Tennis Analytics Expert Brings Moneyball Approach to Grand Slam Strategy

A leading tennis strategist who has worked with multiple Grand Slam tournaments is applying data-driven decision-making principles to reshape how the sport approaches tactics at its highest levels, offering lessons in coachability and performance optimization that extend beyond the court.

Craig O'Shannessy, who serves as an analyst for several Grand Slam events and contributes to The New York Times, appeared on Wharton's "Moneyball" podcast on February 11, 2026, to discuss how analytics are transforming tennis strategy. The conversation, hosted by Wharton professors Cade Massey, Eric Bradlow, and Shane Jensen, explored how underutilized tactics like serve-and-volley play and continuous learning are changing success patterns in professional tennis.

The discussion marks the latest intersection of sports analytics and business strategy, a connection that has gained traction since Michael Lewis's "Moneyball" chronicled how the Oakland Athletics used data to compete against wealthier baseball franchises. For finance leaders navigating their own data transformation initiatives, the parallels are instructive: both domains involve using quantitative analysis to challenge conventional wisdom and identify inefficiencies that competitors overlook.

O'Shannessy's work focuses on identifying strategic patterns that top players either underuse or misapply despite available data. The serve-and-volley approach, once dominant in professional tennis but largely abandoned in recent decades, represents one such tactical opportunity that analytics suggest deserves reconsideration in specific match situations.

The broader theme of "coachability"—a player's willingness to adapt strategy based on data rather than intuition—emerged as a critical factor in performance improvement. This mirrors challenges facing corporate finance teams, where resistance to data-driven recommendations often stems from deeply ingrained practices rather than rational analysis of outcomes.

Wharton's "Moneyball" series, part of the business school's ongoing examination of analytics applications across industries, has previously explored data-driven decision-making in various contexts. The February 11 episode, running just over an hour, represents the program's effort to extract business lessons from sports analytics success stories.

The timing is notable as organizations across sectors grapple with how to implement AI and machine learning tools effectively. The tennis case study offers a relatively contained environment where cause and effect are measurable, providing clearer insights into what separates successful analytics adoption from failed initiatives.

For CFOs and finance leaders, the key takeaway centers on organizational learning: having data available matters less than creating cultures where decision-makers actively seek out and act on analytical insights, even when they contradict established practice. O'Shannessy's work with Grand Slam events demonstrates how even tradition-bound, high-stakes environments can evolve when presented with compelling evidence.

The podcast is available through Wharton's Knowledge platform, which regularly publishes research and analysis on business strategy, leadership, and emerging management practices.