Baseball and Statistics

Tristan Mott has always loved baseball and statistics. He never imagined combining the two until he started working with a Texas Rangers employee, a PhD student who uses Artificial Intelligence to come up with optimal pitch sequencing strategies. Through the Honors Program, Mott was able to engage in research to use analytics and computing to make predictions and answer complex questions, combining his love for baseball and statistics!

Mott learned about the Honors Program for the first time during an introductory seminar for computer engineering. After starting research with Dr. Karl Warnick, Honors Coordinator for Electrical and Computer Engineering students, Mott decided to join the Honors Program. One of the most valuable lessons Mott learned in the program is that in answering questions, it is best to first research what has already been discovered. “You don’t need to reinvent the wheel,” he said. “I think that’s a really important aspect of asking questions is first figure out what’s been answered and then base your questions off of that.” One question Mott has been exploring is whether or not it is ethical to use Artificial Intelligence in sports to optimize decision making.

In the world of sports, Mott explained that some games rely heavily on decisions that humans make (such as who to put in as the next pitcher or when to steal a base), and others rely more on humans executing those decisions (such as sprinting on a track). In his Great Questions Essay, Mott explored whether AI should be used to help make these decisions. In sports, where the entertainment value is not derived from the decisions that humans make, he found that AI was more helpful than harmful.

Mott’s thesis expanded on this research and he came up with a model that can be inputted into an algorithm to show the optimal decision that should be made in a baseball game. He said that his model beat the betting lines. Mott was able to present his findings at the MIT Sports Analytics Conference in Boston, where he was surprised to meet people who had read his research and were interested in the work he had done. Mott also presented some safety and AI research he had done to Congress. “The research we’re doing at BYU and in the Honors Program people in the world actually care about,” he explained. Sometimes Mott feels like the things he learns at school have no application to the real world, but sharing his research with others changed his perspective.

As Mott has asked complex questions and made predictions, he has learned that there is power in knowing a little about a lot, rather than just knowing a lot about a little. He has also learned the value of always reading. “The more you read, the more you learn, the better you become at learning,” he said. Mott is especially interested in the writings of Nate Silver, a sports analyst who showed the power in knowing a little about a lot when it comes to making predictions. “I think that the Honors Program does a really good job at showing that if we really want to understand the world, it’s not going to be from becoming an expert in anything. That’s useful for other reasons, but it’s about learning a little bit about everything,” Mott said.

Mott grew up in Austin, Texas and Alpine, Utah and served a mission in Fiji and California. He loves being outside, especially when he’s running or backpacking. He is graduating this April with degrees in computer engineering and computer science, and will start a PhD program this fall. In the Honors Program, he has developed skills that will help him in this next chapter of life. “It’s helped me realize that in most real-world problems it’s very unlikely for it to only concern one discipline,” he said.

In the Honors Program and during his time at BYU, Mott discovered what receiving an education is all about. “The most valuable part of getting an education is learning how to learn,” he said.