It's a question as thought-provoking as one Curtis Goodwin ('20 M.S.) might have posed to the students in his AP Calculus class back when he was an instructor and basketball coach at Fort Worth ISD's Boswell High School: How did a math teacher with a love of sports and numbers become one of the elite data analytics minds working in professional football?
"I'd say my journey was a little unique," says Goodwin, who also previously taught and coached at high schools in Illinois. "I've always been involved in sports, and I've always been involved in math. Data science didn't really exist 10 years ago when I was coming out of college, or it was just getting started. Now, it's pretty prominent."
The potential to land a job that was a perfect marriage of sports and numbers is what inspired Goodwin to enroll in UNT's advanced data analytics graduate program in 2019. The knowledge and connections the program provided helped him land his current role as a performance data scientist for the Houston Texans.
For his master's thesis, Goodwin focused on prediction win probability and game outcomes, as well as season outcomes for different NFL teams. Impressed by his graduate student's work, Michael Monticino -- mathematics professor and chair of the advanced data analytics program -- reached out to Russell Joyner ('03), director of football information systems for the Texans, and connected the two. Goodwin joined the Texans staff under Joyner in June 2020.
"We have a very collaborative environment here at the Texans," Goodwin says. "They're very receptive to any idea and any piece of information that can help them move the ball forward."
Although tracking tendencies have long been a key part of game preparation and scouting in sports, the rise of advanced data analytics in the industry is still fairly recent. Former Oakland Athletics General Manager Billy Beane and author Michael Lewis brought the potential of advanced analytics in professional sports to the mainstream with Moneyball in 2003. A little more than a decade later, the NFL partnered with Zebra Technologies and began embedding radio-frequency identification tags in shoulder pads to track players during games. Today, there are RFID chips in every player's shoulder pads, the game ball, on the referees and in the pylons. All but one of the 32 NFL teams employ performance data scientists full-time on staff.
In 2018, the NFL held its inaugural Big Data Bowl, an annual analytics contest for both professional and amateur data analysts to test their skills on the latest challenges in the game. The 2020-21 winners, a team of four friends, brought home $25,000 for their analytics work on defending the pass play.
"If there's a question or problem statement -- or simply, 'What can the data tell us?' -- my job is to find if there is a story behind this," Goodwin says. "My goal is to investigate that and then provide my findings."
The possibilities presented by those questions are endless. Teams use data to track a player's workload, potentially determining rest days or fitness needs. They also look at opponents' tendencies to game plan week to week. For example, data can reveal a lot about teams that aren't good at diversifying and perhaps rely too heavily on certain strengths. A team can then design its defense or offense to work most efficiently against that tendency. The same data can also show a team its own tendencies, providing valuable feedback in game preparation from week to week.
It sounds like a lot -- and it is. But Goodwin was more than prepared to tackle the numbers.
"The steepest learning curve is that there's a lot more data once you get into the real world -- how do you deal with 100 times, 1,000 times the amount of data?" he says. "The opportunities I had from an educational standpoint -- the concepts and theories and skill sets taught in the advanced data analytics program -- have been very helpful."