Applied Statistics

From �鶹��Ƶ to the majors, George �鶹��Ƶ statistics students are driving athletic success

Teresa Donnellan — Fri, 07 Nov 2025 18:08:03 +0000

From �鶹��Ƶ to the majors, George �鶹��Ƶ statistics students are driving athletic success Teresa Donnellan Fri, 11/07/2025 - 13:08

�鶹��Ƶ students are proving a statistics degree can take you all the way to the pros. Students gathered in the Johnson Center on November 5 for a Sports Analytics Networking Event to learn about how data science is transforming athletics—and how George �鶹��Ƶ students and alumni are leading the charge.

“Sports teams increasingly use data for developing strategies, boosting player performance, and improving fan engagement,” said Department of Statistics Assistant Professor Jonathan Auerbach. “Sports analytics allows students to turn their passion for sports and statistics into a high-impact and highly visible career.”

The first panel at the Sports Analytics Networking Event. From left to right: Moderator Associate Professor Brett Hunter, Cole Boller-Pinkham, David Buxton, and Abby Finch.

"The future of sports is data, and we have been working hard with our colleagues from statistics to provide new opportunities for our students,” said Sports Analytics Professor Craig Esherick.

The event, hosted by the Department of Statistics in collaboration with the School of Sport, Recreation, and Tourism Management; �鶹��Ƶ Athletics; the �鶹��Ƶ student chapter of the American Statistical Association; and the Sport Management Society featured two panels and a networking session designed to connect students with alumni working in sports analytics and those currently helping George �鶹��Ƶ athletes thrive.

From �鶹��Ƶ to the Majors

“Every team I’ve worked on, we have a lot of data, and nobody knows what to do with it,” explained Abby Finch, an alum who worked in Baseball Technology for the New York Mets after graduating from George �鶹��Ƶ statistics, during the first panel. “Having somebody who understands the player development side of baseball as well as what these data points mean—and being able to translate that data in a way that coaches understand—has been very valuable.”

The Department of Statistics recognized Abby Finch (left) and David Buxton (right) as outstanding alumni. Photo provided.

Finch's fellow panelists included Cole Boller-Pinkham, a Honors College junior majoring in statistics with a concentration in sport analytics, currently interning with the United Soccer League, and David Buxton, an alum and specialist in player information for the Milwaukee Brewers. The panelists advised students interested in sports analytics careers to focus on developing the skill of interpreting and presenting data as well as analyzing it.

"Your goal isn’t necessarily to share your statistical knowledge,” said Buxton. “It’s to change people’s actions and help them buy into data-driven decision making.”

Analytics in George �鶹��Ƶ Athletics

The second panel, moderated by Esherick, explored how George �鶹��Ƶ integrates analytics into collegiate athletics.

“We use data supported insights to guide how we develop our athletes,” said Associate Director of Strength and Conditioning Faith Brown, whose academic research focuses on load monitoring in team sports. “We look at trends over time, adjust training loads, and even analyze preseason volume and intensity to reduce injury incidence. It’s not just lifting weights—it’s everything that goes into performance.”

The second panel at the Sports Analytics Networking Event. From left to right: Moderator Professor Craig Esherick, Derek Vigon, and Faith Brown. Photo provided.

Sports psychology also plays a critical role, noted Assistant Athletic Director and Director of Sport Psychology Derek Vigon, who has consulted for the National Football League (NFL)’s Detroit Lions and worked with athletes in the National Basketball Association (NBA), NFL, and MLB. “I interpret psychological data to help athletes and coaches understand stress responses and focus issues,” he said. “It’s all data, and it can be flipped to improve performance.”

Advice for Students

In addition to networking at events like this one, panelists advised students interested in sports analytics careers to be persistent and seize opportunities—such as joining George �鶹��Ƶ’s Analytics Team, participating in prediction competitions and hackathons, and exploring internships in George �鶹��Ƶ Athletics’ Patriot Performance Lab—when they come.

“If you’re passionate about it, keep moving forward and try not to take no for an answer,” advised Vigon. “Network, apply, and apply again. Eventually, it’ll happen.”

With alumni thriving in sports analytics, students contributing to cutting-edge research, and faculty integrating analytics into every facet of athletic performance, George �鶹��Ƶ is positioning itself as a leader in sports analytics education. For students who dream of turning numbers into wins, George �鶹��Ƶ can be the place where those dreams start.

More on Statistics at George �鶹��Ƶ

In This Story

Jonathan L. Auerbach

Craig R. Esherick

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Professor applies statistics and AI to land use modeling and real estate pricing

Teresa Donnellan — Wed, 29 May 2024 16:18:12 +0000

Professor applies statistics and AI to land use modeling and real estate pricing Teresa Donnellan Wed, 05/29/2024 - 12:18

In This Story

Abolfazl Safikhani

�鶹��Ƶ statistics professor Abolfazl Safikhani recently applied his cutting-edge, interdisciplinary research to analyzing land use dynamics and property pricing shifts over time, work that underscores the transformative potential of data-driven insights, especially in urban planning and real estate.

Safikhani earned bachelor’s and master’s degrees in mathematics before earning a doctorate in statistics.

“I decided to do a PhD in statistics because throughout the master’s I had become more and more interested in connecting real world problems to data. And I'm very happy that I made that decision,” he said.

Abolfazl Safikhani

Along with a former colleague at the University of Florida in the urban planning department, Safikhani applied machine learning techniques to a dataset comprising millions of land parcels in Florida. The two endeavored to decipher the intricate dynamics of land use transformations over time and predict future developments with unprecedented accuracy. Their predictions surpassed 98% accuracy.

But the team didn't stop with successful predictions. They recognized the importance of understanding the underlying mechanisms driving these predictions. With the addition of a new collaborator, Tianshu Feng in George �鶹��Ƶ’s Systems Engineering and Operations Research Department, the researchers aim to present their land use analysis software as explainable artificial intelligence (XAI). By elucidating the black box of machine learning algorithms, Safikhani hopes local government decision-makers and urban planners can confidently leverage the software to optimize resource allocation effectively.

Another of Safikhani’s projects considers land use and value specifically concerning the price of residential real estate. Safikhani’s own experience buying real estate in Fairfax County, Virginia, in 2022, inspired this project. When he asked his real estate agent to estimate a fair price of a certain house, the agent came back with an estimate based on the price of three comparable local properties that had recently sold. Ever a “quant guy,” Safikhani said, he thought there could be a better way: applying the idea of transfer learning.

“The big idea of transfer learning is, within your big data set, try to find areas that have similar dynamics to your area of interest. And then use that similarity to improve your prediction,” Safikhani explained. “So, imagine that there is a little neighborhood somewhere in DC or somewhere in Maryland or somewhere in California that has dynamics very similar to the specific neighborhood where you want to buy a house in Northern Virginia. Once you account for some changes, let's say, regulations and things that are different, then the remaining dynamics are their similarities.”

He continued, “If you only use your neighborhood, you can have three data points. If you use another, similar neighborhood, it's going to be 20. If you use neighborhoods from other places over the 50 states of the U.S., you may end up getting a thousand data points.”

Safikhani is working with a colleague from the University of California – Los Angeles to bring in funding to develop this pricing software. Their preliminary results show the benefit of their proposed model versus current pricing systems.

Safikhani's research is poised to revolutionize sectors like urban planning and real estate. In fact, his research has attracted the attention of startups keen to translate his findings into real estate–disrupting tools.

“It seems there's actually a growing interest in having such AI tools that would understand land use development and then really match it with pricing,” he said. “And sooner or later, this [technology] is going to come out. Platforms like Zillow are doing a good job, but there's much more that can be done.”