Harvard educated statistician played in building the models that would turn the low-budget Athletics into winners. Now most sports teams have statisticians or data analysts. A year after the book came out, DePodesta was named the General Manager of the Los Angeles Dodgers.

What worked for one small-market team spread to all teams remarkably rapidly. But why is there such disparity in the data analytics capabilities of major businesses? Why do some advance their practices while others remain on the first or second rung of the ladder to climb to better performance. Digital native organizations such as Amazon, Google, Facebook, and Netflix have extraordinarily powerful data analytics that are at the heart of their power and influence. Some digital immigrant companies, like Land O' Lakes, have successfully crossed the chasm to imitate the magic of the digital natives, but many have yet to do so, however.

One of the reasons that sports teams can do this so rapidly is that they are all mid-sized businesses, most of which garner nine-figure revenues. Their business operations are small and nimble, and can change more rapidly than big businesses with billions of dollars in technical debt that stand in the way of being nimble. Macrie hypothesized that sports teams have better data to prove the success or failure of methods by virtue of their being roughly 30 direct competitors and a single winner each year. “With such clarity, there is that much more of an incentive for good ideas to spread,” he noted.

There are a few things that companies can do to emulate the success of sports champions. It is necessary to modernize the people practices, the processes, and the technologies of the enterprise. These can be multi-year journeys for many executives, as Rob Carter of FedEx told me in an interview about his company’s extraordinary transformation.These are long, complex sets of activities.

First, incorporate learning agility into your culture, so that you are hiring a team that will be willing to continue to remain abreast of new practices and technologies that come into being. Second, it requires more agile processes, as well, which fosters better experimentation, which is required for innovation. Third, and also from a process perspective, it also requires implementing enterprise architecture to document how data flows, and which technology it flows through, among other benefits. This helps facilitate simplification of the technology footprint, and better use of data. Fourth, your company should pursue a cloud first architecture to variabilize the cost structure of IT, and to enhance the flexibility of technology, as well. Fifth, it is important to leverage microservices. (For better background into microservices, please read my introduction to the topic with Matt Miller of Sequoia.)Microservices ensure that changes to any technology minimize the impact to the broader portfolio of technology.

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Philadelphia Eagles celebrate their Super Bowl LII win with the city of Philadelphia (AP Photo/Christopher Szagola)

I recently caught up with Mike Macrie, the CIO of Land O’ Lakes, and a former winner of the Forbes CIO Innovation Award in his office in the northern suburbs of the Twin Cities. He grew up a Philadelphia Eagles fan, and had just watched his team beat the New England Patriots in Super Bowl LII in his adopted hometown of Minneapolis. He mentioned that some attributed the Eagles’ success to data analytics.  It brought to mind that the same has been said about the champion Golden State Warriors, and I wrote about the same topic relative to the World Series champion Houston Astros.

Macrie is a former starting center fielder for Cornell University and a sports junkie, by his own admission. As we spoke a bit more about the topic, it brought to mind the source of the data analytics revolution in sports, which many trace back to Michael Lewis’ profile of Billy Beane and the Oakland Athletics in his classic book,Moneyball: The Art of Winning an Unfair Game. I raised a question to Macrie that I had been contemplating for a while: was it ultimately to Billy Beane’s advantage to have been featured in that book? Sure, his legacy is etched in stone as an innovator, and Brad Pitt played him in the movie based on the book. That’s not bad, but the Athletics have yet to win a championship under Beane. 

Billy Beane, Brad Pitt and Jonah Hill attend Film Independent's screening of 'Moneyball'. (Photo by Alexandra Wyman/WireImage)

The book was published in June 2003, and that season, as well as three prior season, the team made the playoffs all four years, and averaged 98 wins across that span. In the 11 seasons between 2007 and last season, however, the team has only had three winning seasons. Part of the reason for the turnaround is that most baseball teams and indeed most sports teams have imitated what worked well for the Athletics. The book offered insights into the key role that Paul DePodesta, a Harvard educated statistician played in building the models that would turn the low-budget Athletics into winners. Now most sports teams have statisticians or data analysts. A year after the book came out, DePodesta was named the General Manager of the Los Angeles Dodgers.

What worked for one small-market team spread to all teams remarkably rapidly. But why is there such disparity in the data analytics capabilities of major businesses? Why do some advance their practices while others remain on the first or second rung of the ladder to climb to better performance. Digital native organizations such as Amazon, Google, Facebook, and Netflix have extraordinarily powerful data analytics that are at the heart of their power and influence. Some digital immigrant companies, like Land O' Lakes, have successfully crossed the chasm to imitate the magic of the digital natives, but many have yet to do so, however.

One of the reasons that sports teams can do this so rapidly is that they are all mid-sized businesses, most of which garner nine-figure revenues. Their business operations are small and nimble, and can change more rapidly than big businesses with billions of dollars in technical debt that stand in the way of being nimble. Macrie hypothesized that sports teams have better data to prove the success or failure of methods by virtue of their being roughly 30 direct competitors and a single winner each year. “With such clarity, there is that much more of an incentive for good ideas to spread,” he noted.

There are a few things that companies can do to emulate the success of sports champions. It is necessary to modernize the people practices, the processes, and the technologies of the enterprise. These can be multi-year journeys for many executives, as Rob Carter of FedEx told me in an interview about his company’s extraordinary transformation.These are long, complex sets of activities.

First, incorporate learning agility into your culture, so that you are hiring a team that will be willing to continue to remain abreast of new practices and technologies that come into being. Second, it requires more agile processes, as well, which fosters better experimentation, which is required for innovation. Third, and also from a process perspective, it also requires implementing enterprise architecture to document how data flows, and which technology it flows through, among other benefits. This helps facilitate simplification of the technology footprint, and better use of data. Fourth, your company should pursue a cloud first architecture to variabilize the cost structure of IT, and to enhance the flexibility of technology, as well. Fifth, it is important to leverage microservices. (For better background into microservices, please read my introduction to the topic with Matt Miller of Sequoia.)Microservices ensure that changes to any technology minimize the impact to the broader portfolio of technology.

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