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Since T.J. Watt and Myles Garrett came into the NFL in 2017, fans have ferociously debated which of the defensive superstars is the most dominant.

Each has won the Associated Press鈥檚 annual Defensive Player of the Year Award, for instance. But with both Watt and Garrett signing landmark contracts in 2025, and a new NFL season about to kick off, the question of who鈥檚 鈥渂etter鈥� is once again top of mind.

But what if there was a data-driven metric that could answer the pass-rushing debate once and for all?

Well, 好色先生TV鈥檚 Quang Nguyen and his colleague at Loyola University Chicago may have developed just the thing. It鈥檚 called , and it is a relationship between a defensive player鈥檚 speed and the distance between that player and the quarterback of the opposing team.

鈥淭he idea is to help with the task of player evaluation, which is a fundamental problem in sports analytics,鈥� said Nguyen, a Ph.D. student in CMU鈥檚 Department of Statistics & Data Science.

Since the mid-2010s, the NFL has been inserting RFID tags into players鈥� shoulder pads and even the football itself in an attempt to generate data about various aspects of the game. This technology is the heart of the 鈥淣ext Gen Stats鈥� you have likely heard mentioned during NFL broadcasts.

But how to use all of that data is very much a developing field. This is because American football is a team sport, and that means every play is influenced by seemingly innumerable variables. The play context matters, but so does the movement of the players around you, the formidableness of your opponents, and even things like the playing surface or the weather.

Of course, coaches, general managers and team owners have a critical need for insightful data interpretation. And that鈥檚 where statisticians like Nguyen come in.

鈥淕iven all this tracking data, we can help a team, a scout or a coach identify these players with the specific traits and abilities that they want to draft or sign during free agency,鈥� said Nguyen, who was also invited to present his findings at the U.S. Olympic and Paralympic Performance Innovation Summit in Colorado Springs last week.

Breaking Down STRAIN

The trouble with trying to evaluate defensive players 鈥� and especially those involved in pass rush, which is when a player pressures the opposing quarterback 鈥� is that most statistics rely on the outcome of a play.

For instance, creating a turnover, such as a fumble or interception, or tackling the quarterback behind the line of scrimmage, known as a sack, are great ways to assess the prowess of a defense, but they don鈥檛 happen on most plays. At the same time, defensive players like Watt and Garrett influence their opponents in more subtle ways, even if those actions don鈥檛 always produce a fumble, interception or sack.

鈥淪o those statistics are a little bit noisy,鈥� said Nguyen. 鈥淥n the other hand, we can calculate STRAIN for every pass-rusher, on every play, at every moment.鈥�

So, what does the STRAIN metric have to say about the rivalry between defensive juggernauts?

鈥淲ell, according to the statistics, you could say that they鈥檙e both ranked at the top in their own respective positions,鈥� said Nguyen, diplomatically noting that while both players rush passes, Watt is technically a linebacker while Garrett is what鈥檚 known as a defensive end.

Pushed to choose though, Nguyen said Garrett came out slightly ahead in STRAIN scores. However, there鈥檚 a pretty big caveat.

Nguyen and his team only had access to a small slice of the data 鈥� just half a season of metrics from the year 2021 鈥� which was made available as part of the NFL鈥檚 annual Big Data Bowl event. (, earning the chance to present at the NFL Scouting Combine in Indianapolis in 2023 and 2024.)

To truly crown a king of the gridiron, Nguyen said he鈥檇 want multiple seasons鈥� worth of data, or better yet, data from each player鈥檚 entire career. Right now, the NFL doesn鈥檛 make that kind of information available to the public.

鈥淏ut even still, within this limited sample size, we managed to find meaningful differences in STRAIN across different positions and play outcomes,鈥� he said.

Another New Metric: Shiftiness

Nguyen has also developed another valuable tool for player evaluation, but this one is particularly useful for wide receivers.

Again using NFL tracking data, Nguyen was able to evaluate change-of-direction, or the ability of a player to rapidly and efficiently alter their trajectory.

鈥淲e fit a Bayesian circular mixed-effects model, where we explicitly capture both the turning tendency and turning variability,鈥� said Nguyen. 鈥淥ur model also allows us to evaluate how much better or worse a player鈥檚 turning variability is relative to the average player in the same position group.鈥�

Put into practice, Nguyen showed that some players, such as DK Metcalf, are extremely efficient at running straight 鈥� not changing direction, in other words 鈥� while others, such as George Pickens, excel at making erratic, variable turns. In fact, Pickens turned out to be the shiftiest of all the wide receivers Nguyen had data for 鈥� an interesting tidbit, given that the Pittsburgh Steelers traded Pickens away this offseason while also acquiring Metcalf.

鈥淪o the Steelers have swapped wide receivers with two very different styles of running after the catch,鈥� said Ron Yurko, an assistant teaching professor in the Department of Statistics & Data Science and Nguyen鈥檚 Ph.D. thesis advisor.

Looking Beyond Football

While the pass-rush and change-of-direction evaluations were developed for American football, each has enormous potential for use across a variety of other sports, from Olympic flag football to basketball and soccer.

鈥淨uang is at the cutting edge of working with these very rich data points in sports,鈥� said Yurko.

鈥淎nd the fact that he was invited as a Ph.D. student to this very big event with the Olympic and Paralympic Committee, with all these leaders in sports analytics, just shows the respect he鈥檚 already earned,鈥� he said.

Steelers and Browns fans will have to wait until Oct. 12 for the two squads to square off in the season鈥檚 first head-to-head meeting between Watt and Garrett. But for Nguyen, Yurko and other data-heads, all eyes are on Oct. 24 and 25 when the Carnegie Mellon Sports Analytics Center hosts its annual .

This year鈥檚 event features industry leaders such as , as well as a football analytics workshop for attendees to gain hands-on experience with the same type of NFL tracking data that Nguyen has innovated with during his Ph.D.