On May 18, 2018, Sergio Romo, a lifelong relief pitcher, was the Tampa Bay Rays starting pitcher. He also was the starting pitcher the next day. These were his first starts after 588 appearances as a reliever over his 10 year career. Baseball norm is that a team hopes to have their starting pitcher pitch 6 innings, every 5 days. But in this case, Mr. Romo pitched a scoreless first inning on May 18 and another scoreless inning the next day, and that’s it. And with that, the Rays had used its closer to introduce the role of the “opener” to major league baseball. How did we get here?
It's about the data. The data told the baseball analytics guys that batters have dramatically more success against a particular pitcher the third time they face him. The opener concept is simple – use a pitcher most likely to get the opponent’s toughest hitters out at the beginning of the game, and then hand the ball off to another pitcher who may pitch more innings but will not have to face a batter more than twice. This is just one of many examples of how baseball is taking a data-driven approach to analyzing results, evaluating players and devising new strategies.
DI Lesson 1: Distribution Intelligence (DI) needs a seat at the table.
Sabermetricians, i.e., those who use data in the search for objective baseball knowledge, have risen from the windowless back room to the front office and are now often found running teams. Similarly, to harness the full potential of the analytics, DI should not be viewed as a support function, but rather as a key strategic partner and influencer in the Distribution Strategy process. Data scientists are a critical part of the team, but DI is a business function as it is essential that DI leadership understands distribution in order to define the best applications of data insights.
I had the opportunity to speak with Chaim Bloom recently about his transformation experiences. He is the Chief Baseball Officer for the Red Sox and one of the key architects of the Rays exploitation of data. He noted that the Rays did not invent the concept of the opener. What they did do was analyze the data and prepare a compelling case for those who would carry out the novel concept. Getting the manager and pitchers onboard in a sport that has a history of resistance to new ideas was crucial. Bloom also talked about being fortunate that the opener concept was successful when it was first introduced. If it wasn’t, he hopes they would have had the conviction to stay with the concept until an appropriate sample size was attained.
DI Lesson 2: Getting Buy-In is the Art that complements the Science.
Crunching the data to derive an insight is often the easy part. Executing on that insight? For those of you who have tried to sell your top producing wholesalers on a new approach, you may know what I mean here. Start small. Pick your proof-of-conceptees wisely – ones open to new ideas, with the savvy to bring ideas into the marketplace and suggest tweaks, and who will serve as an internal advocate of a successful approach. Even consider compensation-neutral assurances. The key is to construct what Olmstead calls a DI3 model – one that turns Data into Insights into Impact using an Iterative learning process.
In Part 2 of Sergio Romo and Distribution Intelligence, we will offer DI Lessons #3 and #4, sharing perspectives on how DI can help an asset manager bend its cost curve and also on how DI should be strategically positioned within the distribution architecture to enable a more holistic way of thinking across the client journey.
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