By Andrew Honaker, Arts, Culture and Sports reporter
Data analytics has become a behind the scenes driver of decision making within Virginia Tech Athletics, influencing everything from ticket pricing to in game strategy as the department navigates a rapidly changing college sports landscape.
As financial pressures increase and competitive margins shrink, athletic departments across the country are turning to analytics for clarity. At Virginia Tech, that shift has expanded the role of data beyond performance metrics, embedding it into business strategy, fan engagement, and long term planning.
Scott Wise, who works with athletic leadership on data and analytics initiatives, helps translate complex information into practical decisions for administrators and coaches. In an interview, Wise explained how analytics is currently used across the department, the misconceptions surrounding sports data, and where he sees its future impact. His responses were edited for clarity and length.
How would you describe your role in data and analytics within Virginia Tech Athletics?
We’re fortunate to have leadership that genuinely wants to use data to make better decisions. Whether it’s data science or analytics, they want information that helps them choose the best path forward rather than relying solely on instinct.
A large part of my role focuses on pricing studies, revenue projections, and budgeting. Those areas are where analytics can have immediate impact. We’ve been able to evaluate pricing across all sports to better understand demand, maximize revenue, and still consider the fan experience.
What’s made this effective is trust. Leadership wants to make informed decisions, and they’re willing to act on the data we provide. That allows analytics to actually shape outcomes instead of just supporting decisions that have already been made.
How is data analytics currently being used within Virginia Tech athletic programs?
On the team side, softball is a strong example. They have a very forward thinking staff that constantly looks for ways to improve, even by small margins. When you’re one game away from the College World Series, those details matter.
We’ve analyzed several years of historical data to identify what has led to success and what hasn’t. From there, we model potential outcomes to help guide in game strategy. That includes decisions like whether to steal, bunt, adjust the batting order, or choose a specific pitcher in a given situation.
What makes that partnership effective is buy in. Not every program is willing to adapt based on analytics, but softball has embraced it, which allows data to become a competitive advantage rather than just background information.
What is one way analytics influences decisions that fans would never expect?
Pricing decisions are a big one. Fans sometimes think prices are raised randomly or without regard for their experience, but that’s rarely the case.
We analyze household income, discretionary spending, demand trends, and how different segments of fans behave. The goal is to balance financial sustainability with accessibility. While we do need to generate revenue to operate, we also can’t ignore how fans respond to pricing changes.
It’s never as simple as increasing prices because a game sold out the previous year. Every decision requires weighing multiple variables and understanding the potential consequences.
How do you make analytics useful for coaches instead of overwhelming them?
Coaches can get overwhelmed quickly if they’re given too much information. The key is simplifying the output while maintaining strong analysis behind the scenes.
Some staffs are excellent at blending analytics with qualitative insight, like how a player looks physically or how an opponent is reacting in real time. Others struggle if they’re presented with too many numbers.
What’s been most effective is narrowing things down to one or two metrics that resonate with them and presenting those visually, often through dashboards. That allows coaches to make decisions without feeling buried by data.
What types of data are most valuable to the department right now?
Consumer analysis is one of the most valuable areas right now. We’re focused on understanding who attends games, who doesn’t, and why.
With changes in college athletics and new leadership in football, there’s excitement, but excitement alone doesn’t guarantee attendance. Data helps identify fans who haven’t traditionally come out and determine how to re-engage them.
Understanding fan behavior has become just as important as understanding what happens on the field.
How has the role of analytics in college athletics changed in recent years?
It’s changed dramatically. When I first started, only a handful of schools had dedicated analytics roles. Now, departments across the country are creating positions focused on data science and analytics.
That shift is driven by necessity. Every dollar is under more scrutiny than ever before, and departments need to justify decisions with evidence. Analytics helps ensure resources are allocated responsibly and strategically.
Data driven decision making is no longer optional, it’s becoming standard.
What misconceptions about sports analytics do you encounter most often?
A common misconception is that probability equals certainty. If a model shows a 51 percent chance of something happening, people assume that outcome is guaranteed, which isn’t true.
Analytics doesn’t predict the future. It provides information about what is likely to happen. The value comes from using that information to adjust strategy and reduce risk, not from assuming outcomes are predetermined.
Understanding that difference is critical for applying analytics correctly.
How do you measure whether analytics is actually making a difference?
Tracking impact is essential. Financially, that can be straightforward. If we conduct a pricing study, implement recommendations, and see increased revenue the following year, that’s a clear indicator.
The same approach applies to attendance or performance metrics. It’s about defining expected outcomes and comparing them to actual results. Being able to show that connection helps demonstrate the value of analytics.
What challenges come with implementing analytics at the Power Four level?
Buying in remains the biggest challenge. Some people in college athletics don’t trust data or believe it can’t account for real world situations.
That’s not accurate. We can incorporate both quantitative and qualitative factors into models. The challenge is getting people comfortable using analytics as part of their decision making process.
Fortunately, Virginia Tech’s leadership is forward thinking, which puts us in a strong position compared to many programs.
Looking ahead, where do you see analytics having the biggest impact at Virginia Tech?
The biggest opportunity is integrating business data with performance data. That alignment is rare in college athletics, but it’s where analytics can be most powerful.
If we can evaluate everything from ticket sales and fundraising to on field performance within a unified framework, we can make smarter decisions across the entire department. That’s where I see Virginia Tech continuing to grow.
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