This mission is personal. It started over thirty-five years ago. I still vividly remember sitting with my father, staring at the physical pages of USA Today sports lines, listening to old-school voice mail updates trying to get a competitive edge. It was during that era that Randall Cunningham went down with a devastating, season-ending knee injury on the first play of the second quarter in Week 1 of the 1991 season. In a single, unforced instant, an entire year's worth of strategy, research, and financial buy-ins evaporated into thin air.
I didn't lose because I picked wrong. I lost because nobody was even looking at who was most likely to get hurt. Three and a half decades later, I built the thing I wish I'd had that morning.
INJSUR doesn't sell ads. We don't use manipulative tricks. We provide raw, honest, patent-pending data science. Our model is a logistic-regression and neural-network ensemble trained on five seasons of NFL notable-injury records (2022–2026). It scores every player across 20 measurable features — workload, contact rate, age, injury history, and positional hazard data. We turn statistical patterns into a measurable, hedgeable signal.
We are committed to transparency. We publicly detail every miss alongside every hit. The algorithm isn't static — it improves as real-world results come in. A seasonal membership costs less than a single league buy-in. Test the tech, review the metrics, and make sharper decisions.
