When we think of sports, we think of the physical abilities that people have in order to perform. We think of athletes such as Michael Jordan, Randy Moss, Derek Jeter, and Usain Bolt who are athletic anomalies. What if I told you that one man changed the way sports would be viewed forever. “Moneyball” was a term coined by Michael Lewis in his 2003 book and then again in 2011 in a feature film. The book and film told the story of the early 2000s Oakland Athletics baseball teams and their pursuit to win. The Athletics, a small market team, could not compete financially with big market teams like the New York Yankees and Boston Red Sox. Using the “old ways of baseball”, the team would draft and develop their young players until their stars would hit free agency and then leave for a team that would pay them more money. The Athletics General Manager, Billy Beane, needed to find a way to compete without spending the same amount of money as these big market teams. He wanted to find a way to change this cycle of drafting players, developing them, and losing them. In order to find a way to compete, Beane adopted a philosophy within the organization now termed “Moneyball”. The idea was to use analytics to find ways to replace wins without spending all the money that it would cost to sign a star player. It had never been done before within Major League Baseball. The analytics that Beane used is what this project will be focused on. I will focus on how the use of mathematics in baseball turned the sport on its head and changed the way the game is played.
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The roots of the Moneyball movement trace back to the development of sabermetrics. The term “sabermetrics” was coined by Bill James in 1980. James was a baseball writer and analyst who believed there was a widespread misunderstanding about how the game of baseball was played, claiming the sport was not defined by its rules but actually, as summarized by engineering professor Richard J. Puerzer, "defined by the conditions under which the game is played – specifically, the ballparks but also the players, the ethics, the strategies, the equipment, and the expectations of the public." Sabermetrics derives its name from SABR, or the Society for American Baseball Research, and refers to the scientific, evidence-based study of baseball. Before sabermetrics, baseball analysis relied on tradition, scouting intuition, and conventional statistics like batting average, runs batted in, and pitcher wins. These statistics were good to look at but often did not tell the whole story of a player’s worth and value. Beginning in the 1970s, Bill James challenged these long-held beliefs by publishing the Baseball Abstract, an annual collection of essays in which he questioned traditional wisdom using data-driven analysis. James would argue that many beliefs in baseball were accepted without evidence and that players were consistently misjudged as a result of poor scouting. His writings would introduce new statistics, like on-base percentage and slugging percentage, aimed at measuring performance more accurately. A key theme in James’s work was the idea that objective data should guide baseball decisions rather than subjective data. He emphasized that the goal of baseball is to score more runs than the other team. Thus, every statistic should be evaluated in terms of its contribution to run production or prevention. This challenged the traditional ideas of baseball. The traditional philosophy of baseball is to focus on flashy, but misleading data such as batting average, home runs, stolen bases, etc. These statistics looked good on a stat sheet, however, did not consistently correlate in team success. Due to James challenging the “status quo” of baseball scouting, he was viewed as an outcast and a mathematician in sports for many years. It wasn’t until the late 1990s that teams started to adopt sabermetrics little by little. By the time Billy Beane was introduced to it, only a handful of teams used it at some level. However, it was not fully adopted by an organization until the Athletics did it in the early 2000s. The A’s found that by utilizing sabermetric reasoning, they could identify undervalued players that excelled in skills like getting on base. Since the players were undervalued by the league, they could sign the players to cheap contracts. This would allow them to be competitive while still having one of the league’s smallest budgets. Today, Bill James is regarded as the father of modern baseball analytics. His revolutionary approach changed the way that the game is played and laid the groundwork for the metrics, models, and decision-making tools that define the Moneyball era. Sabermetrics has evolved from his original works, but his original philosophy has stayed the same: rigorous statistical analysis has the power to uncover insights that reshape how baseball is understood and played.
Modern baseball analytics blends foundational statistics, like batting average, with more advanced mathematical models to evaluate player performance and identify undervalued skills. In the Moneyball era, the pursuit to find player abilities that contributed most directly to run production, or winning, required moving beyond traditional metrics. In this section I will explain how both basic and advanced metrics blended together to play a role in present-day baseball decision making. Fundamental Sabermetric Statistics One of the most important ideas of sabermetric statistics is how frequent does a player get on base. It recognized that a player that got on a base at a high rate was much more valuable than a player with a high batting average. This was because a player with a high batting average would demand a lot of money, but a player with a high on-base percentage could fly under the radar. Thus, the on-base percentage metric, or OBP was born. This was calculated by adding a player’s hits, walks, and hit by pitches up and then dividing that total by the sum of at bats, walks, hit by pitches, and sacrifice flies. OBP=(H+W+HBP)/(AB+W+HBP+SF) Another important metric is slugging percentage. This accounts for a batter’s power by assigning different weights to singles, doubles, triples, and home runs. Many teams value the home run much more than the double, so a player with a high home run total would demand more money. However, using slugging percentage, teams could find how a batter’s power really looked. This calculation looks like: SLG = (Singles + (2 x Doubles) + (3 x Triples) + (4 x Home Runs)) / At Bats. To get greater analytic knowledge on players, sabermetrics developed On-Base Plus Slugging, or OPS. This took a player’s on-base percentage and combined it with their slugging percentage to give teams a look into how a batter is as a whole. This is found as such: OPS = (On-base Percentage + Slugging Percentage). In looking at pitchers, sabermetrics dismissed the importance of ERA, or Earned runs allowed, which is calculated by dividing a pitcher’s earned runs allowed by the number of innings pitched and then multiplying that by 9. Instead, moving to a metric named WHIP, or walks and hits per inning pitched. This is found by adding the total number of walks and hits and then dividing by the number of innings pitched.
OBP = (H + BB + HBP) / (AB + BB + HBP + SF)
SLG = Total Bases / AB
OPS = OBP + SLG
WHIP = (Walks + Hits) / Innings Pitched
The impact of modern baseball analytics extends farther than the Oakland A’s experiments. The integration of mathematical reasoning into baseball has reshaped roster construction, in-game strategy, player development, and even the economics of the sport. One of the most significant applications of the Moneyball principles is how teams evaluate and acquire players. As organizations shifted away from traditional metrics and dove deeper into sabermetrics, front offices began identifying players whose skills were undervalued in the marketplace. It allowed teams to acquire winning players at a lower cost. This would be significant development for small market teams to stay competitive with the big market teams. However, as this philosophy was adopted widespread, it forced teams to find other cutting-edge metrics that could evaluate players. Analytics has also transformed the way games are managed. Metrics like run expectancy and win probability models help managers determine optimal choices in situations such as when to steal a base, when to bunt, or when to switch pitchers. Included in these models are data-driven defensive alignments, also known as a defensive shift. One could argue that in the present-day game of baseball, this is where analytics rears its head. Philosophies on how many pitches a pitcher should throw, what different pitches are thrown from a team’s bullpen, how fast a batter swings the bat are all examples of how analytics are used in the game today.
The philosophy of Moneyball is the idea that using analytics can give a team an advantage over a team that doesn’t use analytics. The idea that objective analysis will always beat subjective analysis. Is this true? There are arguments for both sides. The truth is that baseball, and all sports, are unfair and that the bigger, faster, and stronger athletes are usually favored. However, Moneyball proved that analytics can close that gap. Where teams become special is when they can mesh analytics with superior athletes to create a great overall team.