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 the rise of free agency and the looming introduction of salary caps in college sports, football programs face new and unprecedented challenges. Gone are the days when a few scouts and position coaches could track down the talent they needed. Today, teams need to evaluate hundreds of players in the transfer portal and make decisions within hours, if not minutes. The modern collegiate general manager must navigate an increasingly complex landscape — one that demands modern tools and technologies to stay competitive.
One key to adapting to the shifting landscape in the NCAA lies in the integration of advanced analytics. During my time with the Steelers, I witnessed firsthand how analytics supplemented and complemented traditional player evaluation. The NFL’s embrace of data science has helped teams assess talent with greater precision and efficiency. College football, however, is still in the early stages of adopting analytics. As the landscape of college football evolves, programs that integrate these advanced tools will be better positioned to identify and develop talent more effectively, staying competitive in an increasingly dynamic environment.
At the heart of football analytics is understanding player movement. In the NFL, ball and player tracking systems have revolutionized the way we assess traits like change of direction (COD) — a critical metric for evaluating everything from receivers to cornerbacks and running backs. Until now, college football has lacked the ability to assess this in-game action with the same precision.
Take a player like Rams WR Puka Nacua. Drafted in the fifth round of the 2023 NFL Draft, Nacua didn’t impress scouts with his combine numbers. His average 40-yard dash and unimpressive three-cone drill suggested a late-round pick at best. However, when we analyzed his game footage through the lens of advanced analytics, a different story emerged — a player with elite agility and movement skills that combine tests couldn’t fully capture. Nacua’s in-game speed and COD were key to his breakout rookie season, where he set a record for receptions and yards.
The question is: How many players like Nacua are hidden in the transfer portal or overlooked because they don’t test well, or aren’t in schemes that maximize their potential? Analytical scouting reports give teams the ability to identify hidden gems by focusing on quantifiable athletic traits that truly matter on the field.
Position-specific, machine learning models give further insights by breaking down player skill into individual traits and abilities — quantifying player ability to change direction, burst off the line of scrimmage, collapse the pocket and create space in the running game. With advanced models that measure player actions with remarkable specificity, we provide coaches and GMs the data they need to make better-informed decisions.
But these advancements aren’t just about finding talent — they’re about making the entire process more efficient. These digital scouting reports update automatically after every game. Analytical evaluations are ready before a player ever enters the transfer portal, enabling teams to make decisions quickly and confidently. College football teams face an enormous challenge in player evaluation, particularly with the influx of players in the transfer portal. The ability to analyze thousands of players and pinpoint those who will thrive in a program’s unique system is no longer a luxury — it’s a necessity.
As college football continues to evolve, its analytics capabilities must evolve with it. We’re at a critical inflection point. Programs that embrace this new wave of technology will gain a distinct advantage. By integrating data science into scouting and decision-making, college football can move closer to the NFL’s level of sophistication. The era of relying solely on traditional scouting methods is ending. Those who fail to adapt will be left behind.
The margins in football are small, and they’re getting smaller every year. Just a handful of plays determines whether a team makes the playoff or finishes with a losing record. In the coming years, the difference between success and failure for college programs will depend on how effectively teams use the transfer portal, which in turn will depend on how effectively they use data. This isn’t just about making smarter personnel decisions — it’s about staying competitive in a rapidly changing environment.
As college football evolves, the analytics gap is closing fast. Programs that embrace this change and equip themselves with the right tools will be poised to lead the way. The future of college football analytics is here, and it’s time to take full advantage.
Karim Kassam is VP of Product for Teamworks Intelligence. Karim was the first analytics professional for the Pittsburgh Steelers in the mid-2010s, and has led analytics efforts for the Jacksonville Jaguars and Minnesota Twins.
