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What Sports Analytics Can Teach Us About Integrating AI Into Care-Special Issue:The HSB Blog 1/24/22

Note: Several weeks ago, our founder, Jeff Englander presented on a panel at the AI Summit NY entitled, “Operationalizing AI in Healthcare-Bringing Analytics and AI Into the Clinical Setting”. One of the topics of the panel was how to increase clinician’s interaction with AI technologies. During the panel, he referenced an article he had written in May of 2018 on “What Steph & LeBron Can Teach Business About Analytics” and how sports demonstrates many practical ways to gain acceptance and integrate analytics into an organization. Given an incredible weekend of playoff football, sorry to all our friends in Nashville, and the number of ensuing debates about sports analytics, we thought it might be timely to reprint it here:

As I sat watching the NBA conference finals last night, I began thinking about what I had learned from the MIT Sloan Sports Analytics conferences I went to over the last several years. I thought about how successful sports had been and the NBA in particular in applying sports analytics and what lessons businesses could learn to help them apply analytics to their businesses.

Five basic skills stood out that sports franchises had been able to apply to their organizations that were readily transferrable to the business world: 1) intensive focus; 2) deep integration; 3) limited analytic “burden”; 4) trust in the process; and, 5) communication and alignment.

1) Intensive focus - when teams deploy sports analytics they bring incredible focus to the task. One player noted that they do not focus on how to stop LeBron, not even on how to stop LeBron from going left off the dribble, but instead how to stop LeBron from going left off the dribble coming off the pick and roll. This degree of pinpoint analysis and application of the data has contributed to the success and continued refinement of sports analytics on the court, field, rink, etc.

2) Deep integration - each of the analytics groups I spoke with attempted to informally integrate their interactions into the daily routines of players and coaches through natural interactions (the Warriors analytics guy used to rebound for Steph Curry at practice). Analytics groups worked to demystify what they were doing and make themselves approachable. The former St. Louis now L.A. Rams analytics group jokingly coined its office as the “nerds nest”. By integrating themselves into the player’s (and coach's) worlds they were able to break down stereotypes and barriers to acceptance of analytics.

3) Limited analytic “burden” - teams’ data science groups noted given the amount of data they generate it’s important to limit the number of insights they present at any one time. One group made it a rule to discuss or review no more than 3 analytical insights per week with players or coaches. This made their work more accessible, more tangible to players/coaches and helped them quantify the value to the front office.

4) Trust in the process - best illustrated by a player who told the story of working with an analytics group and coaches to design a game plan against an elite offensive player which he followed and executed to a tee. But that night the opposition player couldn’t be stopped and in the player’s words "he dropped 30 on me". The other panelists pointed out that you can’t go away from your system based on short-term results. As one coach noted ‘don’t fail the plan, let the plan fail you…Have faith in the process.”

5) Communication and Alignment - last but not least teams stressed the need to be aligned and to communicate that concept clearly all throughout the organization. As Scott Brooks, at the time the coach of the Orlando Magic noted, “we are all in this together, we have to figure this out together”. Surprisingly, at times communication was paramount even for the most successful and highly compensated athletes. For example, at last year’s conference, Chris Bosh a 5x All-Star and 2x NBA Champion, making $18M a year at the time he was referring to, lamented the grueling Miami Heat practices during their near-record 27-game winning streak in 2013, seemingly despite their success (at the time the 2nd longest winning streak in NBA history). When I asked him, what would have made it more bearable, he said communication, just better communication on what they were trying to do.

Clearly, professional sports have very successfully applied analytics to their craft and there are a number of lessons that businesses can copy as they seek to gain broader and more effective adoption of analytics throughout the value chain.


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