How baseball reminds us to pay attention to things that make us human

This is offroad a bit from thinking about community listening. But…it’s really not. It’s about paying attention to human moments. Stay with me here as I connect these dots.

I am not a huge baseball geek but am slowly growing to appreciate the sport’s unique complexity. And – like some of my other sports-enthusiastic friends – I can find interesting parallels between the challenges faced by a specific sport and analogous challenges faced in the non-sport world.

[Tiny but passionate rant here: No, “parallels” does not mean endlessly analyzing the bullshit put out by coaches or quarterbacks or leaders-of-championship-teams to discover golden secrets of success. Because, well, it’s bullshit.]

The analogous non-sport challenge that baseball faces is how to best integrate data and analytics into decision making and the quest for improved performance.

What caught my attention recently was an interview in The Athletic with Joe Maddon. If you are from where I’m from, you know Maddon as the manager of the Chicago Cubs team that won the World Series in 2016, thus ending 108 years of not winning the World Series. We also got to know Maddon as a quirky, smart, opinionated and innovative outlier. Definitely high on the list of folks you’d love to hang with over a few beers.

This interview deals with Maddon’s assessment of baseball’s fixation on data and analytics. There’s a lot in the interview – how this fixation is changing some of the dynamics in how coaches and managers work together, for example.

But what struck me was this one exchange. The context was a discussion about managers and decision making, but Maddon pinpoints a weak spot in analytics without rejecting it outright.

Maddon: “All these numbers are based on large sample sizes, and I understand that. To me, a large sample size is pretty much infallible when it comes down to acquisitions in the offseason. But it is fallible when it comes down to trends in the moment.

So when you’re talking about how to set my defense on August 15, or how to pitch somebody on August 15, I need something more immediate and not just a large sample size. What is he like right now? Has he changed? Has he lost his confidence, or is he more confident than he’s ever been? There are fluctuations with people.” [emphasis mine]

What is he like right now? I think that’s the line that got to me. It’s August 15. What’s happening right now, in this unique moment, with that specific player?

I know this question resonates with me because of my years teaching with Teresa Torres, whose focus on continuously discovering opportunities to improve the things we design for humans begins by looking at specific moments in time. You do this through the stories that people tell about those moments.

That approach adds a very rich, colorful layer over the (important) data we gather and analyze about things that impact our lived experiences.

For example. We can gather and analyze data on the use of local busses, revealing trend lines and patterns. But what does that tell us about the moments folks decide to take, or not take, a bus? About the community member (true story) who had a medical appointment to treat a chronic condition, but decided to pay for an Uber because she knew the bus route to the medical facility required a transfer and that on-time bus performance was spotty at best? What does that also tell us about access to healthcare, or the cost of healthcare (an extra Uber ride) in the community?

Baseball is awash in data and analytics. You can see things now that were less visible in the past. Exactly how a pitcher’s pitch uniquely moves (or doesn’t). How minuscule changes in a batter’s stance or swing improve hitting performance. How a player’s overall skills – at the plate, in the field, on the basepaths – generate combined value. All of this generates analytics we might use.

But in the actual experience of a game, moments are idiosyncratic. That hitter on August 15 doesn’t have a hit yet in the game, but he’s made really hard contact in each previous at bat. He’s confident. The wind is blowing out at Wrigley Field and a ball lifted into the air is going to leave the ballpark. Your pitcher is doing well but beginning to tire.

As manager, do you lean on analytics at this moment? Or do you pause and send the pitching coach out to design a strategy for this moment?

As community experience designers we obviously cannot send out a bus-service coach to design a strategy for the moment the community member was deciding how to get to their medical appointment. (Although…hmmm…wouldn’t that be a cool line of thinking to explore?).

The point is: We need to find ways to develop deep understanding (and appreciation) of a multitude of idiosyncratic moments and combine that with rich data and analytics. Together these will improve our chances to design solutions that deliver effective performance across moments.

Then we Fly the W when the solution works.