Numbers have always played an important role in football. Forty-yard times, run by athletes under controlled conditions, have been part of player evaluation for more than 50 years. More recently, the evolution of data in the NFL — from basic box scores to rich play-by-play information to ball and player tracking (Next Gen Stats) — has coincided with an increased use of analytics in decision-making, from player evaluation to opponent scouting to in-game strategy. Yet in college football, analytics adoption lags far behind, and this widening gap between the NCAA and NFL is poised to become a critical issue. The NCAA is at least a decade behind the NFL in leveraging data for performance evaluation and strategic decisions.

A decade ago, I was hired as the first analytics professional at the Pittsburgh Steelers. At the time, the NFL was just beginning to integrate data into football operations, following the path set by baseball and basketball. Today, advanced NFL teams employ 10 or more data scientists and engineers who provide valuable insights into every aspect of the game using every data source available. College football still has a long way to go.

The college game has traditionally operated with fewer resources for scouting and player evaluation. Unlike the NFL, where teams have dedicated scouting departments and extensive budgets, college football coaches often juggle multiple roles — acting as both coaches and scouts. With smaller budgets and limited in-person evaluation opportunities, the college system has relied on simpler metrics like high school production and combine numbers.

But the landscape is changing rapidly. With........

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