Sports, Stuyvesant Style: The Analytical Revolution

Advanced analytics have taken the sports world by storm. However, sports analysis and management have not always been so data-driven. For much of sports history, scouts and general managers have relied on a variety of factors to assess players, ranging from basic intuition and (often flawed) observation to the use of elementary statistics. In fact, many teams were often hand-crafted by managers according to players they had a personal relationship with, a practice that continues today, albeit in less extreme fashion. Today, managers across all sports rely heavily on data analysis, a shift resulting from a statistical revolution in the 1980s.

Bill James, an avid baseball fan and aspiring writer, had a dream to see baseball statistics develop further than the basics on the backs of trading cards. He wanted to figure out a measurement for the complete impact that individuals contributed to their team. James introduced statistics such as runs created and win shares to measure offensive contribution, runs saved and fielding efficiency for defensive contribution, and game score for pitching efficiency. In addition, his Pythagorean winning percentage created a model that many teams utilize today to measure overall success probability.

James’s work was widely appreciated but not immediately implemented, especially in larger teams like the New York Yankees and Boston Red Sox, who had the money to consistently attract the most proven players available. It was not until 2002, a year that rings out with a sole meaning to baseball players, managers, and fans alike, that the analytical revolution saw its genesis in the big leagues.

In 2002, the Oakland Athletics sported the third lowest payroll in the MLB, a troubling predicament for a team that placed high expectations on itself. After three star players walked in free agency, manager Billy Beane had no choice but to try his hand at a different method of team construction. Combining the available but often-overlooked statistics of on-base-percentage and slugging percentage, the Athletics created a roster entirely built off mathematical estimates and advanced statistics. Upon finishing first in their division, the Athletics forced the rest of baseball to open their eyes to the benefits that these statistics could bring. The Athletics’ impact was not restricted to the MLB, however. Because of Michael Lewis’s book, “Moneyball: The Art of Winning an Unfair Game,” and the following film of the same title, analytics gained widespread popularity across the sports world, as the impact that statistics could have on an underdog team was seen everywhere.

In the years that followed, baseball saw a growing emphasis on analytics across all aspects of the game. More statistics were developed, many of which are still used today: on-base plus slugging (OPS) percentage and walks plus hits per innings pitched were developed to measure batting and pitching efficiency, respectively, and an early framework for wins above replacement (WAR) was developed. “Statistics like OPS have given contracts to players who would’ve previously been dropped, players who can really get on base consistently. Simply put, more data creates more jobs,” Stuyvesant coach Vincent Miller said, speaking about their impacts. OPS has provided a means to differentiate flashy but inconsistent hitters from skilled hitters who dominate their at-bats, while WAR remains arguably the most crucial and summative statistic in baseball today, providing the only complete, though imperfect, picture of an individual’s contribution.

These statistics also saw their importance to organizations skyrocket, with many establishing analytical departments. Baseball had suddenly turned into a mathematically driven league, and just about every team bought into the system. “Anyone who doesn’t use projections, barrels, and advanced stats is archaic. It’s just statistically worse not to,” Stuyvesant alumnus and former baseball captain Sam Levine said (’22).

Today, baseball continues to reap the benefits of emphasized importance on statistics, and it’s beginning to stretch even outside of the big leagues. The MLB currently boasts a colossal collection of statistics to address a range of different skill sets. Infield shifts, a highly effective defensive strategy, are based on data analysis of different hitters. Pitchers in the MLB have been able to track spin rates, leading to greater mobility in pitches. Hitters have set about perfecting their launch angles to identify which pitches they should be swinging at. This type of analysis has also impacted high school level players like Levine, who has been able to analyze the different outcomes of a variety of swings. “At my best, I’m driving to center field, but when I’m pulling a lot, I tend to ground out,” Levine said. Subtle distinctions like these have allowed for improvements to the offensive games of both Levine and professional MLB players. Baseball’s shift to incorporating these statistics into the elementary understanding of the game has allowed for much more diverse playstyles and far more riveting games.

The impact of the emphasis the MLB placed on math was felt not only in baseball, but also across the sports world, though not as immediately. Fans of other major American sporting leagues, including the NBA, NFL, and NHL, have followed James, adapting statistics like WAR, win share, and runs created into equivalent or applicable forms. However, this initiation of analytics in other sports has led to divisions among fans. Neverending debates about the repercussions of shifting to solely analytical conclusions (which was never proposed by James, Beane, or any other advocates of the statistical revolution) have risen in popularity. While not as palpable or concrete as the factions that many fans have proclaimed themselves to be part of, this division is also evident between organizations. Many teams have emulated the Oakland Athletics, transforming their front office to be analytics-focused. In contrast, other teams have rejected the idea of straying from convention and have hired advisors who publicly refute the benefits of these statistics.

Teams who have placed a significant emphasis on analytics have seen consistent success across a span of leagues, possibly hinting at an MLB-esque future for all leagues. With the sheer amount of accurate, precise statistics available to any organization, it would be surprising to see teams continue to push for the antiquated reliance on fleeting observations, which are saturated with human error. Analytics provide a bias-free assessment of a player’s effectiveness and efficiency in almost every aspect of the game. Rather than buying the false guarantee of past performance, analytics offer a means to building a more successful long term strategy. Wharton statistics professor Abraham Wyner phrases this sentiment perfectly. “You’re paying for the future, not the past,” he said. “The data available today has made it better and easier to forecast the future.”

James revolutionized baseball when he dreamed of a world that incorporated his two fascinations, baseball and mathematics, and the sport is far better off because of it. Analytics have seen baseball move away from its early conventions to a world where consistent hitting is now coveted, and players can identify specific areas of improvement. The rest of the sports world is beginning to follow suit, seeing mathematics grow from a nearly negligible part of the game into a crucial and arguably even central element.