Bold statement up front: Projections will misread 2026 baseball data, and the resulting surprises may redefine how we evaluate players this season. But here’s where it gets controversial: the spring data points I’ve seen don’t always align with what you’d expect from last year’s trends, and wind, data quirks, and park differences could be skewing movement readings more than we realize. If you want a clear view for beginners, this piece breaks down what’s off, what to watch, and why a lot of the initial numbers might mislead—while offering practical guidance for interpreting spring signals as we head into the regular season.
Spring has arrived, and it’s great to have live baseball back. In this article, I’ll nimbly run through several players who have stood out so far.
I’m also eager to share my 1-30 system pitching development ranks next week (roughly Wednesday). This ranking draws heavily from a survey I circulated to more than 50 MLB coaches and executives. Stay tuned for that.
There are several spring instances where the data clashes with my instinct. The four-seam shapes of Bubba Chandler and Mitch Keller looked poor in their first starts and improved in their second. Richard Fitts showed more movement on his four-seamer and sweeper on February 25. Rays prospect Ty Johnson threw a four-seamer with about 3 inches more vertical break than last year, despite no changes to his release or spin.
I’m no expert in stuff models or park/environment adjustments, but it’s reasonable to suspect wind is playing a major role in some games—and perhaps data quality is, too. Baseball analyst Vivienne Pelletier notes that crosswinds can affect a pitch by up to 4 inches. When a park lacks second and third decks, the wind fingerprint shifts. And most spring ballparks differ structurally from MLB parks. Even noted pitcher Robert Stock found that air density can influence performance on Stuff+.
In short, a big jump in movement without a corresponding change in velocity slot, release, or spin usually means the pitcher hasn’t fundamentally changed. Until we see 2026 MLB park data prove otherwise, I’d treat those spring shapes as echoing 2025 regular-season forms.
Sample spring numbers:
- Feb 23: 2 IP, 0 H, 2 BB, 2 K
- Feb 28: 3 IP, 1 H, 1 BB, 4 K
Now, let’s look at a case study: one of last year’s least productive pitchers. I anticipate the Nationals will reduce their four-seam and sinker usage significantly. Last season, their team mix leaned 55% fastballs (four-seam and sinker combined)—the highest rate in MLB. In spring, that share has dipped to 41.7%, placing them among the lowest in the league aside from the Marlins. We’ll see more concrete evidence once the regular season data rolls in, but Irvin’s projections are notably pessimistic (about a 5.00 ERA in around 20 starts).
Irvin’s spring usage has shifted to 40% four-seam and sinker after 54% last season. Against left-handed hitters, his curveball has risen to roughly 30% usage, with his cutter around 25%. Against righties, he has tripled his short slider usage to roughly 23% from 2025 levels. The strategy here is to scale back four-seam usage, a pitch that yielded a 16% barrel rate against righties and 12% against lefties.
Thoughtful takeaway for beginners: treat spring movement as a potential signal, but verify it against environment and usage context. Don’t lock in on one data point. Watch how pitchers adjust in MLB parks as the season begins, and compare that to spring park data to distinguish true improvements from park-driven quirks.
Question for discussion: do you think spring data should prompt early changes in player valuations, or should we wait for regular-season data in MLB parks before adjusting our projections? Share your thoughts below.
