Our contributing editor Layla Parast writes about her new-found appreciation for the surprising complexities of American football:

Mid-February can be a bittersweet time for many in the United States—it signals the end of the (American) football season. For some, this may evoke no feelings at all. Football? Why bother? For most of my life, I shared that sentiment. Why would I want to watch people running into each other for no apparent reason?

That all changed when my 10-year-old son fell in love with the sport. I tried (half-heartedly) to redirect his enthusiasm—I showed him swimming, track and field, and rowing events—but to no avail. Reluctantly, I found myself watching games on TV that I couldn’t care less about (as an aside: my son plays flag football, not tackle). Then, one evening, I stumbled upon Netflix’s Quarterback. It transformed how I viewed the game and the players. I began to appreciate the layers of complexity and strategy behind the physical strength, and came to respect the discipline and dedication of the athletes.

Now, I proudly join my son and husband as a football enthusiast. We’ve become, dare I say, a football family. Perhaps Texas deserves some credit for this transformation. I’ve even learned to throw and catch a football decently—skills that earn moms a special nod in this part of the country.

For me, these two worlds—statistics and football—remained separate. But that changed when I received an email with the subject line: “AI and Football.” It was from an assistant on the coaching staff of the University of Texas football team, asking if I could speak to the team about AI.

Should I have forwarded this request to colleagues who specialize in AI or know more about football? Probably. But I saw an opportunity to dive into something new, even if it felt like I was heading into unfamiliar waters.

Sports analytics is hardly uncharted territory, but it was entirely new to me. Preparing for the meeting, I learned as much as I could about football data analytics. By the time I walked into the office of a key member of the football staff, I had categorized what I’d learned into four buckets:

• Injury prevention.
• Individual player performance.
• Recruitment.
• Game-time decision-making.

While I felt comfortable discussing the first three areas, the fourth—how AI can improve real-time game decisions—was their real interest. Through this process, I was reminded of an essential truth about learning: the best way for us to grow is to step outside our comfort zone. As statisticians, we often find existing methods that fit the problems we encounter, tweaking them as necessary. But, at least to me, football (and sports generally) defies standard frameworks. Nothing is independent or identically distributed. Every play is influenced by countless factors: who’s on the field, the weather, momentum shifts, and even the emotional state of the players. It’s messy and dynamic—and absolutely fascinating.

This journey also reminded me that learning is as much about humility as it is about confidence. Pretending to know something isn’t about deceit; it’s about creating opportunities to grow into that knowledge.

Where will this new venture take me? I’m not sure yet. But don’t be surprised if you see more football-inspired musings in the future. As legendary coach Vince Lombardi once said: “The measure of who we are is what we do with what we have.”