Five Things I Learned at Saberseminar 2026

Once a year, the baseball statistics world converges on Chicago for a weekend of presentations, discussions, networking, and a frankly shocking number of job interviews. I’m talking about Saberseminar, if you’re not familiar. The conference has long been a source of baseball learning, where a mixture of leading analysts and up-and-coming students present their latest research. Every time I go, I leave with my head overflowing with new avenues of study, new things I’m curious about, and a bunch of cool knowledge that I won’t expand on but am nevertheless happy to have learned. I went two weeks ago, and I have a notebook full of ideas to investigate now. I can’t tell you everything that I learned – if you want that, you’ll just have to go to Saberseminar yourself. But there were five talks in particular that I loved. They weren’t the only good presentations by any means. But I have a column for highlighting five things, and it felt only appropriate to highlight five of my favorites from the weekend.
1. Injury Research by Scott Powers et al
The first presentation of the weekend is usually a good one, and this year didn’t disappoint. Scott Powers is a professor at Rice University who has a long history of good presentations at analytical conferences. His past research runs the gamut from batted-ball modeling to volleyball serve optimization. This year, his presentation focused on injury prevention. Powers, Rose Graves, Marina Vannucci, James Buffi, Daniel Barrueco, John D’Angelo, and Amanda K. Glazer set out to come up with some realistic injury guidance based on pitch characteristics and biomechanical data from the major leagues. As the name of the talk implies, they took a central idea into their work: Don’t pitch an injury prevention plan that boils down to “stop throwing so dang hard.”
The first part of their work focused on what they could glean from Statcast data. They identified a number of characteristics that are correlated with increased likelihood of elbow injury. Most of those won’t surprise you: fastball velocity, fastball break, and slider usage were the top culprits. Interestingly, they also included some measures of effort – the ratio between average and 90th-percentile fastball velo, for example – and found no linkage there. That’s a great proof of concept for the idea, but unfortunately it doesn’t really present a workable plan for pitching coaches. “Hey, just, uh, make your fastball slower and give it less movement.” As the title of the pitch implies, that’s no good, so they went deeper by using biomechanical data to look for more granular data. Read the rest of this entry »






