Before Dylan Mervis first discovered UVA’s M.S. in Business Analytics (MSBA) Program, he was fascinated by the ways in which data analytics could be used to inform decision-making. As he had been working in commercial real estate, he taught himself the nuts and bolts of probability, statistics, and the R programming language before ultimately concluding that while the real estate industry wasn’t a great fit for him, applying to the MSBA graduate program felt like the right choice, since it presented a great deal of promising options for his future.
“Besides the actual practice of data analytics, I was intrigued by the number of doors a degree in analytics would open,” Mervis says. “Companies in all industries are attempting to grow their analytical capabilities, and broadening my analytical skill set was attractive to me.”
As the MSBA is offered in partnership by UVA’s Darden School of Business and McIntire School of Commerce, the names of two powerhouse schools behind the program were initially a big draw. And upon looking into the curriculum, Mervis was pleased to find that the courses emphasized the intersection of data science and business applications he was seeking; already knowing that he planned to switch industries, he was convinced by what he learned about the value of the University’s alumni network, career support services, and the documented outcome successes of MSBA graduates.
He made his move.
When he began the MSBA program, Mervis recalls being almost unnerved by the intelligence exhibited in class discussions by his peers.
“I remember thinking, ‘Wow! Everyone here is very, very smart. What the hell am I doing here?’” he jokes, counting himself fortunate to have learned with classmates with whom he shared the experience of MSBA’s first module on Enterprise Analytics. It was a situation that helped ease the transition from work to an academic environment. “It was such a great group of diverse minds with different perspectives, and it led to some great ideas and intelligent discussions.”
Yet it was in the second module on Descriptive Analytics, when students learn how to communicate the value of data-informed insights, that Mervis had an epiphany. The “lightbulb moment” took place during Darden Professor Michael Albert’s Data Analytics course.
“I thought I had a decent understanding of the data science process, but Michael’s class really opened my eyes to numerous topics, including potential pitfalls in exploring data, effective communication of models and processes, and why you don’t always need fancy machine learning,” Mervis explains. He credits Albert with giving students the freedom to discover for themselves how different decisions and assumptions in the exploration and modeling processes can impact outcomes. “This is really where I learned that the sea gets deeper the farther you go in, to remain skeptical of your decisions, test everything, and most importantly your model is useless in business unless you can communicate your findings.”
As Mervis had gone into the program with plans to switch industries and move towards securing a job in sports analytics, he says the education he received supercharged his job search simply by revealing the wide-ranging applicability and opportunities available in analytics—no matter the sector or business goal.
Now working with sports entertainment giant ESPN, he has taken the skills he developed at UVA on the job with him.
“I still use code from Michael Albert’s Data Analytics course, as well as [Commerce Professor] Reza Mousavi’s Machine Learning course every day in my work. Along with the technical skills learned, the emphasis placed on exploring the data, thinking deeply about the problem at hand or question being asked, and how to communicate data projects and models were all very valuable,” he says. “Outside of the pure data science realm, [Commerce Professor] David Lehman’s course was excellent in learning to think probabilistically and understanding how decisions are made through various heuristics.”
Through his position in sports analytics, Mervis has enjoyed some welcome similarities to his innovative UVA graduate courses, including the relationships he has with his peers.
“Truly the best part of working at ESPN is that I get to interact with some brilliant people every day, which is what the MSBA program felt like,” he says. “I get to bounce thoughts off the founder of football analytics, Brian Burke, and get to strategize about what the future of ESPN user products may look like.”
And as he and his team ideate potential offerings for the sports powerhouse, Mervis currently has his sleeves rolled up while building a project for NBA player projections. It promises to be of real value to fans of both fantasy basketball and sports betting.
“The majority of final outputs will show up on the website in the form of projections in the fantasy app—similar to how each football player has a projection for fantasy football—and will offer a tool to allow users to adjust certain aspects to receive a new set of projections for the purpose of player prop betting,” Mervis explains. “For example, we may project LeBron James to play 36 minutes and have 28 points, eight rebounds, and seven assists.” The tool is characterized by its extreme adaptability and agile enough to provide up-to-the-minute results. “But then, maybe you read something from your favorite beat reporter that LeBron may be restricted that night and think he will only play for 20 minutes; you can then input 20 minutes and receive a new set of projections.”
While guessing correctly in games of chance is always difficult, being on the build side of the sports stat app has been a real challenge as well.
“Fantasy projections and player props that sports books list are different, because they are asking fundamentally different questions. For fantasy sports, the question being asked is ‘How many points will player X score?’, while the question being asked for player prop bets is ‘What is the probability that the player scores over X number of points?’ For fantasy sports, you need a mean projection, and for props you need a median projection,” Mervis says, noting that the app is not only projecting point estimates for each player, but creating a full distribution of the possible points, rebounds, assists, and more that a single basketball player is potentially capable of in each situation.
Making tools capable of producing those kinds of player-related hypothetical estimates at lightning speed has proven rewarding for Mervis. While he’s not completely sure what long-term goals at ESPN will dictate the future projects that define his work, he clearly enjoys what he’s engaged in now: examining and creating new tools with the statistical nuances of fantasy sports and within the rules dictating many of the games of chance operating within the sports betting markets that have become so ubiquitous in many U.S. states over the last few years.
Mervis is living proof of his assessment that “the program speaks for itself,” when it comes to professional advancement. He notes that even before completing the one-year, 30-credit-hour curriculum, he witnessed his peers making use of their skill development to routinely accelerate their careers.
“Every single class would open with an announcement about promotions, career switches, or professional achievements, and at every single class, there was always something to celebrate,” Mervis recalls. Those positive results would eventually include his own as well, as he pivoted into a field that greatly interested him.
A more surprising result of the experience? “The relationships built within the program,” he says. As the MSBA meets online twice per week and welcomes participants to a combination of 10 weekends in the Rosslyn area of Arlington, VA, and on Grounds in Charlottesville throughout the 12-month span of the program, Mervis was not expecting much in the way of bonds forming outside of classes.
“With the hybrid model, I didn’t think there would be the time or effort to really create relationships with peers, but I was dead wrong,” he admits. “Everyone had such a great attitude, and each time we got together was a great experience. I made some great friends, and the majority of the cohort is still active in our group chat.”
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