The Moneyball Effect
Analytics vs. Wealth: Can Small-Market Teams Compete Long-Term?
At its core, a sports team operates much like a business. Companies focus on generating profit through sales or services, and sports teams similarly aim to generate revenue by attracting viewers and keeping fans engaged — both rely on consistent consumer interest to remain financially successful. In major cities like Los Angeles, New York, and Chicago, teams benefit from large, dedicated fan bases that provide steady attendance and revenue regardless of performance (Pan, Zhu, Gabert & Brown, 1999). However, on-field success plays a crucial role in financial stability for sports teams in smaller markets, such as Milwaukee, Green Bay, and Oakland. Oakland’s Major League Baseball team, the Athletics (A’s), became emblematic of the small-market struggle through the book Moneyball, which chronicles the A’s innovative strategies managing a relatively small budget. Without consistent performance or innovative strategy, small-market organizations struggle to fill stadiums, attract sponsorships, and maintain profitability.
Historically, financial power has played a dominant role in determining success. From 1995 to 2001, Major League Baseball (MLB) teams with above-average payrolls won 98% of all postseason games (Krautmann, 2009). However, the A’s challenged this notion in the early 2000s by using analytics to identify undervalued players and build a competitive roster on a payroll of just $41 million — less than a third of what teams like the Yankees were spending at the time (Wright, 2018). This contrast highlights the article’s central focus: examining whether data-driven strategies can provide small-market teams with a sustainable competitive edge in a league historically dominated by financial power.
The Moneyball Revolution
In the early 2000s, the A’s embraced a data-driven approach known as “Moneyball” to compete against wealthier franchises. Under the leadership of general manager Billy Beane, the A’s sought to exploit inefficiencies in player evaluation, prioritizing advanced metrics over the traditional eye test, a subjective scouting method that relies on a scout’s intuition, visual impressions, and gut feelings about a player’s potential rather than measurable performance data. Instead of relying on conventional statistics like batting average or subjective observations, Beane and his team focused on on-base percentage (OBP) and slugging percentage (SLG) to assess a player’s true value.
OBP measures how often a player reaches base, including walks and hit-by-pitches — factors often overlooked in traditional scouting. SLG evaluates a player’s power by accounting for total bases per at-bat, offering a more complete picture of offensive production than batting average alone. These metrics allowed the A’s to identify players who contributed significantly to run creation, even if they did not appear impressive in person.
A notable example is David Justice, a veteran player many teams passed over because of his age and declining athleticism — what the eye test would consider a red flag. However, Beane recognized that Justice still maintained a high OBP, indicating his plate discipline and ability to get on base remained valuable. By trusting the data over appearance, the A’s acquired productive players at a fraction of the cost.
Beane’s strategy proved successful. The A’s won 103 out of 162 games in the 2002 season, finishing first in the American League West and securing a playoff spot. The team’s success drew league-wide attention, marking a pivotal moment in MLB’s shift toward analytics-driven player evaluation. Moneyball provided small-market teams with a way to compete in the short term by identifying undervalued talent overlooked by traditional scouting methods (Walton, 2017).
Limitations of Analytics in Small Markets
While the Moneyball strategy delivered impressive short-term results, it also revealed structural challenges small-market teams face. Among these was the inability to retain key players once their performance and market value rose. While analytics can help identify undervalued players, those players often become too expensive for small-market teams to retain once they prove their worth. For example, after the 2001 season, key contributors to the A’s success, such as Jason Giambi and Johnny Damon, left in free agency for larger contracts with wealthier teams (Popdust, 2025). This cycle forces small-market teams into a constant rebuilding phase, making sustained success difficult.
Another limitation is that as more teams adopt analytics, the competitive advantage of the Moneyball approach diminishes. What was once an innovative strategy soon became standard practice across MLB and other professional sports leagues. Big-market teams, with their financial resources, could combine Moneyball analytics with their ability to sign elite talent, making them even more dominant. The Boston Red Sox, for instance, applied Moneyball principles while also leveraging their financial strength, winning multiple championships in the 2000s and 2010s (NBC Sports, 2019).
Additionally, analytics alone cannot account for factors such as team chemistry, leadership, and player development, which remain crucial to building a championship-level team. While data-driven decision-making can optimize roster construction, the human element of the game remains vital. Wealthier franchises have the resources to invest in top-tier scouting, training facilities, and player development programs, giving them an added advantage over small-market teams that rely primarily on analytics (Rose, 2021).
Ultimately, while Moneyball proved that small-market teams can temporarily compete using analytics, financial power remains a dominant factor in long-term success. Without the ability to retain top talent or invest in additional resources, small-market teams face ongoing challenges in maintaining competitiveness.
Can Small-market Teams Remain Financially Competitive?
Despite the limitations of analytics, many sports leagues have attempted to reduce the financial advantage of big-market teams. To address this disparity, leagues often implement revenue-sharing systems and salary caps.
The revenue-sharing system redistributes a portion of the revenue generated by large-market teams to smaller-market franchises in an attempt to promote competitive balance. For instance, in 2023, MLB distributed $60.1 million per team from the national revenue pool, while the National Football League (NFL) allocated $372 million per club in 2022 (Rockerbie, 2024). The English Premier League, in contrast, distributes 50% of national revenues equally, 25% based on league standings, and 25% as a facilities fee for televised matches. In theory, this closes the gap between small and big market teams and allows lower-revenue teams to invest in player development and analytics. This is evident in MLB’s financial data, which shows how revenue sharing impacts the distribution of income across teams (Rockerbie, 2024).
In 2008, when MLB required teams to contribute 31% of their local revenues to the league-wide sharing pool, the standard deviation of local club revenue dropped from $67.1 million to $46.3 million after redistribution. This indicates a reduction in revenue disparity across teams, making the league more financially balanced. In 2023, after the contribution rate increased to 48%, the standard deviation fell from $93.3 million to $65.5 million — further demonstrating how a higher contribution rate narrows the revenue gap between wealthier and less wealthy franchises, ultimately promoting greater competitive equity.
However, critics argue that revenue sharing does not necessarily enhance competitive balance. According to the invariance principle that economist Gerald Scully proposed, revenue sharing may not alter the competitive dynamics of a league because teams will continue to make decisions that maximize their revenues. In other words, even when given additional funds through revenue sharing, teams will still make decisions based on what benefits them financially, not necessarily on what improves their on-field performance (Maxcy, 2007).
Empirical evidence on the effectiveness of revenue sharing is mixed. While the financial gap between teams has decreased, this shift has not consistently resulted in greater competitive balance. Factors such as team management, scouting quality, and player contract decisions continue to play significant roles in determining on-field success. Thus, while revenue sharing may promote financial parity, it does not directly lead to more balanced competition on the field.
In addition to revenue sharing, many professional sports leagues have implemented salary caps as a means to enhance competitive balance. A salary cap places a limit on the total amount a team can spend on player salaries, theoretically preventing wealthier teams from outspending their competition. The NFL, National Basketball Association (NBA), and National Hockey League all have salary caps, but MLB operates with a luxury tax system instead. Under this system, teams are allowed to exceed a designated payroll threshold, but they must pay a tax on the excess amount. The more a team exceeds the threshold, and the more frequently it does so, the higher the penalty. This structure is designed to discourage excessive spending without enforcing a strict cap, offering more financial flexibility while still promoting competitive balance.
However, despite claims from team owners and league officials, research suggests that salary caps have not significantly improved competitive balance. An analysis across major U.S. sports leagues found no statistical evidence that salary caps reduce disparities in team success (Totty & Owens, 2011). In fact, using the standard deviation of winning percentages as a measure of competitive balance, researchers found that salary caps were associated with a statistically significant decrease in competitive balance. This effect was particularly evident in the NBA, where salary cap exemptions, such as the Bird Rights rule, allow teams to exceed the cap to retain star players, thereby limiting player movement and maintaining existing power structures.
These findings align with economic theories such as the Coase Theorem and the Invariance Principle, which suggest that salary caps do not fundamentally disrupt the natural allocation of talent. The Coase Theorem, proposed by economist Ronald Coase, argues that if property rights are well defined and transaction costs are low, resources will be allocated efficiently regardless of who holds the initial rights — in this case, player talent will gravitate toward teams where it is most valued regardless of whether there is a salary cap (Surdam, 2006). Similarly, the Invariance Principle posits that redistributing revenue or imposing salary caps will not alter competitive outcomes if team owners continue to act in profit-maximizing ways (Fort, Maxcy, Diehl, 2016). According to these theories, star players will always gravitate toward teams that generate the most revenue, as these franchises can offer greater financial and marketing opportunities beyond salary, such as endorsements and media exposure. Large-market teams have an inherent advantage in attracting talent due to their greater revenue potential, larger fan bases, and increased media visibility. A salary cap does not address this core disparity; it merely limits the amount teams can pay their players rather than altering the underlying revenue structure that drives competitive imbalance.
Conclusion
The evolution of analytics in sports, epitomized by the A’s Moneyball strategy, demonstrated that small-market teams can compete with wealthier franchises, at least in the short term. By exploiting inefficiencies in player evaluation, these teams can build competitive rosters despite financial constraints. However, the long-term sustainability of such a strategy remains questionable. As analytics became widely adopted across professional sports, big-market teams combine data-driven decision-making with their financial resources, diminishing the competitive advantage of small-market teams.
While revenue sharing and salary caps aim to level the playing field, their effectiveness in promoting true competitive balance is unclear. Revenue sharing reduces financial disparities but does not guarantee investment in team improvement, and salary caps often fail to disrupt the natural tendency of top talent to gravitate toward high-revenue franchises. These structural economic realities suggest that while innovative strategies can provide small-market teams with temporary success, financial power remains the dominant factor in long-term competitiveness. Ultimately, small-market franchises must continuously adapt, leveraging analytics alongside strong player development and efficient resource management to remain competitive.
Edited by Disha Kumar
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