⚾ From Box Scores to OKRs: How the Blue Jays’ Playoff Run Turned Me into a Data-Driven Operator

I’ll be honest. I wasn’t much of a baseball fan. I followed the Blue Jays a bit as a kid and had been to a couple of games over the years. This season, though, when the team made their playoff push, I jumped on the bandwagon like half of Toronto.

But the more I watched, the more I noticed something. The sport that once seemed slow suddenly felt precise. Every pitch, lineup rotation, and bullpen call mattered. Then came the stats: OPS, batting average, WHIP, WAR. These numbers didn’t just describe performance, they predicted it.

That’s when it clicked. Baseball is really data in motion. And the way it rewards consistent, measured performance mirrors what high-functioning startups and teams do every day.

OPS vs. Batting Average = OKRs vs. Busyness

Batting average looks clean—hits divided by at-bats—but it ignores walks, doubles, and sacrifice plays. OPS, which combines on-base percentage and slugging, shows total contribution.

It’s the same in startups. Most teams still track busyness—tasks completed, meetings held—but those aren’t outcomes. OPS thinking is about impact per attempt.

I explore this in The Sharp Starts Tracking Playbook: How I Actually Keep Track of Things. It’s not about collecting data; it’s about making better decisions.

Even MIT Sloan Management Review found that teams tracking “impact velocity” outperform peers by 30 percent. Baseball figured that out decades ago.

Every At-Bat Is a Feedback Loop

Baseball players don’t just try harder—they tweak swings, adjust pitches, and learn from every at-bat.

That’s iteration. The same loop applies to product sprints, marketing tests, or career pivots.

You review, adjust, and try again. That’s what I wrote about in Focus Like a Founder: staying consistent under pressure separates real operators from the busy ones.

Harvard Business Review says feedback isn’t about fixing flaws, it’s about reinforcing strengths. That’s how hitters improve and how teams execute better.

Pattern Recognition Matters

After a few weeks of watching games, I stopped caring so much about the scores and started noticing patterns. Pitchers working certain counts. Hitters adjusting stances. Managers optimizing bullpen matchups using analytics.

That’s data literacy. Baseball trained me to spot trends before I realized it.

I dive deeper into this in From Cleats to Gloves: The Pivot Playbook I Use to Survive Startups and Career Shifts. The key skill is spotting the signal in the noise and acting fast.

Farnam Street calls pattern recognition a meta-skill that compounds across fields. Once you start seeing the signal, it sticks.

Every Role Matters

Even bench players matter in baseball. A relief pitcher might throw only ten pitches, but those ten pitches can completely change a game.

Startups work the same way. Your “bullpen”—ops, analytics, design, QA—might not get the spotlight, but they hold the line when things get messy.

Baseball made me more appreciative of the invisible work of project managers, ops leads, and quiet executors. They win late innings and save the day without anyone noticing.

Baseball Made Me an Operator

Watching the Blue Jays became a masterclass in decision-making. Data, discipline, and the long game all matter.

Baseball solidified my belief that consistency beats charisma. Numbers sharpen intuition. Progress doesn’t come from home runs, it comes from singles and sacrifice plays.

Track your stats. Understand your numbers. Adjust constantly. Execute when it matters.

Whether it’s a playoff game or a product launch, the rules are the same.

📚Further Reading

• Atomic Habits by James Clear – practical system for habit-building

• Deep Work by Cal Newport – sustained focus is the ultimate competitive edge

• The Score Takes Care of Itself by Bill Walsh – leadership from a detail-driven coach

• Moneyball by Michael Lewis – the book that made analytics sexy

• Range by David Epstein – how generalists outlearn specialists across any field

TL;DR

• I started watching the Blue Jays this season and got hooked on the stats.

• Baseball’s obsession with numbers mirrors how operators should run teams.

• OPS is more useful than batting average, like OKRs are better than just being busy.

• Every at-bat is a feedback loop; every sprint should be too.

• Baseball gave me a crash course in systems thinking and execution.