The Secret to One-Run Games

The Secret to One-Run Games

In the sweltering Jupiter, FL heat one day during the off-season, I had the pleasure of taking in a backfield game with my boss’s boss, then-Marlins GM Kim Ng. Despite the “winter” months, the aluminum grandstand was so hot I had to tuck my black Thinkpad into the shadow of the overhang to keep the keyboard from sizzling my fingers. We watched the game, and Kim asked me offhand: “Why do you think we were so bad in one-run games?” A great question. Because, boy, we were bad. Two years prior, we went 21-29 (.420 winning percentage) in one-run games. And the previous season, 2022, we were an abysmal 24-40 (.375). But I wasn’t sure why we had been so bad. I offered the standard sabermetric truism, that randomness governs one-run records and regression ultimately balances things out. It’s fine to say such things from a cold distance, but sitting in the heart of the storm, the steaming hot struggle of daily mire, it’s difficult to believe them. It’s hard to look back on two years of struggles and say, “It’ll balance out.” And then say it again and again after each successive loss. And even if we can hold the faith in regression, that doesn’t explain the composition of one-run games. How does a team end up in them? Did they blow a late lead or battle through a pitcher’s duel? Are they disproportionately losing 1-run games in high-scoring affairs or in extra-inning nail-nibblers? I didn’t have the answers to these questions, and I felt they were worth exploring.