Tennis Analytics Expert Brings Moneyball Approach to Grand Slam Strategy

Wharton podcast explores data-driven decision-making in professional sports, offering lessons for corporate leaders on coachability and continuous improvement

Craig O'Shannessy, a tennis strategist and analyst for multiple Grand Slams, joined Wharton professors Cade Massey, Eric Bradlow, and Shane Jensen on February 11 to discuss how analytics and continuous learning are reshaping success at the highest levels of professional tennis—a conversation that mirrors broader questions about data adoption and organizational change facing corporate finance leaders.

The discussion, part of Wharton's "Moneyball" podcast series, examined how O'Shannessy applies data-driven decision-making to tennis strategy, including underutilized tactics like serve-and-volley play. O'Shannessy, who contributes to The New York Times and provides strategic analysis for Grand Slam tournaments, has become a leading voice in bringing quantitative rigor to a sport traditionally dominated by intuition and conventional wisdom.

For CFOs and finance leaders navigating their own data transformation initiatives, the parallels are instructive. The podcast explored themes of coachability—how top performers incorporate new information and adjust their approach—and the challenge of convincing high-achievers to change tactics that have historically brought success. These same dynamics play out in finance departments where seasoned professionals must adapt to AI-powered forecasting tools and automated decision systems.

The hour-long conversation featured three Wharton faculty members who have built careers studying decision-making and analytics. Massey, Bradlow, and Jensen brought academic rigor to questions about when data should override instinct, how to measure the value of strategic adjustments, and what separates athletes (or executives) who successfully evolve from those who resist change.

O'Shannessy's work analyzing net play—a tactic he argues remains underused despite data showing its effectiveness—offers a case study in how organizations struggle to implement insights even when the numbers are clear. The resistance isn't about data quality; it's about the psychological and cultural barriers to changing ingrained behavior, a challenge familiar to any CFO who has tried to shift a company's budgeting process or capital allocation framework.

The podcast represents Wharton's ongoing effort to extract business lessons from sports analytics, a field that has increasingly influenced corporate decision-making since Michael Lewis's "Moneyball" popularized the approach two decades ago. The school's "Moneyball" series regularly examines how data-driven strategies translate across domains, from professional sports to corporate finance.

What makes O'Shannessy's perspective particularly relevant for finance leaders is his focus on continuous improvement at the elite level. He works with players who have already achieved extraordinary success, yet must constantly refine their approach as competitors adapt and new data emerges. The same pressure faces finance organizations in 2026, where AI tools and real-time data streams are forcing even high-performing teams to reconsider fundamental processes.

The conversation arrives as corporate finance departments face mounting pressure to demonstrate measurable returns from their analytics investments. Like professional tennis players who must justify strategic changes with tournament results, CFOs must show that data initiatives translate into better forecasting accuracy, faster close cycles, or improved capital efficiency.